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**Expected Output**: **out** [ 0.94822985 0. 1.16101444 2.747859 0. 1.36677003] 2.2 - The convolutional blockThe ResNet "convolutional block" is the second block type. You can use this type of block when the input and output dimensions don't match up. The difference with the identity block is that there is a CONV2D layer in the shortcut path: **Figure 4** : **Convolutional block** * The CONV2D layer in the shortcut path is used to resize the input $x$ to a different dimension, so that the dimensions match up in the final addition needed to add the shortcut value back to the main path. (This plays a similar role as the matrix $W_s$ discussed in lecture.) * For example, to reduce the activation dimensions's height and width by a factor of 2, you can use a 1x1 convolution with a stride of 2. * The CONV2D layer on the shortcut path does not use any non-linear activation function. Its main role is to just apply a (learned) linear function that reduces the dimension of the input, so that the dimensions match up for the later addition step. The details of the convolutional block are as follows. First component of main path:- The first CONV2D has $F_1$ filters of shape (1,1) and a stride of (s,s). Its padding is "valid" and its name should be `conv_name_base + '2a'`. Use 0 as the `glorot_uniform` seed.- The first BatchNorm is normalizing the 'channels' axis. Its name should be `bn_name_base + '2a'`.- Then apply the ReLU activation function. This has no name and no hyperparameters. Second component of main path:- The second CONV2D has $F_2$ filters of shape (f,f) and a stride of (1,1). Its padding is "same" and it's name should be `conv_name_base + '2b'`. Use 0 as the `glorot_uniform` seed.- The second BatchNorm is normalizing the 'channels' axis. Its name should be `bn_name_base + '2b'`.- Then apply the ReLU activation function. This has no name and no hyperparameters. Third component of main path:- The third CONV2D has $F_3$ filters of shape (1,1) and a stride of (1,1). Its padding is "valid" and it's name should be `conv_name_base + '2c'`. Use 0 as the `glorot_uniform` seed.- The third BatchNorm is normalizing the 'channels' axis. Its name should be `bn_name_base + '2c'`. Note that there is no ReLU activation function in this component. Shortcut path:- The CONV2D has $F_3$ filters of shape (1,1) and a stride of (s,s). Its padding is "valid" and its name should be `conv_name_base + '1'`. Use 0 as the `glorot_uniform` seed.- The BatchNorm is normalizing the 'channels' axis. Its name should be `bn_name_base + '1'`. Final step: - The shortcut and the main path values are added together.- Then apply the ReLU activation function. This has no name and no hyperparameters. **Exercise**: Implement the convolutional block. We have implemented the first component of the main path; you should implement the rest. As before, always use 0 as the seed for the random initialization, to ensure consistency with our grader.- [Conv2D](https://keras.io/layers/convolutional/conv2d)- [BatchNormalization](https://keras.io/layers/normalization/batchnormalization) (axis: Integer, the axis that should be normalized (typically the features axis))- For the activation, use: `Activation('relu')(X)`- [Add](https://keras.io/layers/merge/add) | def convolutional_block(X, f, filters, stage, block, s = 2):
"""
Implementation of the convolutional block as defined in Figure 4
Arguments:
X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
f -- integer, specifying the shape of the middle CONV's window for the main path
filters -- python list of integers, defining the number of filters in the CONV layers of the main path
stage -- integer, used to name the layers, depending on their position in the network
block -- string/character, used to name the layers, depending on their position in the network
s -- Integer, specifying the stride to be used
Returns:
X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C)
"""
# defining name basis
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
# Retrieve Filters
F1, F2, F3 = filters
# Save the input value
X_shortcut = X
##### MAIN PATH #####
# First component of main path
X = Conv2D(filters =F1, kernel_size =(1, 1), strides = (s,s), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)
X = Activation('relu')(X)
### START CODE HERE ###
# Second component of main path (≈3 lines)
X = Conv2D(filters =F2, kernel_size =(f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)
X = Activation('relu')(X)
# Third component of main path (≈2 lines)
X = Conv2D(filters =F3, kernel_size =(1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)
##### SHORTCUT PATH #### (≈2 lines)
X_shortcut = Conv2D(filters =F3, kernel_size =(1, 1), strides = (s,s), padding = 'valid', name = conv_name_base + '1', kernel_initializer = glorot_uniform(seed=0))(X_shortcut)
X_shortcut = BatchNormalization(axis = 3, name = bn_name_base + '1')(X_shortcut)
# Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
X = Add()([X, X_shortcut])
X = Activation('relu')(X)
### END CODE HERE ###
return X
tf.reset_default_graph()
with tf.Session() as test:
np.random.seed(1)
A_prev = tf.placeholder("float", [3, 4, 4, 6])
X = np.random.randn(3, 4, 4, 6)
A = convolutional_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')
test.run(tf.global_variables_initializer())
out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})
print("out = " + str(out[0][1][1][0])) | out = [ 0.09018463 1.23489773 0.46822017 0.0367176 0. 0.65516603]
| MIT | Convolutional Neural Networks/Residual_Networks_v2a.ipynb | joyfinder/Deep_Learning_Specialisation |
**Expected Output**: **out** [ 0.09018463 1.23489773 0.46822017 0.0367176 0. 0.65516603] 3 - Building your first ResNet model (50 layers)You now have the necessary blocks to build a very deep ResNet. The following figure describes in detail the architecture of this neural network. "ID BLOCK" in the diagram stands for "Identity block," and "ID BLOCK x3" means you should stack 3 identity blocks together. **Figure 5** : **ResNet-50 model** The details of this ResNet-50 model are:- Zero-padding pads the input with a pad of (3,3)- Stage 1: - The 2D Convolution has 64 filters of shape (7,7) and uses a stride of (2,2). Its name is "conv1". - BatchNorm is applied to the 'channels' axis of the input. - MaxPooling uses a (3,3) window and a (2,2) stride.- Stage 2: - The convolutional block uses three sets of filters of size [64,64,256], "f" is 3, "s" is 1 and the block is "a". - The 2 identity blocks use three sets of filters of size [64,64,256], "f" is 3 and the blocks are "b" and "c".- Stage 3: - The convolutional block uses three sets of filters of size [128,128,512], "f" is 3, "s" is 2 and the block is "a". - The 3 identity blocks use three sets of filters of size [128,128,512], "f" is 3 and the blocks are "b", "c" and "d".- Stage 4: - The convolutional block uses three sets of filters of size [256, 256, 1024], "f" is 3, "s" is 2 and the block is "a". - The 5 identity blocks use three sets of filters of size [256, 256, 1024], "f" is 3 and the blocks are "b", "c", "d", "e" and "f".- Stage 5: - The convolutional block uses three sets of filters of size [512, 512, 2048], "f" is 3, "s" is 2 and the block is "a". - The 2 identity blocks use three sets of filters of size [512, 512, 2048], "f" is 3 and the blocks are "b" and "c".- The 2D Average Pooling uses a window of shape (2,2) and its name is "avg_pool".- The 'flatten' layer doesn't have any hyperparameters or name.- The Fully Connected (Dense) layer reduces its input to the number of classes using a softmax activation. Its name should be `'fc' + str(classes)`.**Exercise**: Implement the ResNet with 50 layers described in the figure above. We have implemented Stages 1 and 2. Please implement the rest. (The syntax for implementing Stages 3-5 should be quite similar to that of Stage 2.) Make sure you follow the naming convention in the text above. You'll need to use this function: - Average pooling [see reference](https://keras.io/layers/pooling/averagepooling2d)Here are some other functions we used in the code below:- Conv2D: [See reference](https://keras.io/layers/convolutional/conv2d)- BatchNorm: [See reference](https://keras.io/layers/normalization/batchnormalization) (axis: Integer, the axis that should be normalized (typically the features axis))- Zero padding: [See reference](https://keras.io/layers/convolutional/zeropadding2d)- Max pooling: [See reference](https://keras.io/layers/pooling/maxpooling2d)- Fully connected layer: [See reference](https://keras.io/layers/core/dense)- Addition: [See reference](https://keras.io/layers/merge/add) | # GRADED FUNCTION: ResNet50
def ResNet50(input_shape = (64, 64, 3), classes = 6):
"""
Implementation of the popular ResNet50 the following architecture:
CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3
-> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER
Arguments:
input_shape -- shape of the images of the dataset
classes -- integer, number of classes
Returns:
model -- a Model() instance in Keras
"""
# Define the input as a tensor with shape input_shape
X_input = Input(input_shape)
# Zero-Padding
X = ZeroPadding2D((3, 3))(X_input)
# Stage 1
X = Conv2D(64, (7, 7), strides = (2, 2), name = 'conv1', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = 'bn_conv1')(X)
X = Activation('relu')(X)
X = MaxPooling2D((3, 3), strides=(2, 2))(X)
# Stage 2
X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, block='a', s = 1)
X = identity_block(X, 3, [64, 64, 256], stage=2, block='b')
X = identity_block(X, 3, [64, 64, 256], stage=2, block='c')
### START CODE HERE ###
# Stage 3 (≈4 lines)
X = convolutional_block(X, f = 3, filters = [128, 128, 512], stage = 3, block='a', s = 2)
X = identity_block(X, 3, [128, 128, 512], stage=3, block='b')
X = identity_block(X, 3, [128, 128, 512], stage=3, block='c')
X = identity_block(X, 3, [128, 128, 512], stage=3, block='d')
# Stage 4 (≈6 lines)
X = convolutional_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block='a', s = 2)
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='b')
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c')
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d')
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e')
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='f')
# Stage 5 (≈3 lines)
X = convolutional_block(X, f = 3, filters = [512, 512, 2048], stage = 5, block='a', s = 2)
X = identity_block(X, 3, [512, 512, 2048], stage=5, block='b')
X = identity_block(X, 3, [512, 512, 2048], stage=5, block='c')
# AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)"
X = AveragePooling2D(pool_size=(2, 2), name = 'avg_pool')(X)
### END CODE HERE ###
# output layer
X = Flatten()(X)
X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)
# Create model
model = Model(inputs = X_input, outputs = X, name='ResNet50')
return model | _____no_output_____ | MIT | Convolutional Neural Networks/Residual_Networks_v2a.ipynb | joyfinder/Deep_Learning_Specialisation |
Run the following code to build the model's graph. If your implementation is not correct you will know it by checking your accuracy when running `model.fit(...)` below. | model = ResNet50(input_shape = (64, 64, 3), classes = 6) | _____no_output_____ | MIT | Convolutional Neural Networks/Residual_Networks_v2a.ipynb | joyfinder/Deep_Learning_Specialisation |
As seen in the Keras Tutorial Notebook, prior training a model, you need to configure the learning process by compiling the model. | model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) | _____no_output_____ | MIT | Convolutional Neural Networks/Residual_Networks_v2a.ipynb | joyfinder/Deep_Learning_Specialisation |
The model is now ready to be trained. The only thing you need is a dataset. Let's load the SIGNS Dataset. **Figure 6** : **SIGNS dataset** | X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
# Normalize image vectors
X_train = X_train_orig/255.
X_test = X_test_orig/255.
# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T
print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape)) | number of training examples = 1080
number of test examples = 120
X_train shape: (1080, 64, 64, 3)
Y_train shape: (1080, 6)
X_test shape: (120, 64, 64, 3)
Y_test shape: (120, 6)
| MIT | Convolutional Neural Networks/Residual_Networks_v2a.ipynb | joyfinder/Deep_Learning_Specialisation |
Run the following cell to train your model on 2 epochs with a batch size of 32. On a CPU it should take you around 5min per epoch. | model.fit(X_train, Y_train, epochs = 2, batch_size = 32) | Epoch 1/2
1080/1080 [==============================] - 265s - loss: 2.5033 - acc: 0.3380
Epoch 2/2
1080/1080 [==============================] - 259s - loss: 1.3300 - acc: 0.6204
| MIT | Convolutional Neural Networks/Residual_Networks_v2a.ipynb | joyfinder/Deep_Learning_Specialisation |
**Expected Output**: ** Epoch 1/2** loss: between 1 and 5, acc: between 0.2 and 0.5, although your results can be different from ours. ** Epoch 2/2** loss: between 1 and 5, acc: between 0.2 and 0.5, you should see your loss decreasing and the accuracy increasing. Let's see how this model (trained on only two epochs) performs on the test set. | preds = model.evaluate(X_test, Y_test)
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1])) | 120/120 [==============================] - 9s
Loss = 13.2004664103
Test Accuracy = 0.166666667163
| MIT | Convolutional Neural Networks/Residual_Networks_v2a.ipynb | joyfinder/Deep_Learning_Specialisation |
**Expected Output**: **Test Accuracy** between 0.16 and 0.25 For the purpose of this assignment, we've asked you to train the model for just two epochs. You can see that it achieves poor performances. Please go ahead and submit your assignment; to check correctness, the online grader will run your code only for a small number of epochs as well. After you have finished this official (graded) part of this assignment, you can also optionally train the ResNet for more iterations, if you want. We get a lot better performance when we train for ~20 epochs, but this will take more than an hour when training on a CPU. Using a GPU, we've trained our own ResNet50 model's weights on the SIGNS dataset. You can load and run our trained model on the test set in the cells below. It may take ≈1min to load the model. | model = load_model('ResNet50.h5')
preds = model.evaluate(X_test, Y_test)
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1])) | 120/120 [==============================] - 9s
Loss = 0.530178320408
Test Accuracy = 0.866666662693
| MIT | Convolutional Neural Networks/Residual_Networks_v2a.ipynb | joyfinder/Deep_Learning_Specialisation |
ResNet50 is a powerful model for image classification when it is trained for an adequate number of iterations. We hope you can use what you've learnt and apply it to your own classification problem to perform state-of-the-art accuracy.Congratulations on finishing this assignment! You've now implemented a state-of-the-art image classification system! 4 - Test on your own image (Optional/Ungraded) If you wish, you can also take a picture of your own hand and see the output of the model. To do this: 1. Click on "File" in the upper bar of this notebook, then click "Open" to go on your Coursera Hub. 2. Add your image to this Jupyter Notebook's directory, in the "images" folder 3. Write your image's name in the following code 4. Run the code and check if the algorithm is right! | img_path = 'images/my_image.jpg'
img = image.load_img(img_path, target_size=(64, 64))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = x/255.0
print('Input image shape:', x.shape)
my_image = scipy.misc.imread(img_path)
imshow(my_image)
print("class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = ")
print(model.predict(x)) | Input image shape: (1, 64, 64, 3)
class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] =
[[ 3.41876671e-06 2.77412561e-04 9.99522924e-01 1.98842812e-07
1.95619068e-04 4.11686671e-07]]
| MIT | Convolutional Neural Networks/Residual_Networks_v2a.ipynb | joyfinder/Deep_Learning_Specialisation |
You can also print a summary of your model by running the following code. | model.summary() | ____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 64, 64, 3) 0
____________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D) (None, 70, 70, 3) 0 input_1[0][0]
____________________________________________________________________________________________________
conv1 (Conv2D) (None, 32, 32, 64) 9472 zero_padding2d_1[0][0]
____________________________________________________________________________________________________
bn_conv1 (BatchNormalization) (None, 32, 32, 64) 256 conv1[0][0]
____________________________________________________________________________________________________
activation_4 (Activation) (None, 32, 32, 64) 0 bn_conv1[0][0]
____________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 15, 15, 64) 0 activation_4[0][0]
____________________________________________________________________________________________________
res2a_branch2a (Conv2D) (None, 15, 15, 64) 4160 max_pooling2d_1[0][0]
____________________________________________________________________________________________________
bn2a_branch2a (BatchNormalizatio (None, 15, 15, 64) 256 res2a_branch2a[0][0]
____________________________________________________________________________________________________
activation_5 (Activation) (None, 15, 15, 64) 0 bn2a_branch2a[0][0]
____________________________________________________________________________________________________
res2a_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_5[0][0]
____________________________________________________________________________________________________
bn2a_branch2b (BatchNormalizatio (None, 15, 15, 64) 256 res2a_branch2b[0][0]
____________________________________________________________________________________________________
activation_6 (Activation) (None, 15, 15, 64) 0 bn2a_branch2b[0][0]
____________________________________________________________________________________________________
res2a_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_6[0][0]
____________________________________________________________________________________________________
res2a_branch1 (Conv2D) (None, 15, 15, 256) 16640 max_pooling2d_1[0][0]
____________________________________________________________________________________________________
bn2a_branch2c (BatchNormalizatio (None, 15, 15, 256) 1024 res2a_branch2c[0][0]
____________________________________________________________________________________________________
bn2a_branch1 (BatchNormalization (None, 15, 15, 256) 1024 res2a_branch1[0][0]
____________________________________________________________________________________________________
add_2 (Add) (None, 15, 15, 256) 0 bn2a_branch2c[0][0]
bn2a_branch1[0][0]
____________________________________________________________________________________________________
activation_7 (Activation) (None, 15, 15, 256) 0 add_2[0][0]
____________________________________________________________________________________________________
res2b_branch2a (Conv2D) (None, 15, 15, 64) 16448 activation_7[0][0]
____________________________________________________________________________________________________
bn2b_branch2a (BatchNormalizatio (None, 15, 15, 64) 256 res2b_branch2a[0][0]
____________________________________________________________________________________________________
activation_8 (Activation) (None, 15, 15, 64) 0 bn2b_branch2a[0][0]
____________________________________________________________________________________________________
res2b_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_8[0][0]
____________________________________________________________________________________________________
bn2b_branch2b (BatchNormalizatio (None, 15, 15, 64) 256 res2b_branch2b[0][0]
____________________________________________________________________________________________________
activation_9 (Activation) (None, 15, 15, 64) 0 bn2b_branch2b[0][0]
____________________________________________________________________________________________________
res2b_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_9[0][0]
____________________________________________________________________________________________________
bn2b_branch2c (BatchNormalizatio (None, 15, 15, 256) 1024 res2b_branch2c[0][0]
____________________________________________________________________________________________________
add_3 (Add) (None, 15, 15, 256) 0 bn2b_branch2c[0][0]
activation_7[0][0]
____________________________________________________________________________________________________
activation_10 (Activation) (None, 15, 15, 256) 0 add_3[0][0]
____________________________________________________________________________________________________
res2c_branch2a (Conv2D) (None, 15, 15, 64) 16448 activation_10[0][0]
____________________________________________________________________________________________________
bn2c_branch2a (BatchNormalizatio (None, 15, 15, 64) 256 res2c_branch2a[0][0]
____________________________________________________________________________________________________
activation_11 (Activation) (None, 15, 15, 64) 0 bn2c_branch2a[0][0]
____________________________________________________________________________________________________
res2c_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_11[0][0]
____________________________________________________________________________________________________
bn2c_branch2b (BatchNormalizatio (None, 15, 15, 64) 256 res2c_branch2b[0][0]
____________________________________________________________________________________________________
activation_12 (Activation) (None, 15, 15, 64) 0 bn2c_branch2b[0][0]
____________________________________________________________________________________________________
res2c_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_12[0][0]
____________________________________________________________________________________________________
bn2c_branch2c (BatchNormalizatio (None, 15, 15, 256) 1024 res2c_branch2c[0][0]
____________________________________________________________________________________________________
add_4 (Add) (None, 15, 15, 256) 0 bn2c_branch2c[0][0]
activation_10[0][0]
____________________________________________________________________________________________________
activation_13 (Activation) (None, 15, 15, 256) 0 add_4[0][0]
____________________________________________________________________________________________________
res3a_branch2a (Conv2D) (None, 8, 8, 128) 32896 activation_13[0][0]
____________________________________________________________________________________________________
bn3a_branch2a (BatchNormalizatio (None, 8, 8, 128) 512 res3a_branch2a[0][0]
____________________________________________________________________________________________________
activation_14 (Activation) (None, 8, 8, 128) 0 bn3a_branch2a[0][0]
____________________________________________________________________________________________________
res3a_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_14[0][0]
____________________________________________________________________________________________________
bn3a_branch2b (BatchNormalizatio (None, 8, 8, 128) 512 res3a_branch2b[0][0]
____________________________________________________________________________________________________
activation_15 (Activation) (None, 8, 8, 128) 0 bn3a_branch2b[0][0]
____________________________________________________________________________________________________
res3a_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_15[0][0]
____________________________________________________________________________________________________
res3a_branch1 (Conv2D) (None, 8, 8, 512) 131584 activation_13[0][0]
____________________________________________________________________________________________________
bn3a_branch2c (BatchNormalizatio (None, 8, 8, 512) 2048 res3a_branch2c[0][0]
____________________________________________________________________________________________________
bn3a_branch1 (BatchNormalization (None, 8, 8, 512) 2048 res3a_branch1[0][0]
____________________________________________________________________________________________________
add_5 (Add) (None, 8, 8, 512) 0 bn3a_branch2c[0][0]
bn3a_branch1[0][0]
____________________________________________________________________________________________________
activation_16 (Activation) (None, 8, 8, 512) 0 add_5[0][0]
____________________________________________________________________________________________________
res3b_branch2a (Conv2D) (None, 8, 8, 128) 65664 activation_16[0][0]
____________________________________________________________________________________________________
bn3b_branch2a (BatchNormalizatio (None, 8, 8, 128) 512 res3b_branch2a[0][0]
____________________________________________________________________________________________________
activation_17 (Activation) (None, 8, 8, 128) 0 bn3b_branch2a[0][0]
____________________________________________________________________________________________________
res3b_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_17[0][0]
____________________________________________________________________________________________________
bn3b_branch2b (BatchNormalizatio (None, 8, 8, 128) 512 res3b_branch2b[0][0]
____________________________________________________________________________________________________
activation_18 (Activation) (None, 8, 8, 128) 0 bn3b_branch2b[0][0]
____________________________________________________________________________________________________
res3b_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_18[0][0]
____________________________________________________________________________________________________
bn3b_branch2c (BatchNormalizatio (None, 8, 8, 512) 2048 res3b_branch2c[0][0]
____________________________________________________________________________________________________
add_6 (Add) (None, 8, 8, 512) 0 bn3b_branch2c[0][0]
activation_16[0][0]
____________________________________________________________________________________________________
activation_19 (Activation) (None, 8, 8, 512) 0 add_6[0][0]
____________________________________________________________________________________________________
res3c_branch2a (Conv2D) (None, 8, 8, 128) 65664 activation_19[0][0]
____________________________________________________________________________________________________
bn3c_branch2a (BatchNormalizatio (None, 8, 8, 128) 512 res3c_branch2a[0][0]
____________________________________________________________________________________________________
activation_20 (Activation) (None, 8, 8, 128) 0 bn3c_branch2a[0][0]
____________________________________________________________________________________________________
res3c_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_20[0][0]
____________________________________________________________________________________________________
bn3c_branch2b (BatchNormalizatio (None, 8, 8, 128) 512 res3c_branch2b[0][0]
____________________________________________________________________________________________________
activation_21 (Activation) (None, 8, 8, 128) 0 bn3c_branch2b[0][0]
____________________________________________________________________________________________________
res3c_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_21[0][0]
____________________________________________________________________________________________________
bn3c_branch2c (BatchNormalizatio (None, 8, 8, 512) 2048 res3c_branch2c[0][0]
____________________________________________________________________________________________________
add_7 (Add) (None, 8, 8, 512) 0 bn3c_branch2c[0][0]
activation_19[0][0]
____________________________________________________________________________________________________
activation_22 (Activation) (None, 8, 8, 512) 0 add_7[0][0]
____________________________________________________________________________________________________
res3d_branch2a (Conv2D) (None, 8, 8, 128) 65664 activation_22[0][0]
____________________________________________________________________________________________________
bn3d_branch2a (BatchNormalizatio (None, 8, 8, 128) 512 res3d_branch2a[0][0]
____________________________________________________________________________________________________
activation_23 (Activation) (None, 8, 8, 128) 0 bn3d_branch2a[0][0]
____________________________________________________________________________________________________
res3d_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_23[0][0]
____________________________________________________________________________________________________
bn3d_branch2b (BatchNormalizatio (None, 8, 8, 128) 512 res3d_branch2b[0][0]
____________________________________________________________________________________________________
activation_24 (Activation) (None, 8, 8, 128) 0 bn3d_branch2b[0][0]
____________________________________________________________________________________________________
res3d_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_24[0][0]
____________________________________________________________________________________________________
bn3d_branch2c (BatchNormalizatio (None, 8, 8, 512) 2048 res3d_branch2c[0][0]
____________________________________________________________________________________________________
add_8 (Add) (None, 8, 8, 512) 0 bn3d_branch2c[0][0]
activation_22[0][0]
____________________________________________________________________________________________________
activation_25 (Activation) (None, 8, 8, 512) 0 add_8[0][0]
____________________________________________________________________________________________________
res4a_branch2a (Conv2D) (None, 4, 4, 256) 131328 activation_25[0][0]
____________________________________________________________________________________________________
bn4a_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4a_branch2a[0][0]
____________________________________________________________________________________________________
activation_26 (Activation) (None, 4, 4, 256) 0 bn4a_branch2a[0][0]
____________________________________________________________________________________________________
res4a_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_26[0][0]
____________________________________________________________________________________________________
bn4a_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4a_branch2b[0][0]
____________________________________________________________________________________________________
activation_27 (Activation) (None, 4, 4, 256) 0 bn4a_branch2b[0][0]
____________________________________________________________________________________________________
res4a_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_27[0][0]
____________________________________________________________________________________________________
res4a_branch1 (Conv2D) (None, 4, 4, 1024) 525312 activation_25[0][0]
____________________________________________________________________________________________________
bn4a_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4a_branch2c[0][0]
____________________________________________________________________________________________________
bn4a_branch1 (BatchNormalization (None, 4, 4, 1024) 4096 res4a_branch1[0][0]
____________________________________________________________________________________________________
add_9 (Add) (None, 4, 4, 1024) 0 bn4a_branch2c[0][0]
bn4a_branch1[0][0]
____________________________________________________________________________________________________
activation_28 (Activation) (None, 4, 4, 1024) 0 add_9[0][0]
____________________________________________________________________________________________________
res4b_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_28[0][0]
____________________________________________________________________________________________________
bn4b_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4b_branch2a[0][0]
____________________________________________________________________________________________________
activation_29 (Activation) (None, 4, 4, 256) 0 bn4b_branch2a[0][0]
____________________________________________________________________________________________________
res4b_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_29[0][0]
____________________________________________________________________________________________________
bn4b_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4b_branch2b[0][0]
____________________________________________________________________________________________________
activation_30 (Activation) (None, 4, 4, 256) 0 bn4b_branch2b[0][0]
____________________________________________________________________________________________________
res4b_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_30[0][0]
____________________________________________________________________________________________________
bn4b_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4b_branch2c[0][0]
____________________________________________________________________________________________________
add_10 (Add) (None, 4, 4, 1024) 0 bn4b_branch2c[0][0]
activation_28[0][0]
____________________________________________________________________________________________________
activation_31 (Activation) (None, 4, 4, 1024) 0 add_10[0][0]
____________________________________________________________________________________________________
res4c_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_31[0][0]
____________________________________________________________________________________________________
bn4c_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4c_branch2a[0][0]
____________________________________________________________________________________________________
activation_32 (Activation) (None, 4, 4, 256) 0 bn4c_branch2a[0][0]
____________________________________________________________________________________________________
res4c_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_32[0][0]
____________________________________________________________________________________________________
bn4c_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4c_branch2b[0][0]
____________________________________________________________________________________________________
activation_33 (Activation) (None, 4, 4, 256) 0 bn4c_branch2b[0][0]
____________________________________________________________________________________________________
res4c_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_33[0][0]
____________________________________________________________________________________________________
bn4c_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4c_branch2c[0][0]
____________________________________________________________________________________________________
add_11 (Add) (None, 4, 4, 1024) 0 bn4c_branch2c[0][0]
activation_31[0][0]
____________________________________________________________________________________________________
activation_34 (Activation) (None, 4, 4, 1024) 0 add_11[0][0]
____________________________________________________________________________________________________
res4d_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_34[0][0]
____________________________________________________________________________________________________
bn4d_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4d_branch2a[0][0]
____________________________________________________________________________________________________
activation_35 (Activation) (None, 4, 4, 256) 0 bn4d_branch2a[0][0]
____________________________________________________________________________________________________
res4d_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_35[0][0]
____________________________________________________________________________________________________
bn4d_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4d_branch2b[0][0]
____________________________________________________________________________________________________
activation_36 (Activation) (None, 4, 4, 256) 0 bn4d_branch2b[0][0]
____________________________________________________________________________________________________
res4d_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_36[0][0]
____________________________________________________________________________________________________
bn4d_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4d_branch2c[0][0]
____________________________________________________________________________________________________
add_12 (Add) (None, 4, 4, 1024) 0 bn4d_branch2c[0][0]
activation_34[0][0]
____________________________________________________________________________________________________
activation_37 (Activation) (None, 4, 4, 1024) 0 add_12[0][0]
____________________________________________________________________________________________________
res4e_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_37[0][0]
____________________________________________________________________________________________________
bn4e_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4e_branch2a[0][0]
____________________________________________________________________________________________________
activation_38 (Activation) (None, 4, 4, 256) 0 bn4e_branch2a[0][0]
____________________________________________________________________________________________________
res4e_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_38[0][0]
____________________________________________________________________________________________________
bn4e_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4e_branch2b[0][0]
____________________________________________________________________________________________________
activation_39 (Activation) (None, 4, 4, 256) 0 bn4e_branch2b[0][0]
____________________________________________________________________________________________________
res4e_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_39[0][0]
____________________________________________________________________________________________________
bn4e_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4e_branch2c[0][0]
____________________________________________________________________________________________________
add_13 (Add) (None, 4, 4, 1024) 0 bn4e_branch2c[0][0]
activation_37[0][0]
____________________________________________________________________________________________________
activation_40 (Activation) (None, 4, 4, 1024) 0 add_13[0][0]
____________________________________________________________________________________________________
res4f_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_40[0][0]
____________________________________________________________________________________________________
bn4f_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4f_branch2a[0][0]
____________________________________________________________________________________________________
activation_41 (Activation) (None, 4, 4, 256) 0 bn4f_branch2a[0][0]
____________________________________________________________________________________________________
res4f_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_41[0][0]
____________________________________________________________________________________________________
bn4f_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4f_branch2b[0][0]
____________________________________________________________________________________________________
activation_42 (Activation) (None, 4, 4, 256) 0 bn4f_branch2b[0][0]
____________________________________________________________________________________________________
res4f_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_42[0][0]
____________________________________________________________________________________________________
bn4f_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4f_branch2c[0][0]
____________________________________________________________________________________________________
add_14 (Add) (None, 4, 4, 1024) 0 bn4f_branch2c[0][0]
activation_40[0][0]
____________________________________________________________________________________________________
activation_43 (Activation) (None, 4, 4, 1024) 0 add_14[0][0]
____________________________________________________________________________________________________
res5a_branch2a (Conv2D) (None, 2, 2, 512) 524800 activation_43[0][0]
____________________________________________________________________________________________________
bn5a_branch2a (BatchNormalizatio (None, 2, 2, 512) 2048 res5a_branch2a[0][0]
____________________________________________________________________________________________________
activation_44 (Activation) (None, 2, 2, 512) 0 bn5a_branch2a[0][0]
____________________________________________________________________________________________________
res5a_branch2b (Conv2D) (None, 2, 2, 512) 2359808 activation_44[0][0]
____________________________________________________________________________________________________
bn5a_branch2b (BatchNormalizatio (None, 2, 2, 512) 2048 res5a_branch2b[0][0]
____________________________________________________________________________________________________
activation_45 (Activation) (None, 2, 2, 512) 0 bn5a_branch2b[0][0]
____________________________________________________________________________________________________
res5a_branch2c (Conv2D) (None, 2, 2, 2048) 1050624 activation_45[0][0]
____________________________________________________________________________________________________
res5a_branch1 (Conv2D) (None, 2, 2, 2048) 2099200 activation_43[0][0]
____________________________________________________________________________________________________
bn5a_branch2c (BatchNormalizatio (None, 2, 2, 2048) 8192 res5a_branch2c[0][0]
____________________________________________________________________________________________________
bn5a_branch1 (BatchNormalization (None, 2, 2, 2048) 8192 res5a_branch1[0][0]
____________________________________________________________________________________________________
add_15 (Add) (None, 2, 2, 2048) 0 bn5a_branch2c[0][0]
bn5a_branch1[0][0]
____________________________________________________________________________________________________
activation_46 (Activation) (None, 2, 2, 2048) 0 add_15[0][0]
____________________________________________________________________________________________________
res5b_branch2a (Conv2D) (None, 2, 2, 512) 1049088 activation_46[0][0]
____________________________________________________________________________________________________
bn5b_branch2a (BatchNormalizatio (None, 2, 2, 512) 2048 res5b_branch2a[0][0]
____________________________________________________________________________________________________
activation_47 (Activation) (None, 2, 2, 512) 0 bn5b_branch2a[0][0]
____________________________________________________________________________________________________
res5b_branch2b (Conv2D) (None, 2, 2, 512) 2359808 activation_47[0][0]
____________________________________________________________________________________________________
bn5b_branch2b (BatchNormalizatio (None, 2, 2, 512) 2048 res5b_branch2b[0][0]
____________________________________________________________________________________________________
activation_48 (Activation) (None, 2, 2, 512) 0 bn5b_branch2b[0][0]
____________________________________________________________________________________________________
res5b_branch2c (Conv2D) (None, 2, 2, 2048) 1050624 activation_48[0][0]
____________________________________________________________________________________________________
bn5b_branch2c (BatchNormalizatio (None, 2, 2, 2048) 8192 res5b_branch2c[0][0]
____________________________________________________________________________________________________
add_16 (Add) (None, 2, 2, 2048) 0 bn5b_branch2c[0][0]
activation_46[0][0]
____________________________________________________________________________________________________
activation_49 (Activation) (None, 2, 2, 2048) 0 add_16[0][0]
____________________________________________________________________________________________________
res5c_branch2a (Conv2D) (None, 2, 2, 512) 1049088 activation_49[0][0]
____________________________________________________________________________________________________
bn5c_branch2a (BatchNormalizatio (None, 2, 2, 512) 2048 res5c_branch2a[0][0]
____________________________________________________________________________________________________
activation_50 (Activation) (None, 2, 2, 512) 0 bn5c_branch2a[0][0]
____________________________________________________________________________________________________
res5c_branch2b (Conv2D) (None, 2, 2, 512) 2359808 activation_50[0][0]
____________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizatio (None, 2, 2, 512) 2048 res5c_branch2b[0][0]
____________________________________________________________________________________________________
activation_51 (Activation) (None, 2, 2, 512) 0 bn5c_branch2b[0][0]
____________________________________________________________________________________________________
res5c_branch2c (Conv2D) (None, 2, 2, 2048) 1050624 activation_51[0][0]
____________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizatio (None, 2, 2, 2048) 8192 res5c_branch2c[0][0]
____________________________________________________________________________________________________
add_17 (Add) (None, 2, 2, 2048) 0 bn5c_branch2c[0][0]
activation_49[0][0]
____________________________________________________________________________________________________
activation_52 (Activation) (None, 2, 2, 2048) 0 add_17[0][0]
____________________________________________________________________________________________________
avg_pool (AveragePooling2D) (None, 1, 1, 2048) 0 activation_52[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 2048) 0 avg_pool[0][0]
____________________________________________________________________________________________________
fc6 (Dense) (None, 6) 12294 flatten_1[0][0]
====================================================================================================
Total params: 23,600,006
Trainable params: 23,546,886
Non-trainable params: 53,120
____________________________________________________________________________________________________
| MIT | Convolutional Neural Networks/Residual_Networks_v2a.ipynb | joyfinder/Deep_Learning_Specialisation |
Finally, run the code below to visualize your ResNet50. You can also download a .png picture of your model by going to "File -> Open...-> model.png". | plot_model(model, to_file='model.png')
SVG(model_to_dot(model).create(prog='dot', format='svg')) | _____no_output_____ | MIT | Convolutional Neural Networks/Residual_Networks_v2a.ipynb | joyfinder/Deep_Learning_Specialisation |
Plotting is Back | %matplotlib inline
from massinference.plot import Limits
from massinference.map import KappaMap, ShearMap
import matplotlib.pyplot as plt
limits = Limits(1.8, 1.65, -2.0, -1.9)
plot_config = KappaMap.default().plot(limits=limits)
ShearMap.default().plot(plot_config=plot_config) | _____no_output_____ | MIT | GroupMeeting12_5_16.ipynb | davidthomas5412/PanglossNotebooks |
Rearchitected MassInference Benchmarking Benchmark- 36 square arcmin field- 10 source objects / arcmin- lightcnoe radius of 4 arcmins- no relevance filtering- no smooth kappas- 4 independent samples- no setup/io/etc, timer starts after objects initialized Pangloss ... 214.165 seconds MassInference ... 1.082 seconds Numpy Performance Hacks | import numpy as np
x = np.random.rand(10**8)
%timeit -n 1 -r 1 np.isnan(x)
%timeit -n 1 -r 1 np.isnan(np.sum(x))
%timeit -n 1 -r 1 np.sum(-x)
%timeit -n 1 -r 1 (-np.sum(x)) | 1 loop, best of 1: 6.02 s per loop
1 loop, best of 1: 1.51 s per loop
| MIT | GroupMeeting12_5_16.ipynb | davidthomas5412/PanglossNotebooks |
Table of Contents1 Data2 Model3 Training4 Explore Latent Space | import sys
import yaml
import tensorflow as tf
import numpy as np
import pandas as pd
import functools
from pathlib import Path
from datetime import datetime
from tqdm import tqdm_notebook as tqdm
# Plotting
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import animation
plt.rcParams['animation.ffmpeg_path'] = str(Path.home() / "anaconda3/envs/image-processing/bin/ffmpeg")
%load_ext autoreload
%autoreload 2
import dcgan
import gan_utils
from load_data import preprocess_images
from ds_utils.generative_utils import animate_latent_transition, gen_latent_linear, gen_latent_idx
from ds_utils.plot_utils import plot_sample_imgs
data_folder = Path.home() / "Documents/datasets"
# load model config
with open('configs/dcgan_celeba_config.yaml', 'r') as f:
config = yaml.load(f)
HIDDEN_DIM = config['data']['z_size']
IMG_SHAPE = config['data']['input_shape']
BATCH_SIZE = config['training']['batch_size']
IMG_IS_BW = IMG_SHAPE[2] == 1
PLOT_IMG_SHAPE = IMG_SHAPE[:2] if IMG_IS_BW else IMG_SHAPE
config | _____no_output_____ | Apache-2.0 | deep learning/GAN/DCGAN.ipynb | sugatoray/data-science-learning |
Data | # load Fashion MNIST dataset
((X_train, y_train), (X_test, y_test)) = tf.keras.datasets.fashion_mnist.load_data()
X_train = preprocess_images(X_train)
X_test = preprocess_images(X_test)
print(X_train[0].shape)
print(X_train[0].max())
print(X_train[0].min())
print(X_train.shape)
assert X_train[0].shape == tuple(config['data']['input_shape'])
train_ds = tf.data.Dataset.from_tensor_slices(X_train).take(5000)
test_ds = tf.data.Dataset.from_tensor_slices(X_test).take(256)
sys.path.append("../")
from tmp_load_data import load_imgs_tfdataset
train_ds = load_imgs_tfdataset(data_folder/'img_align_celeba', '*.jpg', config, 500, zipped=False)
test_ds = load_imgs_tfdataset(data_folder/'img_align_celeba', '*.jpg', config, 100, zipped=False) | _____no_output_____ | Apache-2.0 | deep learning/GAN/DCGAN.ipynb | sugatoray/data-science-learning |
Model | # instantiate GAN
gan = dcgan.DCGan(IMG_SHAPE, config)
# test generator
generator_out = gan.generator.predict(np.random.randn(BATCH_SIZE, HIDDEN_DIM))
generator_out.shape
# test discriminator
discriminator_out = gan.discriminator.predict(generator_out)
discriminator_out.shape
# test gan
gan.gan.predict(np.random.randn(BATCH_SIZE, HIDDEN_DIM)).max()
# plot random generated image
plt.imshow(gan.generator.predict([np.random.randn(1, HIDDEN_DIM)])[0]
.reshape(PLOT_IMG_SHAPE), cmap='gray' if IMG_IS_BW else 'jet')
plt.show()
gan.generator.summary() | _____no_output_____ | Apache-2.0 | deep learning/GAN/DCGAN.ipynb | sugatoray/data-science-learning |
Training | # setup model directory for checkpoint and tensorboard logs
model_name = "dcgan_celeba"
model_dir = Path.home() / "Documents/models/tf_playground/gan" / model_name
model_dir.mkdir(exist_ok=True, parents=True)
export_dir = model_dir / 'export'
export_dir.mkdir(exist_ok=True)
log_dir = model_dir / "logs" / datetime.now().strftime("%Y%m%d-%H%M%S")
nb_epochs = 1000
gan._train(train_ds=gan.setup_dataset(train_ds),
validation_ds=gan.setup_dataset(test_ds),
nb_epochs=nb_epochs,
log_dir=log_dir,
checkpoint_dir=export_dir,
is_tfdataset=True)
# export Keras model (.h5)
gan.generator.save(str(export_dir / 'generator.h5'))
gan.discriminator.save(str(export_dir / 'discriminator.h5'))
# plot generator results
plot_side = 5
plot_sample_imgs(lambda x: gan.generator.predict(np.random.randn(plot_side*plot_side, HIDDEN_DIM)),
img_shape=PLOT_IMG_SHAPE,
plot_side=plot_side,
cmap='gray' if IMG_IS_BW else 'jet') | _____no_output_____ | Apache-2.0 | deep learning/GAN/DCGAN.ipynb | sugatoray/data-science-learning |
Explore Latent Space | %matplotlib inline
def gen_image_fun(latent_vectors):
img = gan.generator.predict(latent_vectors)[0].reshape(PLOT_IMG_SHAPE)
return img
img = gen_image_fun(z_s)
render_dir = Path.home() / 'Documents/videos/gan' / "gan_celeba"
nb_samples = 10
nb_transition_frames = 10
nb_frames = min(2000, (nb_samples-1)*nb_transition_frames)
# random list of z vectors
z_s = np.random.randn(nb_samples, HIDDEN_DIM)
animate_latent_transition(latent_vectors=z_s,
gen_image_fun=gen_image_fun,
gen_latent_fun=lambda z_s, i: gen_latent_linear(z_s, i, nb_transition_frames),
img_size=PLOT_IMG_SHAPE,
nb_frames=nb_frames,
render_dir=render_dir)
render_dir = Path.home() / 'Documents/videos/gan' / "gan_fmnist_test"
nb_transition_frames = 10
# random list of z vectors
#rand_idx = np.random.randint(len(X_train))
z_start = np.random.randn(1, HIDDEN_DIM)
vals = np.linspace(-1., 1., nb_transition_frames)
for z_idx in range(20):
animate_latent_transition(latent_vectors=z_start,
gen_image_fun=gen_image_fun,
gen_latent_fun=lambda z_s, i: gen_latent_idx(z_s, i, z_idx, vals),
img_size=PLOT_IMG_SHAPE,
nb_frames=nb_transition_frames,
render_dir=render_dir) | _____no_output_____ | Apache-2.0 | deep learning/GAN/DCGAN.ipynb | sugatoray/data-science-learning |
ReferencesKoGPT3 shares the same structure as KoGPT2. - [KoGPT2-Transformers huggingface 활용 예시](https://github.com/taeminlee/KoGPT2-Transformers) | from transformers import GPT2Tokenizer, PreTrainedTokenizerFast
model_dir = "skt/ko-gpt-trinity-1.2B-v0.5"
# Load the Tokenizer: "Fast" means that the tokenizer code is written in Rust Lang
tokenizer = PreTrainedTokenizerFast.from_pretrained(
model_dir,
bos_token="<s>",
eos_token="</s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
)
from transformers import GPT2LMHeadModel
# designate the model's name registered on huggingface: https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5
model_dir = "skt/ko-gpt-trinity-1.2B-v0.5"
# Attach Language model Head to the pretrained GPT model
model = GPT2LMHeadModel.from_pretrained(model_dir) # KoGPT3 shares the same structure as KoGPT2.
import torch
# move the model to device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()
# encode the sample sentence
sample = "이 편지는 영국에서 최초로 시작되어 일년에 한바퀴 돌면서 받는 사람에게 행운을 주었고 지금은 당신에게로 옮겨진 이 편지는"
print(tokenizer.encode(sample))
import torch
torch.manual_seed(42)
# encode the sample sentence
input_ids = tokenizer.encode(sample, add_special_tokens=False, return_tensors="pt")
# generate output sequence from the given encoded input sequence
output_sequences = model.generate(input_ids=input_ids, do_sample=True, max_length=150, num_return_sequences=3)
# decode the output sequence and print its outcome
for index, generated_sequence in enumerate(output_sequences):
generated_sequence = generated_sequence.tolist()
decoded_sequence = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
print(f"Generated Sequence | Number {index} : {decoded_sequence}")
print()
| Generated Sequence | Number 0 : 이 편지는 영국에서 최초로 시작되어 일년에 한바퀴 돌면서 받는 사람에게 행운을 주었고 지금은 당신에게로 옮겨진 이 편지는 이 지구 상의 모든 사람에게 복을 주는 것이 된다. 이 편지는 그 내용이 진실되고 당신이 다른 사람에게 복을 주기를 바라는 마음이 담겨져 있다. 그리고 당신의 친구들은 이 편지를 읽고 당신의 행운을 기원해주며 당신에게 다시 연락한다. 마지막으로 당신의 친구들은 이 편지를 읽을 때마다 복을 받길 기원하며 당신에게 행운을 가져다 줄 것이다. 이 우체통에 넣은 편지는 당신이 한밤중에 받아도 좋을 것이다. 그 편지 안에는 당신이 받을 것으로 예상하는 것에 대한 목록이 나와 있다. 그리고 당신이 생각하고 있는 행운에 대한 목록이 있다. 당신에게 좋은 일이나 불행한 일을 예상해보기 바란다.
행운 편지지와 함께 당신이 예상
Generated Sequence | Number 1 : 이 편지는 영국에서 최초로 시작되어 일년에 한바퀴 돌면서 받는 사람에게 행운을 주었고 지금은 당신에게로 옮겨진 이 편지는 지금 당신의 가장 가까운 사람에게 당신의 가장 진실한 사랑을 전하는 편지입니다.
(
내가 가장 아끼는 사람은... )
<unk> 당신이 나의 삶의 가장 큰 행복이랍니다.
<unk> 당신이 나의 가장 사랑하는 사람이랍니다.
<unk> 나는 당신에게 한평생 좋은 친구가 될 것입니다.
<unk> 당신은 나와 우정을 나누면 좋은 친구이고
<unk> 나는 당신에게 일생의 동반자가 될 것입니다.
<unk> 당신은 나와 행복할 것이고 나와 슬픔을 나누면 행복할 것입니다.
<unk> 당신은 나와 하나가 될 것이고 당신이 바로 나입니다.
<unk> 당신은 나의 가장 소중한 친구입니다.
<unk> 당신은 나의
Generated Sequence | Number 2 : 이 편지는 영국에서 최초로 시작되어 일년에 한바퀴 돌면서 받는 사람에게 행운을 주었고 지금은 당신에게로 옮겨진 이 편지는 세계에서 가장 많은 사람이 읽어주는 이 편지 중에서 최고의 베스트 셀러가 되었다.
'당신이 원하는 모든 것은 당신 안에 있소. 당신의 소망을 들어주는 사람이 있다는 것이 얼마나 행운인지 아나요?'
'당신의 마음을 이해하게 된다면 그것만으로도 큰 기쁨이오. 당신은 당신 자신을 위해 무엇을 해야 할까요?"
'당신이 원하는 무엇이든 당신이 원하는 것을 하는 사람이 있으면 당신도 그것을 원하오. 당신이 지금 무엇을 해야 하는지는 결코 생각하지도 않으면서도 그것을 바라고 있지 않는 사람을 위한 것은 아무것도 없지.'
'당신이 지금 그것을 해야 한다는 것이 무엇을 의미하는지 아나요?'
'당신의 인생을 위해서 무엇이든 하시오. 모든
| MIT | inference_without_finetune_kogpt_trinity.ipynb | snoop2head/KoGPT-Joong-2 |
Suport Vector clustering let us learn how to work on svm in sk learn | import pandas as pd
from sklearn.datasets import load_iris
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
#importing necessary libraries
#loading the iris data set from the sklearn library
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
#seperating the data set and getting
#ready to get data ready to give input to the model
df['target'] = iris.target
df['flower_name'] = df.target.apply(lambda x: iris.target_names[x])
X = df.drop(['target', 'flower_name'], axis = 'columns')
y = df.target
#spliting the data set into 2 for traning and predicting
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2)
#traning the model and provideint the result
model = SVC()
model.fit(X_train, y_train)
#providing the presentage of the correct answer predicted
model.score(X_test, y_test) | _____no_output_____ | BSD-3-Clause | svc/SVC.ipynb | aswathyaa/Acharya-MachineLearning |
new dataframe with one column from each VM | new_df = pd.DataFrame()
for index in range(len(dataframes)):
diter = dataframes[index]
new_df[['net_write_' + diter.dataframeName]] = diter[['net_write']]
print(new_df.shape)
df = new_df
df.describe()
df.index
df.dtypes
df.head() | _____no_output_____ | Apache-2.0 | time_series/materna_dataset/Materna_PCA.ipynb | sanjosh/machine_learning |
Inf columns | df.columns.to_series()[np.isinf(df).any()]
df.index[np.isinf(df).any(1)]
import numpy as np
df.replace([np.inf, -np.inf], np.nan)
| _____no_output_____ | Apache-2.0 | time_series/materna_dataset/Materna_PCA.ipynb | sanjosh/machine_learning |
Null columns | df.isnull().values.any()
df[df.isnull().any(axis=1)]
df = df.interpolate( axis='columns')
df.dropna()
df.shape | _____no_output_____ | Apache-2.0 | time_series/materna_dataset/Materna_PCA.ipynb | sanjosh/machine_learning |
mean throughput over time per VM | ax = df.mean().plot(grid=False)
### mean throughput across VMs at any time
ax = df.T.mean().plot(grid=False)
| _____no_output_____ | Apache-2.0 | time_series/materna_dataset/Materna_PCA.ipynb | sanjosh/machine_learning |
multivariate PCAhttps://www.statsmodels.org/stable/examples/notebooks/generated/pca_fertility_factors.html | import statsmodels.api as sm
from statsmodels.multivariate.pca import PCA
pca_model = PCA(df, standardize=False, demean=True)
fig = pca_model.plot_scree(log_scale=False)
%matplotlib inline
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(8, 4))
lines = ax.plot(pca_model.factors.iloc[:,:3], lw=4, alpha=.6)
ax.set_xticklabels(df.T.columns.values[::10])
ax.set_xlim(0, 51)
ax.set_xlabel("time", size=17)
fig.subplots_adjust(.1, .1, .85, .9)
legend = fig.legend(lines, ['PC 1', 'PC 2', 'PC 3'], loc='center right')
legend.draw_frame(False)
idx = pca_model.loadings.iloc[:,0].argsort()
def make_plot(labels):
fig, ax = plt.subplots(figsize=(9,5))
ax = df.loc[labels].T.plot(legend=False, grid=False, ax=ax)
df.T.mean().plot(ax=ax, grid=False, label='Mean')
ax.set_xlim(0, 51);
fig.subplots_adjust(.1, .1, .75, .9)
ax.set_xlabel("time", size=17)
ax.set_ylabel("vm", size=17);
legend = ax.legend(*ax.get_legend_handles_labels(), loc='center left', bbox_to_anchor=(1, .5))
legend.draw_frame(False)
labels = df.index[idx[-5:]]
make_plot(labels)
idx = pca_model.loadings.iloc[:,1].argsort()
make_plot(df.index[idx[-5:]])
make_plot(df.index[idx[:5]])
fig, ax = plt.subplots()
pca_model.loadings.plot.scatter(x='comp_00',y='comp_01', ax=ax)
ax.set_xlabel("PC 1", size=17)
ax.set_ylabel("PC 2", size=17)
df.index[pca_model.loadings.iloc[:, 1] > .2].values | _____no_output_____ | Apache-2.0 | time_series/materna_dataset/Materna_PCA.ipynb | sanjosh/machine_learning |
Linear Regression with Db2 Stored Procedures Contents:* [1. Introduction](Introduction)* [2. Libraries and Modules](Libraries-and-Modules)* [3. Connect to Db2](Connect-to-Db2)* [4. Data exploration](Data-exploration)* [5. Train/Test Split](Train/Test-Split)* [6. Data transformation](Data-transformation-after-Train/Test-Split)* [7. Train a linear regression model](Train-a-linear-regression-model)* [8. Predict purchase amount for train and test data](Predict-sale-prices-for-test-data)* [9. Evaluate Model Performance](Evaluate-Model-Performance) 1. Introduction Historical customer data for a fictional outdoor equipment store is used in IBM offering tutorials to train the machine learning models. The sample data is structured in rows and columns.**Feature columns**Feature columns are columns that contain the attributes on which the machine learning model will base predictions. In this historical data, there are four feature columns:GENDER: Customer genderAGE: Customer ageMARITAL_STATUS: "Married", "Single", or "Unspecified"PROFESSION: General category of the customer's profession, such "Hospitality" or "Sales", or simply "Other"IS_TENT: Whether or not the customer bought a tentPRODUCT_LINE: The product category in which the customer has been most interested**Label column**PURCHASE_AMOUNT: The average amount of money the customer has spent on each visit to the storeLink: https://dataplatform.cloud.ibm.com/exchange/public/entry/view/aa07a773f71cf1172a349f33e2028e4e 2. Libraries and Modules | import os
import sys
module_path = os.path.abspath(os.path.join('../lib/'))
if module_path not in sys.path:
sys.path.append(module_path)
import ibm_db
import ibm_db_dbi
# import ibm_db_sa
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from InDBMLModules import col_to_row_organize, print_multi_result_set, connect_to_db,\
close_connection_to_db, drop_object, plot_histogram, plot_barchart,\
null_impute_most_freq, null_impute_mean, plot_pred_act
%load_ext autoreload
%autoreload 2 | _____no_output_____ | Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
3. Connect to Db2 | conn_str = "DATABASE=in_db;" + \
"HOSTNAME=*********************;"+ \
"PROTOCOL=TCPIP;" + \
"PORT=*******;" + \
"UID=***;" + \
"PWD=******************;"
ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=True)
rc = close_connection_to_db(ibm_db_conn, verbose=True) | Connected to the database!
Connection is closed.
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
4. Data exploration Create a special schema for this experiment | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
drop_object("LINREG", "SCHEMA", ibm_db_conn, verbose = True)
sql ="create schema LINREG authorization MLP"
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
print("Schema LINREG was created.")
rc = close_connection_to_db(ibm_db_conn, verbose=False) | Pre-existing SCHEMA LINREG was not found.
Schema LINREG was created.
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Collect statistics on the entire dataset by creating the column properties table | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
drop_object("LINREG.GS_COL_PROP", "TABLE", ibm_db_conn, verbose = True)
sql = """CALL IDAX.COLUMN_PROPERTIES('intable=DATA.GO_SALES, outtable=LINREG.GS_COL_PROP, withstatistics=true, incolumn=ID:id; PURCHASE_AMOUNT:target')"""
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
print("TABLE LINREG.GS_COL_PROP was created.")
rc = close_connection_to_db(ibm_db_conn, verbose=False) | Pre-existing TABLE LINREG.GS_COL_PROP was not found.
TABLE LINREG.GS_COL_PROP was created.
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
List columns with any nulls | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
sql = "select COLNO, NAME, TYPE,NUMMISSING,NUMMISSING+NUMINVALID+NUMVALID as ALL_VALUES, dec(NUMMISSING,10,2)/(dec(NUMMISSING, 10,2)+dec(NUMINVALID, 10,2)+dec(NUMVALID, 10,2))*100 as NULL_PERCENTAGE from LINREG.GS_COL_PROP where NUMMISSING > 0"
GS_NULL_PREC = pd.read_sql(sql,ibm_db_dbi_conn)
print("Column properties table fetched successfully!")
rc = close_connection_to_db(ibm_db_conn, verbose=False)
GS_NULL_PREC.sort_values('COLNO') | Column properties table fetched successfully!
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Evaluate CONTINIOUS columns using RUNSTATS Plot distribution based on runstats results | numerical_columns = ["AGE"]
plot_histogram (numerical_columns,"DATA","GO_SALES",conn_str) | _____no_output_____ | Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Evaluate NOMINAL columns using RUNSTATS Plot data distribution for nominal columns | nominal_columns = ["GENDER","MARITAL_STATUS","PROFESSION","PRODUCT_LINE","IS_TENT"]
plot_barchart (nominal_columns, "DATA", "GO_SALES", conn_str) | _____no_output_____ | Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Check data skewness using SUMMARY1000 stored procedure | # Create HOUSING_PRICES_SUM1000 table that contains whole dataset feature stats (mean, stdev, freq, etc)
ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
drop_object("LINREG.GO_SALES_SUM1000", "TABLE", ibm_db_conn, verbose = True)
drop_object("LINREG.GO_SALES_SUM1000_CHAR", "TABLE", ibm_db_conn, verbose = True)
drop_object("LINREG.GO_SALES_SUM1000_NUM", "TABLE", ibm_db_conn, verbose = True)
sql = "CALL IDAX.SUMMARY1000('intable=DATA.GO_SALES,outtable=LINREG.GO_SALES_SUM1000')"
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
print("SUM1000 tables were created.")
sql = "select * from LINREG.GO_SALES_SUM1000_NUM"
GO_SALES_SUM1000_NUM = pd.read_sql(sql,ibm_db_dbi_conn)
rc = close_connection_to_db(ibm_db_conn, verbose=False)
GO_SALES_SUM1000_NUM[["COLUMNNAME", "SKEWNESS"]] | Pre-existing TABLE LINREG.GO_SALES_SUM1000 was not found.
Pre-existing TABLE LINREG.GO_SALES_SUM1000_CHAR was not found.
Pre-existing TABLE LINREG.GO_SALES_SUM1000_NUM was not found.
SUM1000 tables were created.
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
**Observation:**SKEWNESS on numerical columns is negligible. 5. Train/Test Split | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
drop_object("LINREG.GSTRAIN", "TABLE", ibm_db_conn, verbose = True)
drop_object("LINREG.GSTEST", "TABLE", ibm_db_conn, verbose = True)
sql = "CALL IDAX.SPLIT_DATA('intable = DATA.GO_SALES, id = ID, traintable = LINREG.GSTRAIN, testtable = LINREG.GSTEST, fraction=0.8, seed=1')"
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
print("Dataset splitting was successful!")
rc = close_connection_to_db(ibm_db_conn, verbose=False) | Pre-existing TABLE LINREG.GSTRAIN was not found.
Pre-existing TABLE LINREG.GSTEST was not found.
Dataset splitting was successful!
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
6. Data transformation Get statistics of the train data to be used for transforming the test data Create the SUMMARY1000 table for training dataset | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
drop_object("LINREG.GSTRAIN_STATS", "TABLE", ibm_db_conn, verbose = True)
drop_object("LINREG.GSTRAIN_STATS_NUM", "TABLE", ibm_db_conn, verbose = True)
drop_object("LINREG.GSTRAIN_STATS_CHAR", "TABLE", ibm_db_conn, verbose = True)
sql = """CALL IDAX.SUMMARY1000('intable=LINREG.GSTRAIN,outtable=LINREG.GSTRAIN_STATS')"""
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
print("LINREG.GSTRAIN_STATS, LINREG.GSTRAIN_STATS_NUM, and LINREG.GSTRAIN_STATS_CHAR were created")
rc = close_connection_to_db(ibm_db_conn, verbose=False) | Pre-existing TABLE LINREG.GSTRAIN_STATS was not found.
Pre-existing TABLE LINREG.GSTRAIN_STATS_NUM was not found.
Pre-existing TABLE LINREG.GSTRAIN_STATS_CHAR was not found.
LINREG.GSTRAIN_STATS, LINREG.GSTRAIN_STATS_NUM, and LINREG.GSTRAIN_STATS_CHAR were created
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Null imputation Null impute NUMERICAL columns in TRAINING data with mean | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
sql = """CALL IDAX.IMPUTE_DATA('intable=LINREG.GSTRAIN,method=mean,inColumn=AGE');"""
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
print("AGE in LINREG.GSTRAIN null imputed successfully!")
rc = close_connection_to_db(ibm_db_conn, verbose=False) | AGE in LINREG.GSTRAIN null imputed successfully!
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Null impute the NOMINAL columns in TRAINING with the most frequent value | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
for column in nominal_columns:
null_impute_most_freq ("LINREG", "GSTRAIN", column, "GSTRAIN_STATS",ibm_db_conn, verbose=True)
rc = close_connection_to_db(ibm_db_conn, verbose=False) | GENDER in LINREG.GSTRAIN null imputed successfully!
MARITAL_STATUS in LINREG.GSTRAIN null imputed successfully!
PROFESSION in LINREG.GSTRAIN null imputed successfully!
PRODUCT_LINE in LINREG.GSTRAIN null imputed successfully!
IS_TENT in LINREG.GSTRAIN null imputed successfully!
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Null impute NUMERICAL column in TEST data with mean | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
for column in numerical_columns:
null_impute_mean("LINREG", "GSTEST", column, "GSTRAIN_STATS",ibm_db_conn, verbose=True)
rc = close_connection_to_db(ibm_db_conn, verbose=False) | AGE in LINREG.GSTEST null imputed successfully!
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Null impute the NOMINAL columns in TEST data with the most frequent value | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
for column in nominal_columns:
null_impute_most_freq ("LINREG", "GSTEST", column, "GSTRAIN_STATS",ibm_db_conn, verbose=True)
rc = close_connection_to_db(ibm_db_conn, verbose=False) | GENDER in LINREG.GSTEST null imputed successfully!
MARITAL_STATUS in LINREG.GSTEST null imputed successfully!
PROFESSION in LINREG.GSTEST null imputed successfully!
PRODUCT_LINE in LINREG.GSTEST null imputed successfully!
IS_TENT in LINREG.GSTEST null imputed successfully!
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Standardize AGE in training data | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
drop_object("LINREG.GSTRAIN_STD", "TABLE", ibm_db_conn, verbose = True)
sql = """CALL IDAX.STD_NORM('intable=LINREG.GSTRAIN,
incolumn="GENDER":L;"AGE":S;"MARITAL_STATUS":L;"PROFESSION":L;"IS_TENT":L;"PRODUCT_LINE":L;"PURCHASE_AMOUNT":L,
id=ID, outtable=LINREG.GSTRAIN_STD');"""
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
print("LINREG.GSTRAIN_STD was created and AGE column was standardized.")
rc = close_connection_to_db(ibm_db_conn, verbose=False) | Pre-existing TABLE LINREG.GSTRAIN_STD was not found.
LINREG.GSTRAIN_STD was created and AGE column was standardized.
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Standardize AGE in test data | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
drop_object("LINREG.GSTEST_STD", "TABLE", ibm_db_conn, verbose = True)
sql = "CREATE TABLE LINREG.GSTEST_STD AS (SELECT * FROM LINREG.GSTEST) WITH DATA"
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
print ("Table LINREG.GSTEST_STD was created.")
sql = """UPDATE LINREG.GSTEST_STD
SET AGE = ((CAST(AGE AS FLOAT) - (SELECT AVERAGE FROM LINREG.GSTRAIN_STATS_NUM WHERE COLUMNNAME='AGE'))/(SELECT STDDEV FROM LINREG.GSTRAIN_STATS_NUM WHERE COLUMNNAME='AGE'))"""
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
print("AGE was standardized in test data successfully!")
#renaming AGE to STD_AGE
sql = """ALTER TABLE LINREG.GSTEST_STD RENAME COLUMN AGE TO STD_AGE"""
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
rc = close_connection_to_db(ibm_db_conn, verbose=False) | Pre-existing TABLE LINREG.GSTEST_STD was not found.
Table LINREG.GSTEST_STD was created.
AGE was standardized in test data successfully!
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
7. Train a linear regression model Train the model | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
drop_object("LINREG.GSLINREG", "MODEL", ibm_db_conn, verbose = True)
sql = """CALL IDAX.LINEAR_REGRESSION('model=LINREG.GSLINREG, intable=LINREG.GSTRAIN_STD, id=ID,
target= PURCHASE_AMOUNT, incolumn =GENDER;STD_AGE;MARITAL_STATUS;PROFESSION;IS_TENT;PRODUCT_LINE');"""
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
print("Model trained successfully!")
rc = close_connection_to_db(ibm_db_conn, verbose=False) | Pre-existing MODEL LINREG.GSLINREG was not found.
Model trained successfully!
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
8. Predict purchase amount for train and test data Create view GSTEST_INPUT from feature columns in GSTEST_STD | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
drop_object("LINREG.GSTEST_INPUT", "VIEW", ibm_db_conn, verbose = True)
sql = "CREATE VIEW LINREG.GSTEST_INPUT AS (SELECT ID,GENDER,STD_AGE,MARITAL_STATUS,PROFESSION,IS_TENT,PRODUCT_LINE FROM LINREG.GSTEST_STD)"
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
print("VIEW LINREG.GSTEST_INPUT was created successfuly!")
rc = close_connection_to_db(ibm_db_conn, verbose=False) | Pre-existing VIEW LINREG.GSTEST_INPUT was not found.
VIEW LINREG.GSTEST_INPUT was created successfuly!
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Predict purchase amounts using IDAX.PREDICT_LINEAR_REGRESSION | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
drop_object("LINREG.GSTEST_OUTPUT", "TABLE", ibm_db_conn, verbose = True)
sql = """CALL IDAX.PREDICT_LINEAR_REGRESSION('model=LINREG.GSLINREG, intable=LINREG.GSTEST_INPUT, outtable =LINREG.GSTEST_OUTPUT, id=ID')"""
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
print("LINREG.GSTEST_OUTPUT was created with test results.")
rc = close_connection_to_db(ibm_db_conn, verbose=False) | Pre-existing TABLE LINREG.GSTEST_OUTPUT was not found.
LINREG.GSTEST_OUTPUT was created with test results.
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Create view GSTRAIN_INPUT from feature columns in GSTRAIN_STD | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
drop_object("LINREG.GSTRAIN_INPUT", "VIEW", ibm_db_conn, verbose = True)
sql = "CREATE VIEW LINREG.GSTRAIN_INPUT AS (SELECT ID,GENDER,STD_AGE,MARITAL_STATUS,PROFESSION,IS_TENT,PRODUCT_LINE FROM LINREG.GSTRAIN_STD)"
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
print("VIEW LINREG.GSTRAIN_INPUT was created successfuly!")
rc = close_connection_to_db(ibm_db_conn, verbose=False) | Pre-existing VIEW LINREG.GSTRAIN_INPUT was not found.
VIEW LINREG.GSTRAIN_INPUT was created successfuly!
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Predict purchase amounts using IDAX.PREDICT_LINEAR_REGRESSION | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
drop_object("LINREG.GSTRAIN_OUTPUT", "TABLE", ibm_db_conn, verbose = True)
sql = """CALL IDAX.PREDICT_LINEAR_REGRESSION('model=LINREG.GSLINREG, intable=LINREG.GSTRAIN_INPUT, outtable =LINREG.GSTRAIN_OUTPUT, id=ID')"""
stmt = ibm_db.exec_immediate(ibm_db_conn, sql)
print("LINREG.GSTEST_OUTPUT was created with train results.")
rc = close_connection_to_db(ibm_db_conn, verbose=False) | Pre-existing TABLE LINREG.GSTRAIN_OUTPUT was not found.
LINREG.GSTEST_OUTPUT was created with train results.
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
9. Evaluate Model Performance Evaluate model performance on TRAINING data | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
print("Training performance: ")
sql = """CALL IDAX.MSE('intable= LINREG.GSTRAIN_STD, id = ID, target = PURCHASE_AMOUNT, resulttable=LINREG.GSTRAIN_OUTPUT, resultid=ID, resulttarget=PURCHASE_AMOUNT')"""
print_multi_result_set(ibm_db_conn, sql)
sql = """CALL IDAX.MAE('intable= LINREG.GSTRAIN_STD, id = ID, target = PURCHASE_AMOUNT, resulttable=LINREG.GSTRAIN_OUTPUT, resultid=ID, resulttarget=PURCHASE_AMOUNT')"""
print_multi_result_set(ibm_db_conn, sql)
rc = close_connection_to_db(ibm_db_conn, verbose=False) | Training performance:
{'MSE': 98.36862842452432}
{'MAE': 7.5297574998766}
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Evaluate model performance on TEST data | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
print("Test performance: ")
sql = """CALL IDAX.MSE('intable= LINREG.GSTEST_STD, id = ID, target = PURCHASE_AMOUNT, resulttable=LINREG.GSTEST_OUTPUT, resultid=ID, resulttarget=PURCHASE_AMOUNT')"""
print_multi_result_set(ibm_db_conn, sql)
sql = """CALL IDAX.MAE('intable= LINREG.GSTEST_STD, id = ID, target = PURCHASE_AMOUNT, resulttable=LINREG.GSTEST_OUTPUT, resultid=ID, resulttarget=PURCHASE_AMOUNT')"""
print_multi_result_set(ibm_db_conn, sql)
rc = close_connection_to_db(ibm_db_conn, verbose=False) | Test performance:
{'MSE': 97.53595615684684}
{'MAE': 7.461251686160011}
| Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
**Observations:*** Mean absolute error on test data is 7.46 -> Model predicts with a fairly good accuracy.* Performance is consistent for Training and Test datasets -> Model is not overfitting the training set. Visually evaluate model performance | ibm_db_conn, ibm_db_dbi_conn = connect_to_db(conn_str, verbose=False)
sql = """select ACT.ID, ACT.PURCHASE_AMOUNT AS ACTUAL, PRED.PURCHASE_AMOUNT AS PREDICTION
from LINREG.GSTEST_STD AS ACT, LINREG.GSTEST_OUTPUT AS PRED
where ACT.ID = PRED.ID"""
GSTEST_ACT_PRED = pd.read_sql(sql,ibm_db_dbi_conn)
rc = close_connection_to_db(ibm_db_conn, verbose=False)
act = GSTEST_ACT_PRED.ACTUAL.values
pred = GSTEST_ACT_PRED.PREDICTION.values
plot_pred_act(pred,act,"Purchase Amount Prediction Performance on Test Data", "Actual", "Prediction") | _____no_output_____ | Apache-2.0 | In_Db2_Machine_Learning/Building ML Models with Db2/Notebooks/Regression_Demo.ipynb | ibmmichaelschapira/db2-samples |
Copyright 2018 The AdaNet Authors. | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License. | _____no_output_____ | Apache-2.0 | adanet/examples/tutorials/adanet_objective.ipynb | sararob/adanet |
The AdaNet objective Run in Google Colab View source on GitHub One of key contributions from *AdaNet: Adaptive Structural Learning of NeuralNetworks* [[Cortes et al., ICML 2017](https://arxiv.org/abs/1607.01097)] isdefining an algorithm that aims to directly minimize the DeepBoostgeneralization bound from *Deep Boosting*[[Cortes et al., ICML 2014](http://proceedings.mlr.press/v32/cortesb14.pdf)]when applied to neural networks. This algorithm, called **AdaNet**, adaptivelygrows a neural network as an ensemble of subnetworks that minimizes the AdaNetobjective (a.k.a. AdaNet loss):$$F(w) = \frac{1}{m} \sum_{i=1}^{m} \Phi \left(\sum_{j=1}^{N}w_jh_j(x_i), y_i \right) + \sum_{j=1}^{N} \left(\lambda r(h_j) + \beta \right) |w_j| $$where $w$ is the set of mixture weights, one per subnetwork $h$,$\Phi$ is a surrogate loss function such as logistic loss or MSE, $r$ is afunction for measuring a subnetwork's complexity, and $\lambda$ and $\beta$are hyperparameters. Mixture weightsSo what are mixture weights? When forming an ensemble $f$ of subnetworks $h$,we need to somehow combine the their predictions. This is done by multiplyingthe outputs of subnetwork $h_i$ with mixture weight $w_i$, and summing theresults:$$f(x) = \sum_{j=1}^{N}w_jh_j(x)$$In practice, most commonly used set of mixture weight is **uniform averageweighting**:$$f(x) = \frac{1}{N}\sum_{j=1}^{N}h_j(x)$$However, we can also solve a convex optimization problem to learn the mixtureweights that minimize the loss function $\Phi$:$$F(w) = \frac{1}{m} \sum_{i=1}^{m} \Phi \left(\sum_{j=1}^{N}w_jh_j(x_i), y_i \right)$$This is the first term in the AdaNet objective. The second term applies L1regularization to the mixture weights:$$\sum_{j=1}^{N} \left(\lambda r(h_j) + \beta \right) |w_j|$$When $\lambda > 0$ this penalty serves to prevent the optimization fromassigning too much weight to more complex subnetworks according to thecomplexity measure function $r$. How AdaNet uses the objectiveThis objective function serves two purposes:1. To **learn to scale/transform the outputs of each subnetwork $h$** as part of the ensemble.2. To **select the best candidate subnetwork $h$** at each AdaNet iteration to include in the ensemble.Effectively, when learning mixture weights $w$, AdaNet solves a convexcombination of the outputs of the frozen subnetworks $h$. For $\lambda >0$,AdaNet penalizes more complex subnetworks with greater L1 regularization ontheir mixture weight, and will be less likely to select more complex subnetworksto add to the ensemble at each iteration.In this tutorial, in you will observe the benefits of using AdaNet to learn theensemble's mixture weights and to perform candidate selection. | # If you're running this in Colab, first install the adanet package:
!pip install adanet
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import adanet
import tensorflow as tf
# The random seed to use.
RANDOM_SEED = 42 | _____no_output_____ | Apache-2.0 | adanet/examples/tutorials/adanet_objective.ipynb | sararob/adanet |
Boston Housing datasetIn this example, we will solve a regression task known as the [Boston Housing dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html) to predict the price of suburban houses in Boston, MA in the 1970s. There are 13 numerical features, the labels are in thousands of dollars, and there are only 506 examples. Download the dataConveniently, the data is available via Keras: | (x_train, y_train), (x_test, y_test) = (
tf.keras.datasets.boston_housing.load_data())
# Preview the first example from the training data
print('Model inputs: %s \n' % x_train[0])
print('Model output (house price): $%s ' % (y_train[0] * 1000))
| Model inputs: [ 1.23247 0. 8.14 0. 0.538 6.142 91.7
3.9769 4. 307. 21. 396.9 18.72 ]
Model output (house price): $15200.0
| Apache-2.0 | adanet/examples/tutorials/adanet_objective.ipynb | sararob/adanet |
Supply the data in TensorFlowOur first task is to supply the data in TensorFlow. Using thetf.estimator.Estimator convention, we will define a function that returns aninput_fn which returns feature and label Tensors.We will also use the tf.data.Dataset API to feed the data into our models.Also, as a preprocessing step, we will apply `tf.log1p` to log-scale thefeatures and labels for improved numerical stability during training. To recoverthe model's predictions in the correct scale, you can apply `tf.math.expm1` to theprediction. | FEATURES_KEY = "x"
def input_fn(partition, training, batch_size):
"""Generate an input function for the Estimator."""
def _input_fn():
if partition == "train":
dataset = tf.data.Dataset.from_tensor_slices(({
FEATURES_KEY: tf.log1p(x_train)
}, tf.log1p(y_train)))
else:
dataset = tf.data.Dataset.from_tensor_slices(({
FEATURES_KEY: tf.log1p(x_test)
}, tf.log1p(y_test)))
# We call repeat after shuffling, rather than before, to prevent separate
# epochs from blending together.
if training:
dataset = dataset.shuffle(10 * batch_size, seed=RANDOM_SEED).repeat()
dataset = dataset.batch(batch_size)
iterator = dataset.make_one_shot_iterator()
features, labels = iterator.get_next()
return features, labels
return _input_fn | _____no_output_____ | Apache-2.0 | adanet/examples/tutorials/adanet_objective.ipynb | sararob/adanet |
Define the subnetwork generatorLet's define a subnetwork generator similar to the one in[[Cortes et al., ICML 2017](https://arxiv.org/abs/1607.01097)] and in`simple_dnn.py` which creates two candidate fully-connected neural networks ateach iteration with the same width, but one an additional hidden layer. To makeour generator *adaptive*, each subnetwork will have at least the same numberof hidden layers as the most recently added subnetwork to the`previous_ensemble`.We define the complexity measure function $r$ to be $r(h) = \sqrt{d(h)}$, where$d$ is the number of hidden layers in the neural network $h$, to approximate theRademacher bounds from[[Golowich et. al, 2017](https://arxiv.org/abs/1712.06541)]. So subnetworkswith more hidden layers, and therefore more capacity, will have more heavilyregularized mixture weights. | _NUM_LAYERS_KEY = "num_layers"
class _SimpleDNNBuilder(adanet.subnetwork.Builder):
"""Builds a DNN subnetwork for AdaNet."""
def __init__(self, optimizer, layer_size, num_layers, learn_mixture_weights,
seed):
"""Initializes a `_DNNBuilder`.
Args:
optimizer: An `Optimizer` instance for training both the subnetwork and
the mixture weights.
layer_size: The number of nodes to output at each hidden layer.
num_layers: The number of hidden layers.
learn_mixture_weights: Whether to solve a learning problem to find the
best mixture weights, or use their default value according to the
mixture weight type. When `False`, the subnetworks will return a no_op
for the mixture weight train op.
seed: A random seed.
Returns:
An instance of `_SimpleDNNBuilder`.
"""
self._optimizer = optimizer
self._layer_size = layer_size
self._num_layers = num_layers
self._learn_mixture_weights = learn_mixture_weights
self._seed = seed
def build_subnetwork(self,
features,
logits_dimension,
training,
iteration_step,
summary,
previous_ensemble=None):
"""See `adanet.subnetwork.Builder`."""
input_layer = tf.to_float(features[FEATURES_KEY])
kernel_initializer = tf.glorot_uniform_initializer(seed=self._seed)
last_layer = input_layer
for _ in range(self._num_layers):
last_layer = tf.layers.dense(
last_layer,
units=self._layer_size,
activation=tf.nn.relu,
kernel_initializer=kernel_initializer)
logits = tf.layers.dense(
last_layer,
units=logits_dimension,
kernel_initializer=kernel_initializer)
persisted_tensors = {_NUM_LAYERS_KEY: tf.constant(self._num_layers)}
return adanet.Subnetwork(
last_layer=last_layer,
logits=logits,
complexity=self._measure_complexity(),
persisted_tensors=persisted_tensors)
def _measure_complexity(self):
"""Approximates Rademacher complexity as the square-root of the depth."""
return tf.sqrt(tf.to_float(self._num_layers))
def build_subnetwork_train_op(self, subnetwork, loss, var_list, labels,
iteration_step, summary, previous_ensemble):
"""See `adanet.subnetwork.Builder`."""
return self._optimizer.minimize(loss=loss, var_list=var_list)
def build_mixture_weights_train_op(self, loss, var_list, logits, labels,
iteration_step, summary):
"""See `adanet.subnetwork.Builder`."""
if not self._learn_mixture_weights:
return tf.no_op()
return self._optimizer.minimize(loss=loss, var_list=var_list)
@property
def name(self):
"""See `adanet.subnetwork.Builder`."""
if self._num_layers == 0:
# A DNN with no hidden layers is a linear model.
return "linear"
return "{}_layer_dnn".format(self._num_layers)
class SimpleDNNGenerator(adanet.subnetwork.Generator):
"""Generates a two DNN subnetworks at each iteration.
The first DNN has an identical shape to the most recently added subnetwork
in `previous_ensemble`. The second has the same shape plus one more dense
layer on top. This is similar to the adaptive network presented in Figure 2 of
[Cortes et al. ICML 2017](https://arxiv.org/abs/1607.01097), without the
connections to hidden layers of networks from previous iterations.
"""
def __init__(self,
optimizer,
layer_size=32,
learn_mixture_weights=False,
seed=None):
"""Initializes a DNN `Generator`.
Args:
optimizer: An `Optimizer` instance for training both the subnetwork and
the mixture weights.
layer_size: Number of nodes in each hidden layer of the subnetwork
candidates. Note that this parameter is ignored in a DNN with no hidden
layers.
learn_mixture_weights: Whether to solve a learning problem to find the
best mixture weights, or use their default value according to the
mixture weight type. When `False`, the subnetworks will return a no_op
for the mixture weight train op.
seed: A random seed.
Returns:
An instance of `Generator`.
"""
self._seed = seed
self._dnn_builder_fn = functools.partial(
_SimpleDNNBuilder,
optimizer=optimizer,
layer_size=layer_size,
learn_mixture_weights=learn_mixture_weights)
def generate_candidates(self, previous_ensemble, iteration_number,
previous_ensemble_reports, all_reports):
"""See `adanet.subnetwork.Generator`."""
num_layers = 0
seed = self._seed
if previous_ensemble:
num_layers = tf.contrib.util.constant_value(
previous_ensemble.weighted_subnetworks[
-1].subnetwork.persisted_tensors[_NUM_LAYERS_KEY])
if seed is not None:
seed += iteration_number
return [
self._dnn_builder_fn(num_layers=num_layers, seed=seed),
self._dnn_builder_fn(num_layers=num_layers + 1, seed=seed),
] | _____no_output_____ | Apache-2.0 | adanet/examples/tutorials/adanet_objective.ipynb | sararob/adanet |
Train and evaluateNext we create an `adanet.Estimator` using the `SimpleDNNGenerator` we just defined.In this section we will show the effects of two hyperparamters: **learning mixture weights** and **complexity regularization**.On the righthand side you will be able to play with the hyperparameters of this model. Until you reach the end of this section, we ask that you not change them. At first we will not learn the mixture weights, using their default initial value. Here they will be scalars initialized to $1/N$ where $N$ is the number of subnetworks in the ensemble, effectively creating a **uniform average ensemble**. | #@title AdaNet parameters
LEARNING_RATE = 0.001 #@param {type:"number"}
TRAIN_STEPS = 100000 #@param {type:"integer"}
BATCH_SIZE = 32 #@param {type:"integer"}
LEARN_MIXTURE_WEIGHTS = False #@param {type:"boolean"}
ADANET_LAMBDA = 0 #@param {type:"number"}
BOOSTING_ITERATIONS = 5 #@param {type:"integer"}
def train_and_evaluate(learn_mixture_weights=LEARN_MIXTURE_WEIGHTS,
adanet_lambda=ADANET_LAMBDA):
"""Trains an `adanet.Estimator` to predict housing prices."""
estimator = adanet.Estimator(
# Since we are predicting housing prices, we'll use a regression
# head that optimizes for MSE.
head=tf.contrib.estimator.regression_head(
loss_reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE),
# Define the generator, which defines our search space of subnetworks
# to train as candidates to add to the final AdaNet model.
subnetwork_generator=SimpleDNNGenerator(
optimizer=tf.train.RMSPropOptimizer(learning_rate=LEARNING_RATE),
learn_mixture_weights=learn_mixture_weights,
seed=RANDOM_SEED),
# Lambda is a the strength of complexity regularization. A larger
# value will penalize more complex subnetworks.
adanet_lambda=adanet_lambda,
# The number of train steps per iteration.
max_iteration_steps=TRAIN_STEPS // BOOSTING_ITERATIONS,
# The evaluator will evaluate the model on the full training set to
# compute the overall AdaNet loss (train loss + complexity
# regularization) to select the best candidate to include in the
# final AdaNet model.
evaluator=adanet.Evaluator(
input_fn=input_fn("train", training=False, batch_size=BATCH_SIZE)),
# Configuration for Estimators.
config=tf.estimator.RunConfig(
save_checkpoints_steps=50000,
save_summary_steps=50000,
tf_random_seed=RANDOM_SEED))
# Train and evaluate using using the tf.estimator tooling.
train_spec = tf.estimator.TrainSpec(
input_fn=input_fn("train", training=True, batch_size=BATCH_SIZE),
max_steps=TRAIN_STEPS)
eval_spec = tf.estimator.EvalSpec(
input_fn=input_fn("test", training=False, batch_size=BATCH_SIZE),
steps=None)
return tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
def ensemble_architecture(result):
"""Extracts the ensemble architecture from evaluation results."""
architecture = result["architecture/adanet/ensembles"]
# The architecture is a serialized Summary proto for TensorBoard.
summary_proto = tf.summary.Summary.FromString(architecture)
return summary_proto.value[0].tensor.string_val[0]
results, _ = train_and_evaluate()
print("Loss:", results["average_loss"])
print("Architecture:", ensemble_architecture(results)) | WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpBX73lD
INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f27c3980390>, '_model_dir': '/tmp/tmpBX73lD', '_protocol': None, '_save_checkpoints_steps': 50000, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_tf_random_seed': 42, '_save_summary_steps': 50000, '_device_fn': None, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_evaluation_master': '', '_eval_distribute': None, '_train_distribute': None, '_master': ''}
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 50000 or save_checkpoints_secs None.
INFO:tensorflow:Beginning training AdaNet iteration 0
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Building iteration 0
INFO:tensorflow:Building subnetwork 'linear'
WARNING:tensorflow:From <ipython-input-15-6099e5c14e79>:60: calling __new__ (from adanet.core.subnetwork.generator) with persisted_tensors is deprecated and will be removed in a future version.
Instructions for updating:
`persisted_tensors` is deprecated, please use `shared` instead.
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpBX73lD/model.ckpt.
INFO:tensorflow:loss = 21.773132, step = 1
INFO:tensorflow:global_step/sec: 218.829
INFO:tensorflow:loss = 0.647101, step = 101 (0.458 sec)
INFO:tensorflow:global_step/sec: 600.6
INFO:tensorflow:loss = 0.58654284, step = 201 (0.166 sec)
INFO:tensorflow:global_step/sec: 507.035
INFO:tensorflow:loss = 0.07683488, step = 301 (0.197 sec)
INFO:tensorflow:global_step/sec: 561.539
INFO:tensorflow:loss = 0.08281773, step = 401 (0.178 sec)
INFO:tensorflow:global_step/sec: 550.797
INFO:tensorflow:loss = 0.08148783, step = 501 (0.182 sec)
INFO:tensorflow:global_step/sec: 532.507
INFO:tensorflow:loss = 0.056522045, step = 601 (0.188 sec)
INFO:tensorflow:global_step/sec: 546.83
INFO:tensorflow:loss = 0.025881847, step = 701 (0.183 sec)
INFO:tensorflow:global_step/sec: 533.994
INFO:tensorflow:loss = 0.030095275, step = 801 (0.187 sec)
INFO:tensorflow:global_step/sec: 580.347
INFO:tensorflow:loss = 0.03755435, step = 901 (0.172 sec)
INFO:tensorflow:global_step/sec: 524.546
INFO:tensorflow:loss = 0.06690027, step = 1001 (0.191 sec)
INFO:tensorflow:global_step/sec: 539.782
INFO:tensorflow:loss = 0.036151223, step = 1101 (0.185 sec)
INFO:tensorflow:global_step/sec: 554.345
INFO:tensorflow:loss = 0.05018542, step = 1201 (0.180 sec)
INFO:tensorflow:global_step/sec: 580.845
INFO:tensorflow:loss = 0.09921485, step = 1301 (0.172 sec)
INFO:tensorflow:global_step/sec: 562.908
INFO:tensorflow:loss = 0.026417136, step = 1401 (0.178 sec)
INFO:tensorflow:global_step/sec: 558.397
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INFO:tensorflow:global_step/sec: 583.761
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INFO:tensorflow:global_step/sec: 562.015
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INFO:tensorflow:global_step/sec: 562.949
INFO:tensorflow:loss = 0.020140037, step = 9101 (0.178 sec)
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INFO:tensorflow:global_step/sec: 555.454
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INFO:tensorflow:global_step/sec: 567.627
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INFO:tensorflow:global_step/sec: 565.042
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INFO:tensorflow:global_step/sec: 559.67
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INFO:tensorflow:global_step/sec: 552.605
INFO:tensorflow:loss = 0.021412933, step = 9801 (0.181 sec)
INFO:tensorflow:global_step/sec: 566.807
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INFO:tensorflow:global_step/sec: 543.934
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INFO:tensorflow:global_step/sec: 576.352
INFO:tensorflow:loss = 0.021032713, step = 10101 (0.173 sec)
INFO:tensorflow:global_step/sec: 574.218
INFO:tensorflow:loss = 0.043715518, step = 10201 (0.174 sec)
INFO:tensorflow:global_step/sec: 563.383
INFO:tensorflow:loss = 0.031914454, step = 10301 (0.178 sec)
INFO:tensorflow:global_step/sec: 564.284
INFO:tensorflow:loss = 0.03337904, step = 10401 (0.177 sec)
INFO:tensorflow:global_step/sec: 574.841
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INFO:tensorflow:global_step/sec: 553.689
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INFO:tensorflow:global_step/sec: 564.687
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INFO:tensorflow:global_step/sec: 569.743
INFO:tensorflow:loss = 0.021361638, step = 10801 (0.176 sec)
INFO:tensorflow:global_step/sec: 558.066
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INFO:tensorflow:global_step/sec: 536.901
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INFO:tensorflow:global_step/sec: 530.648
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INFO:tensorflow:global_step/sec: 542.149
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INFO:tensorflow:global_step/sec: 569.444
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INFO:tensorflow:global_step/sec: 569.674
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INFO:tensorflow:global_step/sec: 568.689
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INFO:tensorflow:global_step/sec: 581.913
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INFO:tensorflow:global_step/sec: 587.987
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INFO:tensorflow:global_step/sec: 571.542
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INFO:tensorflow:global_step/sec: 571.893
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INFO:tensorflow:global_step/sec: 560.164
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INFO:tensorflow:global_step/sec: 535.277
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INFO:tensorflow:global_step/sec: 574.835
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INFO:tensorflow:global_step/sec: 566.044
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INFO:tensorflow:global_step/sec: 556.514
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INFO:tensorflow:global_step/sec: 567.765
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INFO:tensorflow:global_step/sec: 583.745
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INFO:tensorflow:global_step/sec: 574.514
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INFO:tensorflow:global_step/sec: 579.71
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INFO:tensorflow:global_step/sec: 569.498
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INFO:tensorflow:global_step/sec: 569.372
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INFO:tensorflow:global_step/sec: 552.538
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INFO:tensorflow:global_step/sec: 583.199
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INFO:tensorflow:global_step/sec: 572.411
INFO:tensorflow:loss = 0.03021842, step = 18401 (0.175 sec)
INFO:tensorflow:global_step/sec: 560.004
INFO:tensorflow:loss = 0.017465986, step = 18501 (0.178 sec)
INFO:tensorflow:global_step/sec: 584.262
INFO:tensorflow:loss = 0.018047271, step = 18601 (0.171 sec)
INFO:tensorflow:global_step/sec: 559.241
INFO:tensorflow:loss = 0.04243151, step = 18701 (0.179 sec)
INFO:tensorflow:global_step/sec: 567.762
INFO:tensorflow:loss = 0.009879965, step = 18801 (0.177 sec)
INFO:tensorflow:global_step/sec: 559.469
INFO:tensorflow:loss = 0.026315855, step = 18901 (0.178 sec)
INFO:tensorflow:global_step/sec: 568.677
INFO:tensorflow:loss = 0.014082297, step = 19001 (0.175 sec)
INFO:tensorflow:global_step/sec: 582.489
INFO:tensorflow:loss = 0.02952011, step = 19101 (0.172 sec)
INFO:tensorflow:global_step/sec: 586.122
INFO:tensorflow:loss = 0.024289865, step = 19201 (0.170 sec)
INFO:tensorflow:global_step/sec: 578.61
INFO:tensorflow:loss = 0.019341573, step = 19301 (0.173 sec)
INFO:tensorflow:global_step/sec: 551.298
INFO:tensorflow:loss = 0.015597891, step = 19401 (0.181 sec)
INFO:tensorflow:global_step/sec: 574.554
INFO:tensorflow:loss = 0.013870528, step = 19501 (0.174 sec)
INFO:tensorflow:global_step/sec: 581.304
INFO:tensorflow:loss = 0.011807093, step = 19601 (0.172 sec)
INFO:tensorflow:global_step/sec: 572.617
INFO:tensorflow:loss = 0.0114907455, step = 19701 (0.175 sec)
INFO:tensorflow:global_step/sec: 573.516
INFO:tensorflow:loss = 0.017667146, step = 19801 (0.174 sec)
INFO:tensorflow:global_step/sec: 558.638
INFO:tensorflow:loss = 0.04704179, step = 19901 (0.179 sec)
INFO:tensorflow:Saving checkpoints for 20000 into /tmp/tmpBX73lD/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Building iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:23:18
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpBX73lD/model.ckpt-20000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't0_linear' dict for global step 20000: architecture/adanet/ensembles =
W
9adanet/iteration_0/ensemble_t0_linear/architecture/adanetB B
| linear |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.049421377, average_loss/adanet/subnetwork = 0.049421377, average_loss/adanet/uniform_average_ensemble = 0.049421377, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.062442042, loss/adanet/subnetwork = 0.062442042, loss/adanet/uniform_average_ensemble = 0.062442042, prediction/mean/adanet/adanet_weighted_ensemble = 3.105895, prediction/mean/adanet/subnetwork = 3.105895, prediction/mean/adanet/uniform_average_ensemble = 3.105895
INFO:tensorflow:Saving candidate 't0_1_layer_dnn' dict for global step 20000: architecture/adanet/ensembles =
a
>adanet/iteration_0/ensemble_t0_1_layer_dnn/architecture/adanetB B| 1_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.03993654, average_loss/adanet/subnetwork = 0.03993654, average_loss/adanet/uniform_average_ensemble = 0.03993654, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.053605493, loss/adanet/subnetwork = 0.053605493, loss/adanet/uniform_average_ensemble = 0.053605493, prediction/mean/adanet/adanet_weighted_ensemble = 3.1580222, prediction/mean/adanet/subnetwork = 3.1580222, prediction/mean/adanet/uniform_average_ensemble = 3.1580222
INFO:tensorflow:Finished evaluation at 2018-12-13-19:23:19
INFO:tensorflow:Saving dict for global step 20000: average_loss = 0.03993654, average_loss/adanet/adanet_weighted_ensemble = 0.03993654, average_loss/adanet/subnetwork = 0.03993654, average_loss/adanet/uniform_average_ensemble = 0.03993654, global_step = 20000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.053605493, loss/adanet/adanet_weighted_ensemble = 0.053605493, loss/adanet/subnetwork = 0.053605493, loss/adanet/uniform_average_ensemble = 0.053605493, prediction/mean = 3.1580222, prediction/mean/adanet/adanet_weighted_ensemble = 3.1580222, prediction/mean/adanet/subnetwork = 3.1580222, prediction/mean/adanet/uniform_average_ensemble = 3.1580222
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20000: /tmp/tmpBX73lD/model.ckpt-20000
INFO:tensorflow:Loss for final step: 0.034048468.
INFO:tensorflow:Finished training Adanet iteration 0
INFO:tensorflow:Beginning bookkeeping phase for iteration 0
INFO:tensorflow:Building iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Starting ensemble evaluation for iteration 0
INFO:tensorflow:Restoring parameters from /tmp/tmpBX73lD/model.ckpt-20000
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/adanet/core/estimator.py:717: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Encountered end of input after 14 evaluations
INFO:tensorflow:Computed ensemble metrics: adanet_loss/t0_linear = 0.035089, adanet_loss/t0_1_layer_dnn = 0.020803
INFO:tensorflow:Finished ensemble evaluation for iteration 0
INFO:tensorflow:'t0_1_layer_dnn' at index 1 is moving onto the next iteration
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-0.txt: ['0:1_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Warm-starting from: (u'/tmp/tmpBX73lD/model.ckpt-20000',)
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: global_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Building iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Overwriting checkpoint with new graph for iteration 1 to /tmp/tmpBX73lD/model.ckpt-20000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Finished bookkeeping phase for iteration 0
INFO:tensorflow:Beginning training AdaNet iteration 1
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-0.txt: ['0:1_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpBX73lD/increment.ckpt-1
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 20000 into /tmp/tmpBX73lD/model.ckpt.
INFO:tensorflow:loss = 0.02689482, step = 20001
INFO:tensorflow:global_step/sec: 177.265
INFO:tensorflow:loss = 0.026641333, step = 20101 (0.565 sec)
INFO:tensorflow:global_step/sec: 571.393
INFO:tensorflow:loss = 0.020572826, step = 20201 (0.175 sec)
INFO:tensorflow:global_step/sec: 534.231
INFO:tensorflow:loss = 0.018674508, step = 20301 (0.187 sec)
INFO:tensorflow:global_step/sec: 552.2
INFO:tensorflow:loss = 0.027517587, step = 20401 (0.181 sec)
INFO:tensorflow:global_step/sec: 510.477
INFO:tensorflow:loss = 0.01638335, step = 20501 (0.196 sec)
INFO:tensorflow:global_step/sec: 542.85
INFO:tensorflow:loss = 0.018517539, step = 20601 (0.184 sec)
INFO:tensorflow:global_step/sec: 547.313
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INFO:tensorflow:global_step/sec: 547.207
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INFO:tensorflow:global_step/sec: 517.958
INFO:tensorflow:loss = 0.024092598, step = 21201 (0.193 sec)
INFO:tensorflow:global_step/sec: 583.39
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INFO:tensorflow:global_step/sec: 524.161
INFO:tensorflow:loss = 0.017033618, step = 21401 (0.191 sec)
INFO:tensorflow:global_step/sec: 559.967
INFO:tensorflow:loss = 0.01003223, step = 21501 (0.179 sec)
INFO:tensorflow:global_step/sec: 541.765
INFO:tensorflow:loss = 0.01647801, step = 21601 (0.185 sec)
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INFO:tensorflow:global_step/sec: 547.336
INFO:tensorflow:loss = 0.023737881, step = 21901 (0.183 sec)
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INFO:tensorflow:global_step/sec: 558.046
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INFO:tensorflow:global_step/sec: 545.449
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INFO:tensorflow:global_step/sec: 549.441
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INFO:tensorflow:global_step/sec: 556.306
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INFO:tensorflow:global_step/sec: 532.244
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INFO:tensorflow:global_step/sec: 535.174
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INFO:tensorflow:global_step/sec: 536.619
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INFO:tensorflow:global_step/sec: 524.701
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INFO:tensorflow:global_step/sec: 567.924
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INFO:tensorflow:global_step/sec: 556.693
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INFO:tensorflow:global_step/sec: 549.466
INFO:tensorflow:loss = 0.018869447, step = 33501 (0.182 sec)
INFO:tensorflow:global_step/sec: 538.933
INFO:tensorflow:loss = 0.014404563, step = 33601 (0.186 sec)
INFO:tensorflow:global_step/sec: 570.873
INFO:tensorflow:loss = 0.007743339, step = 33701 (0.175 sec)
INFO:tensorflow:global_step/sec: 523.04
INFO:tensorflow:loss = 0.021582767, step = 33801 (0.191 sec)
INFO:tensorflow:global_step/sec: 533.758
INFO:tensorflow:loss = 0.009738045, step = 33901 (0.187 sec)
INFO:tensorflow:global_step/sec: 549.783
INFO:tensorflow:loss = 0.010697973, step = 34001 (0.182 sec)
INFO:tensorflow:global_step/sec: 549.312
INFO:tensorflow:loss = 0.014111896, step = 34101 (0.182 sec)
INFO:tensorflow:global_step/sec: 506.714
INFO:tensorflow:loss = 0.01161824, step = 34201 (0.198 sec)
INFO:tensorflow:global_step/sec: 528.597
INFO:tensorflow:loss = 0.013626029, step = 34301 (0.189 sec)
INFO:tensorflow:global_step/sec: 549.305
INFO:tensorflow:loss = 0.014306208, step = 34401 (0.186 sec)
INFO:tensorflow:global_step/sec: 539.406
INFO:tensorflow:loss = 0.010782365, step = 34501 (0.182 sec)
INFO:tensorflow:global_step/sec: 564.149
INFO:tensorflow:loss = 0.014526726, step = 34601 (0.177 sec)
INFO:tensorflow:global_step/sec: 543.195
INFO:tensorflow:loss = 0.016402217, step = 34701 (0.184 sec)
INFO:tensorflow:global_step/sec: 554.256
INFO:tensorflow:loss = 0.019311573, step = 34801 (0.181 sec)
INFO:tensorflow:global_step/sec: 547.708
INFO:tensorflow:loss = 0.022482123, step = 34901 (0.182 sec)
INFO:tensorflow:global_step/sec: 527.343
INFO:tensorflow:loss = 0.02219373, step = 35001 (0.190 sec)
INFO:tensorflow:global_step/sec: 538.46
INFO:tensorflow:loss = 0.010233812, step = 35101 (0.186 sec)
INFO:tensorflow:global_step/sec: 545.09
INFO:tensorflow:loss = 0.011155672, step = 35201 (0.183 sec)
INFO:tensorflow:global_step/sec: 510.36
INFO:tensorflow:loss = 0.019443393, step = 35301 (0.196 sec)
INFO:tensorflow:global_step/sec: 549.995
INFO:tensorflow:loss = 0.0088263145, step = 35401 (0.182 sec)
INFO:tensorflow:global_step/sec: 539.31
INFO:tensorflow:loss = 0.019199822, step = 35501 (0.185 sec)
INFO:tensorflow:global_step/sec: 547.963
INFO:tensorflow:loss = 0.016015904, step = 35601 (0.182 sec)
INFO:tensorflow:global_step/sec: 542.803
INFO:tensorflow:loss = 0.012871675, step = 35701 (0.184 sec)
INFO:tensorflow:global_step/sec: 538.764
INFO:tensorflow:loss = 0.021360168, step = 35801 (0.186 sec)
INFO:tensorflow:global_step/sec: 528.896
INFO:tensorflow:loss = 0.015004412, step = 35901 (0.189 sec)
INFO:tensorflow:global_step/sec: 550.146
INFO:tensorflow:loss = 0.016787032, step = 36001 (0.182 sec)
INFO:tensorflow:global_step/sec: 544.037
INFO:tensorflow:loss = 0.02503136, step = 36101 (0.184 sec)
INFO:tensorflow:global_step/sec: 554.173
INFO:tensorflow:loss = 0.008402772, step = 36201 (0.181 sec)
INFO:tensorflow:global_step/sec: 556.137
INFO:tensorflow:loss = 0.0091250455, step = 36301 (0.180 sec)
INFO:tensorflow:global_step/sec: 532.992
INFO:tensorflow:loss = 0.0181378, step = 36401 (0.188 sec)
INFO:tensorflow:global_step/sec: 550.661
INFO:tensorflow:loss = 0.008492513, step = 36501 (0.181 sec)
INFO:tensorflow:global_step/sec: 536.25
INFO:tensorflow:loss = 0.0114019755, step = 36601 (0.187 sec)
INFO:tensorflow:global_step/sec: 549.158
INFO:tensorflow:loss = 0.02097696, step = 36701 (0.182 sec)
INFO:tensorflow:global_step/sec: 562.939
INFO:tensorflow:loss = 0.0132971015, step = 36801 (0.178 sec)
INFO:tensorflow:global_step/sec: 521.469
INFO:tensorflow:loss = 0.00968274, step = 36901 (0.192 sec)
INFO:tensorflow:global_step/sec: 563.196
INFO:tensorflow:loss = 0.014091542, step = 37001 (0.177 sec)
INFO:tensorflow:global_step/sec: 547.948
INFO:tensorflow:loss = 0.020744445, step = 37101 (0.183 sec)
INFO:tensorflow:global_step/sec: 564.589
INFO:tensorflow:loss = 0.009579487, step = 37201 (0.177 sec)
INFO:tensorflow:global_step/sec: 549.351
INFO:tensorflow:loss = 0.011741485, step = 37301 (0.182 sec)
INFO:tensorflow:global_step/sec: 573.677
INFO:tensorflow:loss = 0.009951888, step = 37401 (0.174 sec)
INFO:tensorflow:global_step/sec: 524.599
INFO:tensorflow:loss = 0.014136355, step = 37501 (0.191 sec)
INFO:tensorflow:global_step/sec: 547.861
INFO:tensorflow:loss = 0.014360774, step = 37601 (0.183 sec)
INFO:tensorflow:global_step/sec: 539.901
INFO:tensorflow:loss = 0.00806953, step = 37701 (0.185 sec)
INFO:tensorflow:global_step/sec: 551.742
INFO:tensorflow:loss = 0.014863034, step = 37801 (0.181 sec)
INFO:tensorflow:global_step/sec: 556.973
INFO:tensorflow:loss = 0.008398596, step = 37901 (0.180 sec)
INFO:tensorflow:global_step/sec: 548.026
INFO:tensorflow:loss = 0.017693192, step = 38001 (0.190 sec)
INFO:tensorflow:global_step/sec: 519.251
INFO:tensorflow:loss = 0.01951421, step = 38101 (0.185 sec)
INFO:tensorflow:global_step/sec: 557.135
INFO:tensorflow:loss = 0.013768952, step = 38201 (0.180 sec)
INFO:tensorflow:global_step/sec: 562.828
INFO:tensorflow:loss = 0.019956227, step = 38301 (0.178 sec)
INFO:tensorflow:global_step/sec: 546.224
INFO:tensorflow:loss = 0.018904533, step = 38401 (0.183 sec)
INFO:tensorflow:global_step/sec: 550.261
INFO:tensorflow:loss = 0.010122333, step = 38501 (0.182 sec)
INFO:tensorflow:global_step/sec: 535.693
INFO:tensorflow:loss = 0.013586002, step = 38601 (0.187 sec)
INFO:tensorflow:global_step/sec: 547.357
INFO:tensorflow:loss = 0.013408544, step = 38701 (0.183 sec)
INFO:tensorflow:global_step/sec: 543.493
INFO:tensorflow:loss = 0.0072285975, step = 38801 (0.184 sec)
INFO:tensorflow:global_step/sec: 524.888
INFO:tensorflow:loss = 0.018272143, step = 38901 (0.190 sec)
INFO:tensorflow:global_step/sec: 559.776
INFO:tensorflow:loss = 0.015372202, step = 39001 (0.178 sec)
INFO:tensorflow:global_step/sec: 533.097
INFO:tensorflow:loss = 0.018851195, step = 39101 (0.188 sec)
INFO:tensorflow:global_step/sec: 559.75
INFO:tensorflow:loss = 0.012927763, step = 39201 (0.179 sec)
INFO:tensorflow:global_step/sec: 536.7
INFO:tensorflow:loss = 0.010040123, step = 39301 (0.186 sec)
INFO:tensorflow:global_step/sec: 567.781
INFO:tensorflow:loss = 0.009394429, step = 39401 (0.176 sec)
INFO:tensorflow:global_step/sec: 554.859
INFO:tensorflow:loss = 0.01086911, step = 39501 (0.180 sec)
INFO:tensorflow:global_step/sec: 554.505
INFO:tensorflow:loss = 0.009075993, step = 39601 (0.180 sec)
INFO:tensorflow:global_step/sec: 534.188
INFO:tensorflow:loss = 0.008560747, step = 39701 (0.187 sec)
INFO:tensorflow:global_step/sec: 545.825
INFO:tensorflow:loss = 0.017553164, step = 39801 (0.184 sec)
INFO:tensorflow:global_step/sec: 554.441
INFO:tensorflow:loss = 0.019945253, step = 39901 (0.180 sec)
INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmpBX73lD/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-0.txt: ['0:1_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:24:10
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpBX73lD/model.ckpt-40000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't0_1_layer_dnn' dict for global step 40000: architecture/adanet/ensembles =
a
>adanet/iteration_0/ensemble_t0_1_layer_dnn/architecture/adanetB B| 1_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.03993654, average_loss/adanet/subnetwork = 0.03993654, average_loss/adanet/uniform_average_ensemble = 0.03993654, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.053605493, loss/adanet/subnetwork = 0.053605493, loss/adanet/uniform_average_ensemble = 0.053605493, prediction/mean/adanet/adanet_weighted_ensemble = 3.1580222, prediction/mean/adanet/subnetwork = 3.1580222, prediction/mean/adanet/uniform_average_ensemble = 3.1580222
INFO:tensorflow:Saving candidate 't1_1_layer_dnn' dict for global step 40000: architecture/adanet/ensembles =
o
>adanet/iteration_1/ensemble_t1_1_layer_dnn/architecture/adanetB# B | 1_layer_dnn | 1_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.04097581, average_loss/adanet/subnetwork = 0.044653624, average_loss/adanet/uniform_average_ensemble = 0.04097581, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.059345886, loss/adanet/subnetwork = 0.06800773, loss/adanet/uniform_average_ensemble = 0.059345886, prediction/mean/adanet/adanet_weighted_ensemble = 3.1586797, prediction/mean/adanet/subnetwork = 3.1593368, prediction/mean/adanet/uniform_average_ensemble = 3.1586797
INFO:tensorflow:Saving candidate 't1_2_layer_dnn' dict for global step 40000: architecture/adanet/ensembles =
o
>adanet/iteration_1/ensemble_t1_2_layer_dnn/architecture/adanetB# B | 1_layer_dnn | 2_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.034043197, average_loss/adanet/subnetwork = 0.032510567, average_loss/adanet/uniform_average_ensemble = 0.034043197, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.045813102, loss/adanet/subnetwork = 0.042689238, loss/adanet/uniform_average_ensemble = 0.045813102, prediction/mean/adanet/adanet_weighted_ensemble = 3.151645, prediction/mean/adanet/subnetwork = 3.1452672, prediction/mean/adanet/uniform_average_ensemble = 3.151645
INFO:tensorflow:Finished evaluation at 2018-12-13-19:24:13
INFO:tensorflow:Saving dict for global step 40000: average_loss = 0.034043197, average_loss/adanet/adanet_weighted_ensemble = 0.034043197, average_loss/adanet/subnetwork = 0.032510567, average_loss/adanet/uniform_average_ensemble = 0.034043197, global_step = 40000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.045813102, loss/adanet/adanet_weighted_ensemble = 0.045813102, loss/adanet/subnetwork = 0.042689238, loss/adanet/uniform_average_ensemble = 0.045813102, prediction/mean = 3.151645, prediction/mean/adanet/adanet_weighted_ensemble = 3.151645, prediction/mean/adanet/subnetwork = 3.1452672, prediction/mean/adanet/uniform_average_ensemble = 3.151645
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 40000: /tmp/tmpBX73lD/model.ckpt-40000
INFO:tensorflow:Loss for final step: 0.011342968.
INFO:tensorflow:Finished training Adanet iteration 1
INFO:tensorflow:Beginning bookkeeping phase for iteration 1
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-0.txt: ['0:1_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Starting ensemble evaluation for iteration 1
INFO:tensorflow:Restoring parameters from /tmp/tmpBX73lD/model.ckpt-40000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Encountered end of input after 14 evaluations
INFO:tensorflow:Computed ensemble metrics: adanet_loss/t0_1_layer_dnn = 0.020803, adanet_loss/t1_1_layer_dnn = 0.020815, adanet_loss/t1_2_layer_dnn = 0.014043
INFO:tensorflow:Finished ensemble evaluation for iteration 1
INFO:tensorflow:'t1_2_layer_dnn' at index 2 is moving onto the next iteration
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-1.txt: ['0:1_layer_dnn', '1:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Warm-starting from: (u'/tmp/tmpBX73lD/model.ckpt-40000',)
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: global_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Building iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Overwriting checkpoint with new graph for iteration 2 to /tmp/tmpBX73lD/model.ckpt-40000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Finished bookkeeping phase for iteration 1
INFO:tensorflow:Beginning training AdaNet iteration 2
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-1.txt: ['0:1_layer_dnn', '1:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpBX73lD/increment.ckpt-2
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmpBX73lD/model.ckpt.
INFO:tensorflow:loss = 0.017468113, step = 40001
INFO:tensorflow:global_step/sec: 146.85
INFO:tensorflow:loss = 0.018093407, step = 40101 (0.682 sec)
INFO:tensorflow:global_step/sec: 507.802
INFO:tensorflow:loss = 0.015324731, step = 40201 (0.196 sec)
INFO:tensorflow:global_step/sec: 520.817
INFO:tensorflow:loss = 0.012765523, step = 40301 (0.192 sec)
INFO:tensorflow:global_step/sec: 518.662
INFO:tensorflow:loss = 0.019902166, step = 40401 (0.193 sec)
INFO:tensorflow:global_step/sec: 520.075
INFO:tensorflow:loss = 0.009869992, step = 40501 (0.192 sec)
INFO:tensorflow:global_step/sec: 515.259
INFO:tensorflow:loss = 0.009510946, step = 40601 (0.194 sec)
INFO:tensorflow:global_step/sec: 501.361
INFO:tensorflow:loss = 0.0093863765, step = 40701 (0.199 sec)
INFO:tensorflow:global_step/sec: 483.72
INFO:tensorflow:loss = 0.013932006, step = 40801 (0.207 sec)
INFO:tensorflow:global_step/sec: 514.726
INFO:tensorflow:loss = 0.012854252, step = 40901 (0.194 sec)
INFO:tensorflow:global_step/sec: 491.079
INFO:tensorflow:loss = 0.018182788, step = 41001 (0.204 sec)
INFO:tensorflow:global_step/sec: 518.707
INFO:tensorflow:loss = 0.018021746, step = 41101 (0.192 sec)
INFO:tensorflow:global_step/sec: 482.246
INFO:tensorflow:loss = 0.017811758, step = 41201 (0.207 sec)
INFO:tensorflow:global_step/sec: 495.84
INFO:tensorflow:loss = 0.013001744, step = 41301 (0.202 sec)
INFO:tensorflow:global_step/sec: 517.741
INFO:tensorflow:loss = 0.010265199, step = 41401 (0.193 sec)
INFO:tensorflow:global_step/sec: 501.716
INFO:tensorflow:loss = 0.0071805054, step = 41501 (0.199 sec)
INFO:tensorflow:global_step/sec: 511.841
INFO:tensorflow:loss = 0.009893357, step = 41601 (0.198 sec)
INFO:tensorflow:global_step/sec: 501.918
INFO:tensorflow:loss = 0.018157754, step = 41701 (0.197 sec)
INFO:tensorflow:global_step/sec: 503.674
INFO:tensorflow:loss = 0.015058249, step = 41801 (0.198 sec)
INFO:tensorflow:global_step/sec: 512.929
INFO:tensorflow:loss = 0.013771176, step = 41901 (0.195 sec)
INFO:tensorflow:global_step/sec: 508.901
INFO:tensorflow:loss = 0.009073267, step = 42001 (0.197 sec)
INFO:tensorflow:global_step/sec: 511.58
INFO:tensorflow:loss = 0.02188246, step = 42101 (0.196 sec)
INFO:tensorflow:global_step/sec: 529.849
INFO:tensorflow:loss = 0.011396105, step = 42201 (0.193 sec)
INFO:tensorflow:global_step/sec: 486.486
INFO:tensorflow:loss = 0.011808677, step = 42301 (0.201 sec)
INFO:tensorflow:global_step/sec: 516.273
INFO:tensorflow:loss = 0.008540548, step = 42401 (0.194 sec)
INFO:tensorflow:global_step/sec: 473.073
INFO:tensorflow:loss = 0.021454208, step = 42501 (0.212 sec)
INFO:tensorflow:global_step/sec: 483.983
INFO:tensorflow:loss = 0.009850922, step = 42601 (0.207 sec)
INFO:tensorflow:global_step/sec: 519.837
INFO:tensorflow:loss = 0.015087103, step = 42701 (0.192 sec)
INFO:tensorflow:global_step/sec: 508.184
INFO:tensorflow:loss = 0.010692885, step = 42801 (0.197 sec)
INFO:tensorflow:global_step/sec: 514.221
INFO:tensorflow:loss = 0.011624115, step = 42901 (0.196 sec)
INFO:tensorflow:global_step/sec: 504.803
INFO:tensorflow:loss = 0.013021843, step = 43001 (0.197 sec)
INFO:tensorflow:global_step/sec: 493.338
INFO:tensorflow:loss = 0.008456488, step = 43101 (0.204 sec)
INFO:tensorflow:global_step/sec: 488.472
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INFO:tensorflow:loss = 0.010868691, step = 44501 (0.195 sec)
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INFO:tensorflow:loss = 0.008098583, step = 45501 (0.193 sec)
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INFO:tensorflow:global_step/sec: 512.576
INFO:tensorflow:loss = 0.011623999, step = 45701 (0.195 sec)
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INFO:tensorflow:global_step/sec: 490.79
INFO:tensorflow:loss = 0.011725863, step = 46601 (0.204 sec)
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INFO:tensorflow:global_step/sec: 511.792
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INFO:tensorflow:loss = 0.012994383, step = 47101 (0.198 sec)
INFO:tensorflow:global_step/sec: 487.588
INFO:tensorflow:loss = 0.010521249, step = 47201 (0.205 sec)
INFO:tensorflow:global_step/sec: 517.953
INFO:tensorflow:loss = 0.020745177, step = 47301 (0.193 sec)
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INFO:tensorflow:loss = 0.010111434, step = 47401 (0.191 sec)
INFO:tensorflow:global_step/sec: 525.914
INFO:tensorflow:loss = 0.006004952, step = 47501 (0.190 sec)
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INFO:tensorflow:loss = 0.012459682, step = 47601 (0.207 sec)
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INFO:tensorflow:loss = 0.016056355, step = 48301 (0.203 sec)
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INFO:tensorflow:loss = 0.009139307, step = 48401 (0.194 sec)
INFO:tensorflow:global_step/sec: 530.983
INFO:tensorflow:loss = 0.008170824, step = 48501 (0.188 sec)
INFO:tensorflow:global_step/sec: 486.034
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INFO:tensorflow:global_step/sec: 519.305
INFO:tensorflow:loss = 0.010816611, step = 48701 (0.194 sec)
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INFO:tensorflow:global_step/sec: 459.813
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INFO:tensorflow:global_step/sec: 500.894
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INFO:tensorflow:loss = 0.015327201, step = 49101 (0.200 sec)
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INFO:tensorflow:loss = 0.015008008, step = 49201 (0.201 sec)
INFO:tensorflow:global_step/sec: 510.18
INFO:tensorflow:loss = 0.0051358948, step = 49301 (0.196 sec)
INFO:tensorflow:global_step/sec: 518.969
INFO:tensorflow:loss = 0.010307699, step = 49401 (0.193 sec)
INFO:tensorflow:global_step/sec: 489.321
INFO:tensorflow:loss = 0.011590662, step = 49501 (0.204 sec)
INFO:tensorflow:global_step/sec: 482.253
INFO:tensorflow:loss = 0.012576545, step = 49601 (0.207 sec)
INFO:tensorflow:global_step/sec: 497.063
INFO:tensorflow:loss = 0.010206463, step = 49701 (0.201 sec)
INFO:tensorflow:global_step/sec: 512.379
INFO:tensorflow:loss = 0.009683546, step = 49801 (0.195 sec)
INFO:tensorflow:global_step/sec: 519.763
INFO:tensorflow:loss = 0.019511562, step = 49901 (0.193 sec)
INFO:tensorflow:global_step/sec: 482.165
INFO:tensorflow:loss = 0.014295067, step = 50001 (0.207 sec)
INFO:tensorflow:global_step/sec: 514.817
INFO:tensorflow:loss = 0.007459954, step = 50101 (0.194 sec)
INFO:tensorflow:global_step/sec: 545.111
INFO:tensorflow:loss = 0.016832381, step = 50201 (0.185 sec)
INFO:tensorflow:global_step/sec: 426.363
INFO:tensorflow:loss = 0.0125337485, step = 50301 (0.233 sec)
INFO:tensorflow:global_step/sec: 474.937
INFO:tensorflow:loss = 0.009039086, step = 50401 (0.211 sec)
INFO:tensorflow:global_step/sec: 484.811
INFO:tensorflow:loss = 0.005735507, step = 50501 (0.206 sec)
INFO:tensorflow:global_step/sec: 506.673
INFO:tensorflow:loss = 0.0173325, step = 50601 (0.197 sec)
INFO:tensorflow:global_step/sec: 504.261
INFO:tensorflow:loss = 0.008636335, step = 50701 (0.198 sec)
INFO:tensorflow:global_step/sec: 488.964
INFO:tensorflow:loss = 0.016918551, step = 50801 (0.205 sec)
INFO:tensorflow:global_step/sec: 506.984
INFO:tensorflow:loss = 0.0148557965, step = 50901 (0.197 sec)
INFO:tensorflow:global_step/sec: 522.196
INFO:tensorflow:loss = 0.0060494742, step = 51001 (0.191 sec)
INFO:tensorflow:global_step/sec: 505.14
INFO:tensorflow:loss = 0.012138335, step = 51101 (0.198 sec)
INFO:tensorflow:global_step/sec: 506.411
INFO:tensorflow:loss = 0.007853473, step = 51201 (0.198 sec)
INFO:tensorflow:global_step/sec: 509.199
INFO:tensorflow:loss = 0.014691928, step = 51301 (0.196 sec)
INFO:tensorflow:global_step/sec: 504.175
INFO:tensorflow:loss = 0.0060182875, step = 51401 (0.198 sec)
INFO:tensorflow:global_step/sec: 507.957
INFO:tensorflow:loss = 0.023528608, step = 51501 (0.197 sec)
INFO:tensorflow:global_step/sec: 491.84
INFO:tensorflow:loss = 0.008916151, step = 51601 (0.203 sec)
INFO:tensorflow:global_step/sec: 504.119
INFO:tensorflow:loss = 0.015216347, step = 51701 (0.199 sec)
INFO:tensorflow:global_step/sec: 502.922
INFO:tensorflow:loss = 0.0076746964, step = 51801 (0.199 sec)
INFO:tensorflow:global_step/sec: 522.621
INFO:tensorflow:loss = 0.010943584, step = 51901 (0.192 sec)
INFO:tensorflow:global_step/sec: 503.459
INFO:tensorflow:loss = 0.011525083, step = 52001 (0.198 sec)
INFO:tensorflow:global_step/sec: 509.396
INFO:tensorflow:loss = 0.009468526, step = 52101 (0.196 sec)
INFO:tensorflow:global_step/sec: 493.031
INFO:tensorflow:loss = 0.006191155, step = 52201 (0.203 sec)
INFO:tensorflow:global_step/sec: 528.092
INFO:tensorflow:loss = 0.009383469, step = 52301 (0.189 sec)
INFO:tensorflow:global_step/sec: 500.163
INFO:tensorflow:loss = 0.007131893, step = 52401 (0.200 sec)
INFO:tensorflow:global_step/sec: 504.917
INFO:tensorflow:loss = 0.012247307, step = 52501 (0.198 sec)
INFO:tensorflow:global_step/sec: 460.927
INFO:tensorflow:loss = 0.008317162, step = 52601 (0.217 sec)
INFO:tensorflow:global_step/sec: 511.821
INFO:tensorflow:loss = 0.012988508, step = 52701 (0.195 sec)
INFO:tensorflow:global_step/sec: 487.348
INFO:tensorflow:loss = 0.015612132, step = 52801 (0.205 sec)
INFO:tensorflow:global_step/sec: 501.502
INFO:tensorflow:loss = 0.010452649, step = 52901 (0.200 sec)
INFO:tensorflow:global_step/sec: 520.064
INFO:tensorflow:loss = 0.020963026, step = 53001 (0.192 sec)
INFO:tensorflow:global_step/sec: 473.409
INFO:tensorflow:loss = 0.012349683, step = 53101 (0.211 sec)
INFO:tensorflow:global_step/sec: 498.594
INFO:tensorflow:loss = 0.004926747, step = 53201 (0.201 sec)
INFO:tensorflow:global_step/sec: 494.555
INFO:tensorflow:loss = 0.012891041, step = 53301 (0.202 sec)
INFO:tensorflow:global_step/sec: 503.466
INFO:tensorflow:loss = 0.011349333, step = 53401 (0.198 sec)
INFO:tensorflow:global_step/sec: 504.798
INFO:tensorflow:loss = 0.013867449, step = 53501 (0.198 sec)
INFO:tensorflow:global_step/sec: 476.558
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INFO:tensorflow:global_step/sec: 504.826
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INFO:tensorflow:global_step/sec: 504.61
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INFO:tensorflow:global_step/sec: 495.624
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INFO:tensorflow:global_step/sec: 509.16
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INFO:tensorflow:global_step/sec: 517.093
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INFO:tensorflow:global_step/sec: 513.07
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INFO:tensorflow:global_step/sec: 522.813
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INFO:tensorflow:global_step/sec: 504.594
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INFO:tensorflow:global_step/sec: 514.867
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INFO:tensorflow:global_step/sec: 501.754
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INFO:tensorflow:global_step/sec: 490.304
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INFO:tensorflow:global_step/sec: 495.246
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INFO:tensorflow:global_step/sec: 488.796
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INFO:tensorflow:global_step/sec: 523.368
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INFO:tensorflow:global_step/sec: 496.843
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INFO:tensorflow:global_step/sec: 457.27
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INFO:tensorflow:global_step/sec: 447.786
INFO:tensorflow:loss = 0.013384435, step = 55501 (0.224 sec)
INFO:tensorflow:global_step/sec: 445.166
INFO:tensorflow:loss = 0.0077829137, step = 55601 (0.225 sec)
INFO:tensorflow:global_step/sec: 410.946
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INFO:tensorflow:global_step/sec: 422.09
INFO:tensorflow:loss = 0.022338329, step = 55801 (0.237 sec)
INFO:tensorflow:global_step/sec: 436.2
INFO:tensorflow:loss = 0.00855221, step = 55901 (0.229 sec)
INFO:tensorflow:global_step/sec: 416.569
INFO:tensorflow:loss = 0.011254726, step = 56001 (0.240 sec)
INFO:tensorflow:global_step/sec: 371.648
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INFO:tensorflow:global_step/sec: 404.403
INFO:tensorflow:loss = 0.0057478026, step = 56201 (0.247 sec)
INFO:tensorflow:global_step/sec: 418.531
INFO:tensorflow:loss = 0.0074014785, step = 56301 (0.239 sec)
INFO:tensorflow:global_step/sec: 406.025
INFO:tensorflow:loss = 0.012084539, step = 56401 (0.246 sec)
INFO:tensorflow:global_step/sec: 397.556
INFO:tensorflow:loss = 0.013305117, step = 56501 (0.252 sec)
INFO:tensorflow:global_step/sec: 395.23
INFO:tensorflow:loss = 0.008080397, step = 56601 (0.253 sec)
INFO:tensorflow:global_step/sec: 416.243
INFO:tensorflow:loss = 0.013839096, step = 56701 (0.240 sec)
INFO:tensorflow:global_step/sec: 423.462
INFO:tensorflow:loss = 0.010279523, step = 56801 (0.236 sec)
INFO:tensorflow:global_step/sec: 404.57
INFO:tensorflow:loss = 0.0067279865, step = 56901 (0.247 sec)
INFO:tensorflow:global_step/sec: 415.839
INFO:tensorflow:loss = 0.012175392, step = 57001 (0.240 sec)
INFO:tensorflow:global_step/sec: 396.198
INFO:tensorflow:loss = 0.018850144, step = 57101 (0.253 sec)
INFO:tensorflow:global_step/sec: 412.649
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INFO:tensorflow:global_step/sec: 408.375
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INFO:tensorflow:global_step/sec: 400.787
INFO:tensorflow:loss = 0.008404282, step = 57401 (0.249 sec)
INFO:tensorflow:global_step/sec: 400.386
INFO:tensorflow:loss = 0.011441771, step = 57501 (0.250 sec)
INFO:tensorflow:global_step/sec: 397.627
INFO:tensorflow:loss = 0.011922065, step = 57601 (0.252 sec)
INFO:tensorflow:global_step/sec: 408.916
INFO:tensorflow:loss = 0.005451596, step = 57701 (0.245 sec)
INFO:tensorflow:global_step/sec: 478.181
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INFO:tensorflow:global_step/sec: 526.316
INFO:tensorflow:loss = 0.0053738244, step = 57901 (0.194 sec)
INFO:tensorflow:global_step/sec: 494.1
INFO:tensorflow:loss = 0.013297284, step = 58001 (0.198 sec)
INFO:tensorflow:global_step/sec: 500.516
INFO:tensorflow:loss = 0.01874832, step = 58101 (0.200 sec)
INFO:tensorflow:global_step/sec: 504.269
INFO:tensorflow:loss = 0.009302543, step = 58201 (0.198 sec)
INFO:tensorflow:global_step/sec: 498.803
INFO:tensorflow:loss = 0.017056834, step = 58301 (0.200 sec)
INFO:tensorflow:global_step/sec: 508.975
INFO:tensorflow:loss = 0.013326164, step = 58401 (0.196 sec)
INFO:tensorflow:global_step/sec: 502.498
INFO:tensorflow:loss = 0.010238957, step = 58501 (0.199 sec)
INFO:tensorflow:global_step/sec: 512.4
INFO:tensorflow:loss = 0.00997032, step = 58601 (0.195 sec)
INFO:tensorflow:global_step/sec: 517.741
INFO:tensorflow:loss = 0.0059714033, step = 58701 (0.193 sec)
INFO:tensorflow:global_step/sec: 537.591
INFO:tensorflow:loss = 0.009870318, step = 58801 (0.186 sec)
INFO:tensorflow:global_step/sec: 523.782
INFO:tensorflow:loss = 0.02004344, step = 58901 (0.191 sec)
INFO:tensorflow:global_step/sec: 528.176
INFO:tensorflow:loss = 0.00976455, step = 59001 (0.190 sec)
INFO:tensorflow:global_step/sec: 521.096
INFO:tensorflow:loss = 0.011357179, step = 59101 (0.192 sec)
INFO:tensorflow:global_step/sec: 508.942
INFO:tensorflow:loss = 0.008579284, step = 59201 (0.196 sec)
INFO:tensorflow:global_step/sec: 527.872
INFO:tensorflow:loss = 0.009293977, step = 59301 (0.190 sec)
INFO:tensorflow:global_step/sec: 512.264
INFO:tensorflow:loss = 0.0066968855, step = 59401 (0.195 sec)
INFO:tensorflow:global_step/sec: 502.195
INFO:tensorflow:loss = 0.005933701, step = 59501 (0.199 sec)
INFO:tensorflow:global_step/sec: 526.382
INFO:tensorflow:loss = 0.007187545, step = 59601 (0.190 sec)
INFO:tensorflow:global_step/sec: 510.626
INFO:tensorflow:loss = 0.0061123613, step = 59701 (0.196 sec)
INFO:tensorflow:global_step/sec: 509.269
INFO:tensorflow:loss = 0.010090194, step = 59801 (0.196 sec)
INFO:tensorflow:global_step/sec: 520.288
INFO:tensorflow:loss = 0.012975221, step = 59901 (0.192 sec)
INFO:tensorflow:Saving checkpoints for 60000 into /tmp/tmpBX73lD/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-1.txt: ['0:1_layer_dnn', '1:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:25:14
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpBX73lD/model.ckpt-60000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't1_2_layer_dnn' dict for global step 60000: architecture/adanet/ensembles =
o
>adanet/iteration_1/ensemble_t1_2_layer_dnn/architecture/adanetB# B | 1_layer_dnn | 2_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.034043197, average_loss/adanet/subnetwork = 0.032510567, average_loss/adanet/uniform_average_ensemble = 0.034043197, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.045813102, loss/adanet/subnetwork = 0.042689238, loss/adanet/uniform_average_ensemble = 0.045813102, prediction/mean/adanet/adanet_weighted_ensemble = 3.151645, prediction/mean/adanet/subnetwork = 3.1452672, prediction/mean/adanet/uniform_average_ensemble = 3.151645
INFO:tensorflow:Saving candidate 't2_2_layer_dnn' dict for global step 60000: architecture/adanet/ensembles =
}
>adanet/iteration_2/ensemble_t2_2_layer_dnn/architecture/adanetB1 B+| 1_layer_dnn | 2_layer_dnn | 2_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.031925783, average_loss/adanet/subnetwork = 0.032713592, average_loss/adanet/uniform_average_ensemble = 0.031925786, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.043711387, loss/adanet/subnetwork = 0.043944843, loss/adanet/uniform_average_ensemble = 0.043711387, prediction/mean/adanet/adanet_weighted_ensemble = 3.1529949, prediction/mean/adanet/subnetwork = 3.1556947, prediction/mean/adanet/uniform_average_ensemble = 3.1529949
INFO:tensorflow:Saving candidate 't2_3_layer_dnn' dict for global step 60000: architecture/adanet/ensembles =
}
>adanet/iteration_2/ensemble_t2_3_layer_dnn/architecture/adanetB1 B+| 1_layer_dnn | 2_layer_dnn | 3_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.032317463, average_loss/adanet/subnetwork = 0.032910354, average_loss/adanet/uniform_average_ensemble = 0.03231746, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.043847356, loss/adanet/subnetwork = 0.043785788, loss/adanet/uniform_average_ensemble = 0.04384736, prediction/mean/adanet/adanet_weighted_ensemble = 3.1457782, prediction/mean/adanet/subnetwork = 3.134045, prediction/mean/adanet/uniform_average_ensemble = 3.1457782
INFO:tensorflow:Finished evaluation at 2018-12-13-19:25:17
INFO:tensorflow:Saving dict for global step 60000: average_loss = 0.032317463, average_loss/adanet/adanet_weighted_ensemble = 0.032317463, average_loss/adanet/subnetwork = 0.032910354, average_loss/adanet/uniform_average_ensemble = 0.03231746, global_step = 60000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.043847356, loss/adanet/adanet_weighted_ensemble = 0.043847356, loss/adanet/subnetwork = 0.043785788, loss/adanet/uniform_average_ensemble = 0.04384736, prediction/mean = 3.1457782, prediction/mean/adanet/adanet_weighted_ensemble = 3.1457782, prediction/mean/adanet/subnetwork = 3.134045, prediction/mean/adanet/uniform_average_ensemble = 3.1457782
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 60000: /tmp/tmpBX73lD/model.ckpt-60000
INFO:tensorflow:Loss for final step: 0.006897436.
INFO:tensorflow:Finished training Adanet iteration 2
INFO:tensorflow:Beginning bookkeeping phase for iteration 2
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-1.txt: ['0:1_layer_dnn', '1:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Starting ensemble evaluation for iteration 2
INFO:tensorflow:Restoring parameters from /tmp/tmpBX73lD/model.ckpt-60000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Encountered end of input after 14 evaluations
INFO:tensorflow:Computed ensemble metrics: adanet_loss/t1_2_layer_dnn = 0.014043, adanet_loss/t2_2_layer_dnn = 0.012769, adanet_loss/t2_3_layer_dnn = 0.011257
INFO:tensorflow:Finished ensemble evaluation for iteration 2
INFO:tensorflow:'t2_3_layer_dnn' at index 2 is moving onto the next iteration
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-2.txt: ['0:1_layer_dnn', '1:2_layer_dnn', '2:3_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Warm-starting from: (u'/tmp/tmpBX73lD/model.ckpt-60000',)
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_3/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_3_layer_dnn/adanet/iteration_2/candidate_t2_3_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_3_layer_dnn/adanet/iteration_2/candidate_t2_3_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: global_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_3_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_2_layer_dnn/adanet/iteration_2/candidate_t1_2_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_3/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_2_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_2_layer_dnn/adanet/iteration_2/candidate_t1_2_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Building iteration 3
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Overwriting checkpoint with new graph for iteration 3 to /tmp/tmpBX73lD/model.ckpt-60000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Finished bookkeeping phase for iteration 2
INFO:tensorflow:Beginning training AdaNet iteration 3
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-2.txt: ['0:1_layer_dnn', '1:2_layer_dnn', '2:3_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Building iteration 3
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpBX73lD/increment.ckpt-3
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 60000 into /tmp/tmpBX73lD/model.ckpt.
INFO:tensorflow:loss = 0.014349232, step = 60001
INFO:tensorflow:global_step/sec: 122.827
INFO:tensorflow:loss = 0.014175814, step = 60101 (0.815 sec)
INFO:tensorflow:global_step/sec: 500.977
INFO:tensorflow:loss = 0.012988036, step = 60201 (0.200 sec)
INFO:tensorflow:global_step/sec: 458.789
INFO:tensorflow:loss = 0.010762272, step = 60301 (0.217 sec)
INFO:tensorflow:global_step/sec: 472.133
INFO:tensorflow:loss = 0.015884051, step = 60401 (0.212 sec)
INFO:tensorflow:global_step/sec: 479.759
INFO:tensorflow:loss = 0.007582067, step = 60501 (0.209 sec)
INFO:tensorflow:global_step/sec: 500.406
INFO:tensorflow:loss = 0.00642565, step = 60601 (0.200 sec)
INFO:tensorflow:global_step/sec: 492.545
INFO:tensorflow:loss = 0.007641523, step = 60701 (0.203 sec)
INFO:tensorflow:global_step/sec: 475.946
INFO:tensorflow:loss = 0.011311023, step = 60801 (0.210 sec)
INFO:tensorflow:global_step/sec: 433.981
INFO:tensorflow:loss = 0.012714136, step = 60901 (0.231 sec)
INFO:tensorflow:global_step/sec: 488.529
INFO:tensorflow:loss = 0.015741285, step = 61001 (0.205 sec)
INFO:tensorflow:global_step/sec: 465.532
INFO:tensorflow:loss = 0.014458665, step = 61101 (0.215 sec)
INFO:tensorflow:global_step/sec: 463.407
INFO:tensorflow:loss = 0.01391992, step = 61201 (0.216 sec)
INFO:tensorflow:global_step/sec: 497.08
INFO:tensorflow:loss = 0.007650324, step = 61301 (0.201 sec)
INFO:tensorflow:global_step/sec: 475.615
INFO:tensorflow:loss = 0.008895887, step = 61401 (0.210 sec)
INFO:tensorflow:global_step/sec: 490.354
INFO:tensorflow:loss = 0.006260482, step = 61501 (0.204 sec)
INFO:tensorflow:global_step/sec: 458.579
INFO:tensorflow:loss = 0.007975491, step = 61601 (0.218 sec)
INFO:tensorflow:global_step/sec: 487.035
INFO:tensorflow:loss = 0.01617087, step = 61701 (0.205 sec)
INFO:tensorflow:global_step/sec: 488.785
INFO:tensorflow:loss = 0.011673059, step = 61801 (0.205 sec)
INFO:tensorflow:global_step/sec: 489.61
INFO:tensorflow:loss = 0.010906876, step = 61901 (0.204 sec)
INFO:tensorflow:global_step/sec: 484.318
INFO:tensorflow:loss = 0.0073513957, step = 62001 (0.206 sec)
INFO:tensorflow:global_step/sec: 454.578
INFO:tensorflow:loss = 0.017136538, step = 62101 (0.220 sec)
INFO:tensorflow:global_step/sec: 483.011
INFO:tensorflow:loss = 0.009429395, step = 62201 (0.207 sec)
INFO:tensorflow:global_step/sec: 481.891
INFO:tensorflow:loss = 0.009464838, step = 62301 (0.208 sec)
INFO:tensorflow:global_step/sec: 489.893
INFO:tensorflow:loss = 0.0076065753, step = 62401 (0.204 sec)
INFO:tensorflow:global_step/sec: 482.309
INFO:tensorflow:loss = 0.016479883, step = 62501 (0.207 sec)
INFO:tensorflow:global_step/sec: 466.007
INFO:tensorflow:loss = 0.0073400293, step = 62601 (0.215 sec)
INFO:tensorflow:global_step/sec: 501.452
INFO:tensorflow:loss = 0.012695417, step = 62701 (0.199 sec)
INFO:tensorflow:global_step/sec: 491.884
INFO:tensorflow:loss = 0.0101601835, step = 62801 (0.203 sec)
INFO:tensorflow:global_step/sec: 483.8
INFO:tensorflow:loss = 0.010170883, step = 62901 (0.207 sec)
INFO:tensorflow:global_step/sec: 481.621
INFO:tensorflow:loss = 0.010603026, step = 63001 (0.208 sec)
INFO:tensorflow:global_step/sec: 473.655
INFO:tensorflow:loss = 0.007322383, step = 63101 (0.211 sec)
INFO:tensorflow:global_step/sec: 492.708
INFO:tensorflow:loss = 0.0148867555, step = 63201 (0.203 sec)
INFO:tensorflow:global_step/sec: 473.361
INFO:tensorflow:loss = 0.017221898, step = 63301 (0.212 sec)
INFO:tensorflow:global_step/sec: 498.122
INFO:tensorflow:loss = 0.0131410295, step = 63401 (0.201 sec)
INFO:tensorflow:global_step/sec: 477.76
INFO:tensorflow:loss = 0.017954867, step = 63501 (0.209 sec)
INFO:tensorflow:global_step/sec: 496.771
INFO:tensorflow:loss = 0.0077373376, step = 63601 (0.201 sec)
INFO:tensorflow:global_step/sec: 486.384
INFO:tensorflow:loss = 0.0084258355, step = 63701 (0.206 sec)
INFO:tensorflow:global_step/sec: 477.737
INFO:tensorflow:loss = 0.009624679, step = 63801 (0.209 sec)
INFO:tensorflow:global_step/sec: 487.211
INFO:tensorflow:loss = 0.006020199, step = 63901 (0.205 sec)
INFO:tensorflow:global_step/sec: 473.151
INFO:tensorflow:loss = 0.011776499, step = 64001 (0.213 sec)
INFO:tensorflow:global_step/sec: 458.794
INFO:tensorflow:loss = 0.010384669, step = 64101 (0.216 sec)
INFO:tensorflow:global_step/sec: 491.449
INFO:tensorflow:loss = 0.008962873, step = 64201 (0.204 sec)
INFO:tensorflow:global_step/sec: 498.251
INFO:tensorflow:loss = 0.0046904236, step = 64301 (0.201 sec)
INFO:tensorflow:global_step/sec: 408.564
INFO:tensorflow:loss = 0.009667809, step = 64401 (0.245 sec)
INFO:tensorflow:global_step/sec: 450.197
INFO:tensorflow:loss = 0.009793492, step = 64501 (0.222 sec)
INFO:tensorflow:global_step/sec: 465.476
INFO:tensorflow:loss = 0.012524443, step = 64601 (0.215 sec)
INFO:tensorflow:global_step/sec: 482
INFO:tensorflow:loss = 0.0068974327, step = 64701 (0.207 sec)
INFO:tensorflow:global_step/sec: 483.91
INFO:tensorflow:loss = 0.008452025, step = 64801 (0.207 sec)
INFO:tensorflow:global_step/sec: 479.242
INFO:tensorflow:loss = 0.012261448, step = 64901 (0.209 sec)
INFO:tensorflow:global_step/sec: 467.681
INFO:tensorflow:loss = 0.0065639215, step = 65001 (0.214 sec)
INFO:tensorflow:global_step/sec: 487.016
INFO:tensorflow:loss = 0.0072128996, step = 65101 (0.205 sec)
INFO:tensorflow:global_step/sec: 488.635
INFO:tensorflow:loss = 0.008089228, step = 65201 (0.208 sec)
INFO:tensorflow:global_step/sec: 475.862
INFO:tensorflow:loss = 0.015109103, step = 65301 (0.214 sec)
INFO:tensorflow:global_step/sec: 451.859
INFO:tensorflow:loss = 0.010164745, step = 65401 (0.215 sec)
INFO:tensorflow:global_step/sec: 475.477
INFO:tensorflow:loss = 0.007488979, step = 65501 (0.210 sec)
INFO:tensorflow:global_step/sec: 486.888
INFO:tensorflow:loss = 0.01756696, step = 65601 (0.206 sec)
INFO:tensorflow:global_step/sec: 403.704
INFO:tensorflow:loss = 0.009442094, step = 65701 (0.247 sec)
INFO:tensorflow:global_step/sec: 481.668
INFO:tensorflow:loss = 0.009446172, step = 65801 (0.208 sec)
INFO:tensorflow:global_step/sec: 479.653
INFO:tensorflow:loss = 0.00790675, step = 65901 (0.208 sec)
INFO:tensorflow:global_step/sec: 456.184
INFO:tensorflow:loss = 0.014462812, step = 66001 (0.219 sec)
INFO:tensorflow:global_step/sec: 479.519
INFO:tensorflow:loss = 0.014726914, step = 66101 (0.208 sec)
INFO:tensorflow:global_step/sec: 469.782
INFO:tensorflow:loss = 0.013557127, step = 66201 (0.213 sec)
INFO:tensorflow:global_step/sec: 469.404
INFO:tensorflow:loss = 0.0056417743, step = 66301 (0.213 sec)
INFO:tensorflow:global_step/sec: 456.061
INFO:tensorflow:loss = 0.0076914574, step = 66401 (0.219 sec)
INFO:tensorflow:global_step/sec: 475.586
INFO:tensorflow:loss = 0.020085715, step = 66501 (0.210 sec)
INFO:tensorflow:global_step/sec: 468.538
INFO:tensorflow:loss = 0.014946561, step = 66601 (0.214 sec)
INFO:tensorflow:global_step/sec: 473.561
INFO:tensorflow:loss = 0.010401423, step = 66701 (0.211 sec)
INFO:tensorflow:global_step/sec: 482.427
INFO:tensorflow:loss = 0.0070528616, step = 66801 (0.207 sec)
INFO:tensorflow:global_step/sec: 496.672
INFO:tensorflow:loss = 0.007295311, step = 66901 (0.201 sec)
INFO:tensorflow:global_step/sec: 443.892
INFO:tensorflow:loss = 0.0067928964, step = 67001 (0.225 sec)
INFO:tensorflow:global_step/sec: 452.436
INFO:tensorflow:loss = 0.010201834, step = 67101 (0.222 sec)
INFO:tensorflow:global_step/sec: 467.12
INFO:tensorflow:loss = 0.009510251, step = 67201 (0.214 sec)
INFO:tensorflow:global_step/sec: 467.978
INFO:tensorflow:loss = 0.013937269, step = 67301 (0.214 sec)
INFO:tensorflow:global_step/sec: 465.439
INFO:tensorflow:loss = 0.010768862, step = 67401 (0.215 sec)
INFO:tensorflow:global_step/sec: 476.333
INFO:tensorflow:loss = 0.00456707, step = 67501 (0.210 sec)
INFO:tensorflow:global_step/sec: 489.579
INFO:tensorflow:loss = 0.008822214, step = 67601 (0.204 sec)
INFO:tensorflow:global_step/sec: 491.906
INFO:tensorflow:loss = 0.014752589, step = 67701 (0.203 sec)
INFO:tensorflow:global_step/sec: 488.844
INFO:tensorflow:loss = 0.01246707, step = 67801 (0.205 sec)
INFO:tensorflow:global_step/sec: 452.931
INFO:tensorflow:loss = 0.0072872615, step = 67901 (0.220 sec)
INFO:tensorflow:global_step/sec: 474.793
INFO:tensorflow:loss = 0.01730576, step = 68001 (0.211 sec)
INFO:tensorflow:global_step/sec: 486.398
INFO:tensorflow:loss = 0.015187411, step = 68101 (0.206 sec)
INFO:tensorflow:global_step/sec: 450.203
INFO:tensorflow:loss = 0.013181204, step = 68201 (0.222 sec)
INFO:tensorflow:global_step/sec: 472.021
INFO:tensorflow:loss = 0.014625701, step = 68301 (0.212 sec)
INFO:tensorflow:global_step/sec: 451.41
INFO:tensorflow:loss = 0.009088153, step = 68401 (0.221 sec)
INFO:tensorflow:global_step/sec: 470.985
INFO:tensorflow:loss = 0.006735581, step = 68501 (0.212 sec)
INFO:tensorflow:global_step/sec: 482.95
INFO:tensorflow:loss = 0.011414693, step = 68601 (0.207 sec)
INFO:tensorflow:global_step/sec: 478.829
INFO:tensorflow:loss = 0.008719599, step = 68701 (0.209 sec)
INFO:tensorflow:global_step/sec: 488.1
INFO:tensorflow:loss = 0.016607951, step = 68801 (0.205 sec)
INFO:tensorflow:global_step/sec: 487.213
INFO:tensorflow:loss = 0.021428429, step = 68901 (0.205 sec)
INFO:tensorflow:global_step/sec: 495.766
INFO:tensorflow:loss = 0.018561859, step = 69001 (0.202 sec)
INFO:tensorflow:global_step/sec: 473.972
INFO:tensorflow:loss = 0.012570044, step = 69101 (0.211 sec)
INFO:tensorflow:global_step/sec: 492.138
INFO:tensorflow:loss = 0.0117163975, step = 69201 (0.204 sec)
INFO:tensorflow:global_step/sec: 471.138
INFO:tensorflow:loss = 0.010117748, step = 69301 (0.212 sec)
INFO:tensorflow:global_step/sec: 486.218
INFO:tensorflow:loss = 0.009999806, step = 69401 (0.206 sec)
INFO:tensorflow:global_step/sec: 503.776
INFO:tensorflow:loss = 0.008249035, step = 69501 (0.199 sec)
INFO:tensorflow:global_step/sec: 499.658
INFO:tensorflow:loss = 0.013595214, step = 69601 (0.203 sec)
INFO:tensorflow:global_step/sec: 466.553
INFO:tensorflow:loss = 0.006964251, step = 69701 (0.211 sec)
INFO:tensorflow:global_step/sec: 469.021
INFO:tensorflow:loss = 0.008460038, step = 69801 (0.216 sec)
INFO:tensorflow:global_step/sec: 463.815
INFO:tensorflow:loss = 0.016430806, step = 69901 (0.213 sec)
INFO:tensorflow:global_step/sec: 481.763
INFO:tensorflow:loss = 0.013513658, step = 70001 (0.207 sec)
INFO:tensorflow:global_step/sec: 487.85
INFO:tensorflow:loss = 0.00617994, step = 70101 (0.205 sec)
INFO:tensorflow:global_step/sec: 461.248
INFO:tensorflow:loss = 0.010562475, step = 70201 (0.217 sec)
INFO:tensorflow:global_step/sec: 487.434
INFO:tensorflow:loss = 0.009897685, step = 70301 (0.206 sec)
INFO:tensorflow:global_step/sec: 449.651
INFO:tensorflow:loss = 0.00854541, step = 70401 (0.222 sec)
INFO:tensorflow:global_step/sec: 469.629
INFO:tensorflow:loss = 0.0049584727, step = 70501 (0.213 sec)
INFO:tensorflow:global_step/sec: 463.208
INFO:tensorflow:loss = 0.011869925, step = 70601 (0.216 sec)
INFO:tensorflow:global_step/sec: 485.062
INFO:tensorflow:loss = 0.008131639, step = 70701 (0.206 sec)
INFO:tensorflow:global_step/sec: 484.325
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INFO:tensorflow:global_step/sec: 482.378
INFO:tensorflow:loss = 0.012321655, step = 70901 (0.203 sec)
INFO:tensorflow:global_step/sec: 496.473
INFO:tensorflow:loss = 0.006670419, step = 71001 (0.203 sec)
INFO:tensorflow:global_step/sec: 483.753
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INFO:tensorflow:global_step/sec: 493.323
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INFO:tensorflow:global_step/sec: 487.18
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INFO:tensorflow:global_step/sec: 497.285
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INFO:tensorflow:global_step/sec: 477.619
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INFO:tensorflow:global_step/sec: 453.896
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INFO:tensorflow:global_step/sec: 504.33
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INFO:tensorflow:global_step/sec: 495.594
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INFO:tensorflow:global_step/sec: 468.134
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INFO:tensorflow:global_step/sec: 472.066
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INFO:tensorflow:global_step/sec: 480.024
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INFO:tensorflow:global_step/sec: 490.458
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INFO:tensorflow:global_step/sec: 489.975
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INFO:tensorflow:global_step/sec: 459.204
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INFO:tensorflow:global_step/sec: 469.642
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INFO:tensorflow:global_step/sec: 489.017
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INFO:tensorflow:global_step/sec: 500.769
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INFO:tensorflow:global_step/sec: 480.188
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INFO:tensorflow:global_step/sec: 492.922
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INFO:tensorflow:global_step/sec: 467.233
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INFO:tensorflow:global_step/sec: 502.381
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INFO:tensorflow:global_step/sec: 493.94
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INFO:tensorflow:global_step/sec: 501.436
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INFO:tensorflow:global_step/sec: 483.978
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INFO:tensorflow:global_step/sec: 498.509
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INFO:tensorflow:global_step/sec: 484.013
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INFO:tensorflow:global_step/sec: 442.267
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INFO:tensorflow:global_step/sec: 454.103
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INFO:tensorflow:global_step/sec: 478.407
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INFO:tensorflow:global_step/sec: 493.538
INFO:tensorflow:loss = 0.006081101, step = 76201 (0.203 sec)
INFO:tensorflow:global_step/sec: 478.471
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INFO:tensorflow:global_step/sec: 468.799
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INFO:tensorflow:global_step/sec: 476.706
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INFO:tensorflow:global_step/sec: 475.473
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INFO:tensorflow:global_step/sec: 452.751
INFO:tensorflow:loss = 0.007660943, step = 77201 (0.221 sec)
INFO:tensorflow:global_step/sec: 491.147
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INFO:tensorflow:global_step/sec: 477.571
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INFO:tensorflow:global_step/sec: 482.297
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INFO:tensorflow:global_step/sec: 459.238
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INFO:tensorflow:global_step/sec: 454.663
INFO:tensorflow:loss = 0.005551029, step = 77701 (0.220 sec)
INFO:tensorflow:global_step/sec: 460.534
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INFO:tensorflow:global_step/sec: 467.539
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INFO:tensorflow:global_step/sec: 458.735
INFO:tensorflow:loss = 0.011437742, step = 78001 (0.218 sec)
INFO:tensorflow:global_step/sec: 489.375
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INFO:tensorflow:global_step/sec: 488.332
INFO:tensorflow:loss = 0.009729374, step = 78201 (0.205 sec)
INFO:tensorflow:global_step/sec: 467.895
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INFO:tensorflow:global_step/sec: 490.918
INFO:tensorflow:loss = 0.011398161, step = 78401 (0.204 sec)
INFO:tensorflow:global_step/sec: 482.8
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INFO:tensorflow:global_step/sec: 484.266
INFO:tensorflow:loss = 0.010301631, step = 78601 (0.206 sec)
INFO:tensorflow:global_step/sec: 481.498
INFO:tensorflow:loss = 0.0049466062, step = 78701 (0.208 sec)
INFO:tensorflow:global_step/sec: 447.063
INFO:tensorflow:loss = 0.0072362237, step = 78801 (0.224 sec)
INFO:tensorflow:global_step/sec: 472.212
INFO:tensorflow:loss = 0.013689649, step = 78901 (0.212 sec)
INFO:tensorflow:global_step/sec: 475.971
INFO:tensorflow:loss = 0.010056267, step = 79001 (0.210 sec)
INFO:tensorflow:global_step/sec: 482.049
INFO:tensorflow:loss = 0.007534829, step = 79101 (0.207 sec)
INFO:tensorflow:global_step/sec: 469.55
INFO:tensorflow:loss = 0.010161445, step = 79201 (0.213 sec)
INFO:tensorflow:global_step/sec: 480.4
INFO:tensorflow:loss = 0.0071275346, step = 79301 (0.208 sec)
INFO:tensorflow:global_step/sec: 465.213
INFO:tensorflow:loss = 0.006760837, step = 79401 (0.215 sec)
INFO:tensorflow:global_step/sec: 456.544
INFO:tensorflow:loss = 0.005613528, step = 79501 (0.219 sec)
INFO:tensorflow:global_step/sec: 471.007
INFO:tensorflow:loss = 0.008591261, step = 79601 (0.213 sec)
INFO:tensorflow:global_step/sec: 468.477
INFO:tensorflow:loss = 0.0063990387, step = 79701 (0.213 sec)
INFO:tensorflow:global_step/sec: 447.661
INFO:tensorflow:loss = 0.010303696, step = 79801 (0.224 sec)
INFO:tensorflow:global_step/sec: 451.606
INFO:tensorflow:loss = 0.015051338, step = 79901 (0.221 sec)
INFO:tensorflow:Saving checkpoints for 80000 into /tmp/tmpBX73lD/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-2.txt: ['0:1_layer_dnn', '1:2_layer_dnn', '2:3_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Building iteration 3
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:26:26
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpBX73lD/model.ckpt-80000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't2_3_layer_dnn' dict for global step 80000: architecture/adanet/ensembles =
}
>adanet/iteration_2/ensemble_t2_3_layer_dnn/architecture/adanetB1 B+| 1_layer_dnn | 2_layer_dnn | 3_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.032317463, average_loss/adanet/subnetwork = 0.032910354, average_loss/adanet/uniform_average_ensemble = 0.03231746, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.043847356, loss/adanet/subnetwork = 0.043785788, loss/adanet/uniform_average_ensemble = 0.04384736, prediction/mean/adanet/adanet_weighted_ensemble = 3.1457782, prediction/mean/adanet/subnetwork = 3.134045, prediction/mean/adanet/uniform_average_ensemble = 3.1457782
INFO:tensorflow:Saving candidate 't3_3_layer_dnn' dict for global step 80000: architecture/adanet/ensembles =
�
>adanet/iteration_3/ensemble_t3_3_layer_dnn/architecture/adanetB? B9| 1_layer_dnn | 2_layer_dnn | 3_layer_dnn | 3_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.03251077, average_loss/adanet/subnetwork = 0.03740776, average_loss/adanet/uniform_average_ensemble = 0.032510772, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.045306467, loss/adanet/subnetwork = 0.055050053, loss/adanet/uniform_average_ensemble = 0.04530649, prediction/mean/adanet/adanet_weighted_ensemble = 3.1480103, prediction/mean/adanet/subnetwork = 3.1547055, prediction/mean/adanet/uniform_average_ensemble = 3.1480103
INFO:tensorflow:Saving candidate 't3_4_layer_dnn' dict for global step 80000: architecture/adanet/ensembles =
�
>adanet/iteration_3/ensemble_t3_4_layer_dnn/architecture/adanetB? B9| 1_layer_dnn | 2_layer_dnn | 3_layer_dnn | 4_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.031587753, average_loss/adanet/subnetwork = 0.03348904, average_loss/adanet/uniform_average_ensemble = 0.03158775, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.04207115, loss/adanet/subnetwork = 0.041055106, loss/adanet/uniform_average_ensemble = 0.04207114, prediction/mean/adanet/adanet_weighted_ensemble = 3.1415138, prediction/mean/adanet/subnetwork = 3.1287208, prediction/mean/adanet/uniform_average_ensemble = 3.1415138
INFO:tensorflow:Finished evaluation at 2018-12-13-19:26:31
INFO:tensorflow:Saving dict for global step 80000: average_loss = 0.031587753, average_loss/adanet/adanet_weighted_ensemble = 0.031587753, average_loss/adanet/subnetwork = 0.03348904, average_loss/adanet/uniform_average_ensemble = 0.03158775, global_step = 80000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.04207115, loss/adanet/adanet_weighted_ensemble = 0.04207115, loss/adanet/subnetwork = 0.041055106, loss/adanet/uniform_average_ensemble = 0.04207114, prediction/mean = 3.1415138, prediction/mean/adanet/adanet_weighted_ensemble = 3.1415138, prediction/mean/adanet/subnetwork = 3.1287208, prediction/mean/adanet/uniform_average_ensemble = 3.1415138
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 80000: /tmp/tmpBX73lD/model.ckpt-80000
INFO:tensorflow:Loss for final step: 0.0061327047.
INFO:tensorflow:Finished training Adanet iteration 3
INFO:tensorflow:Beginning bookkeeping phase for iteration 3
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-2.txt: ['0:1_layer_dnn', '1:2_layer_dnn', '2:3_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Building iteration 3
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Starting ensemble evaluation for iteration 3
INFO:tensorflow:Restoring parameters from /tmp/tmpBX73lD/model.ckpt-80000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Encountered end of input after 14 evaluations
INFO:tensorflow:Computed ensemble metrics: adanet_loss/t2_3_layer_dnn = 0.011257, adanet_loss/t3_3_layer_dnn = 0.011323, adanet_loss/t3_4_layer_dnn = 0.009588
INFO:tensorflow:Finished ensemble evaluation for iteration 3
INFO:tensorflow:'t3_4_layer_dnn' at index 2 is moving onto the next iteration
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-3.txt: ['0:1_layer_dnn', '1:2_layer_dnn', '2:3_layer_dnn', '3:4_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Rebuilding iteration 3
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Warm-starting from: (u'/tmp/tmpBX73lD/model.ckpt-80000',)
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_3/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_2/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_3/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_3_layer_dnn/adanet/iteration_2/candidate_t2_3_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_3_layer_dnn/adanet/iteration_2/candidate_t2_3_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t3_4_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t3_4_layer_dnn/adanet/iteration_3/candidate_t3_4_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_4/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: global_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t3_4_layer_dnn/adanet/iteration_3/candidate_t3_4_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_3_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_4/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_3/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t2_3_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_2_layer_dnn/adanet/iteration_2/candidate_t1_2_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t2_3_layer_dnn/adanet/iteration_3/candidate_t2_3_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_3/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t2_3_layer_dnn/adanet/iteration_3/candidate_t2_3_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_2_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_2_layer_dnn/adanet/iteration_2/candidate_t1_2_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Building iteration 4
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Building subnetwork '5_layer_dnn'
INFO:tensorflow:Overwriting checkpoint with new graph for iteration 4 to /tmp/tmpBX73lD/model.ckpt-80000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Finished bookkeeping phase for iteration 3
INFO:tensorflow:Beginning training AdaNet iteration 4
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-3.txt: ['0:1_layer_dnn', '1:2_layer_dnn', '2:3_layer_dnn', '3:4_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Rebuilding iteration 3
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Building iteration 4
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Building subnetwork '5_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpBX73lD/increment.ckpt-4
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 80000 into /tmp/tmpBX73lD/model.ckpt.
INFO:tensorflow:loss = 0.01076022, step = 80001
INFO:tensorflow:global_step/sec: 106.247
INFO:tensorflow:loss = 0.011567029, step = 80101 (0.942 sec)
INFO:tensorflow:global_step/sec: 433.175
INFO:tensorflow:loss = 0.0105704125, step = 80201 (0.230 sec)
INFO:tensorflow:global_step/sec: 430.274
INFO:tensorflow:loss = 0.009286735, step = 80301 (0.233 sec)
INFO:tensorflow:global_step/sec: 428.392
INFO:tensorflow:loss = 0.013190662, step = 80401 (0.233 sec)
INFO:tensorflow:global_step/sec: 450.708
INFO:tensorflow:loss = 0.0066475794, step = 80501 (0.222 sec)
INFO:tensorflow:global_step/sec: 468.729
INFO:tensorflow:loss = 0.0055183023, step = 80601 (0.214 sec)
INFO:tensorflow:global_step/sec: 435.68
INFO:tensorflow:loss = 0.0071699126, step = 80701 (0.230 sec)
INFO:tensorflow:global_step/sec: 436.822
INFO:tensorflow:loss = 0.009832906, step = 80801 (0.229 sec)
INFO:tensorflow:global_step/sec: 443.821
INFO:tensorflow:loss = 0.011850201, step = 80901 (0.225 sec)
INFO:tensorflow:global_step/sec: 428.919
INFO:tensorflow:loss = 0.013372295, step = 81001 (0.233 sec)
INFO:tensorflow:global_step/sec: 456.289
INFO:tensorflow:loss = 0.0111810835, step = 81101 (0.219 sec)
INFO:tensorflow:global_step/sec: 438.926
INFO:tensorflow:loss = 0.011970724, step = 81201 (0.228 sec)
INFO:tensorflow:global_step/sec: 465.977
INFO:tensorflow:loss = 0.0065958686, step = 81301 (0.215 sec)
INFO:tensorflow:global_step/sec: 433.362
INFO:tensorflow:loss = 0.007469721, step = 81401 (0.231 sec)
INFO:tensorflow:global_step/sec: 472.037
INFO:tensorflow:loss = 0.005948479, step = 81501 (0.217 sec)
INFO:tensorflow:global_step/sec: 445.149
INFO:tensorflow:loss = 0.0074148737, step = 81601 (0.220 sec)
INFO:tensorflow:global_step/sec: 459.35
INFO:tensorflow:loss = 0.012823062, step = 81701 (0.217 sec)
INFO:tensorflow:global_step/sec: 442.603
INFO:tensorflow:loss = 0.009658838, step = 81801 (0.226 sec)
INFO:tensorflow:global_step/sec: 447.389
INFO:tensorflow:loss = 0.0110181635, step = 81901 (0.224 sec)
INFO:tensorflow:global_step/sec: 452.133
INFO:tensorflow:loss = 0.0074777408, step = 82001 (0.221 sec)
INFO:tensorflow:global_step/sec: 446.444
INFO:tensorflow:loss = 0.0150109315, step = 82101 (0.224 sec)
INFO:tensorflow:global_step/sec: 456.667
INFO:tensorflow:loss = 0.008159091, step = 82201 (0.219 sec)
INFO:tensorflow:global_step/sec: 453.416
INFO:tensorflow:loss = 0.007832337, step = 82301 (0.221 sec)
INFO:tensorflow:global_step/sec: 450.367
INFO:tensorflow:loss = 0.006455669, step = 82401 (0.222 sec)
INFO:tensorflow:global_step/sec: 447.561
INFO:tensorflow:loss = 0.01277595, step = 82501 (0.223 sec)
INFO:tensorflow:global_step/sec: 452.52
INFO:tensorflow:loss = 0.006126184, step = 82601 (0.221 sec)
INFO:tensorflow:global_step/sec: 445.669
INFO:tensorflow:loss = 0.01073208, step = 82701 (0.230 sec)
INFO:tensorflow:global_step/sec: 441.269
INFO:tensorflow:loss = 0.009560438, step = 82801 (0.221 sec)
INFO:tensorflow:global_step/sec: 460.764
INFO:tensorflow:loss = 0.008874605, step = 82901 (0.217 sec)
INFO:tensorflow:global_step/sec: 457.411
INFO:tensorflow:loss = 0.008558904, step = 83001 (0.219 sec)
INFO:tensorflow:global_step/sec: 456.777
INFO:tensorflow:loss = 0.0047848914, step = 83101 (0.219 sec)
INFO:tensorflow:global_step/sec: 456.383
INFO:tensorflow:loss = 0.013862016, step = 83201 (0.219 sec)
INFO:tensorflow:global_step/sec: 439.964
INFO:tensorflow:loss = 0.014882583, step = 83301 (0.227 sec)
INFO:tensorflow:global_step/sec: 452.46
INFO:tensorflow:loss = 0.011824507, step = 83401 (0.221 sec)
INFO:tensorflow:global_step/sec: 474.861
INFO:tensorflow:loss = 0.014059515, step = 83501 (0.210 sec)
INFO:tensorflow:global_step/sec: 451.095
INFO:tensorflow:loss = 0.0069136624, step = 83601 (0.222 sec)
INFO:tensorflow:global_step/sec: 435.603
INFO:tensorflow:loss = 0.008130037, step = 83701 (0.229 sec)
INFO:tensorflow:global_step/sec: 451.657
INFO:tensorflow:loss = 0.0081698615, step = 83801 (0.221 sec)
INFO:tensorflow:global_step/sec: 466.033
INFO:tensorflow:loss = 0.005401345, step = 83901 (0.214 sec)
INFO:tensorflow:global_step/sec: 452.589
INFO:tensorflow:loss = 0.009502525, step = 84001 (0.221 sec)
INFO:tensorflow:global_step/sec: 450.554
INFO:tensorflow:loss = 0.0075874086, step = 84101 (0.222 sec)
INFO:tensorflow:global_step/sec: 470.395
INFO:tensorflow:loss = 0.007451632, step = 84201 (0.213 sec)
INFO:tensorflow:global_step/sec: 445.072
INFO:tensorflow:loss = 0.004052775, step = 84301 (0.225 sec)
INFO:tensorflow:global_step/sec: 471.629
INFO:tensorflow:loss = 0.006894485, step = 84401 (0.212 sec)
INFO:tensorflow:global_step/sec: 468.239
INFO:tensorflow:loss = 0.009384276, step = 84501 (0.214 sec)
INFO:tensorflow:global_step/sec: 441.032
INFO:tensorflow:loss = 0.011024917, step = 84601 (0.227 sec)
INFO:tensorflow:global_step/sec: 477.591
INFO:tensorflow:loss = 0.0065401867, step = 84701 (0.209 sec)
INFO:tensorflow:global_step/sec: 450.806
INFO:tensorflow:loss = 0.009705102, step = 84801 (0.222 sec)
INFO:tensorflow:global_step/sec: 426.918
INFO:tensorflow:loss = 0.01221613, step = 84901 (0.234 sec)
INFO:tensorflow:global_step/sec: 439.88
INFO:tensorflow:loss = 0.004846774, step = 85001 (0.227 sec)
INFO:tensorflow:global_step/sec: 455.384
INFO:tensorflow:loss = 0.005345362, step = 85101 (0.220 sec)
INFO:tensorflow:global_step/sec: 476.276
INFO:tensorflow:loss = 0.0062981583, step = 85201 (0.210 sec)
INFO:tensorflow:global_step/sec: 452.372
INFO:tensorflow:loss = 0.011848188, step = 85301 (0.221 sec)
INFO:tensorflow:global_step/sec: 459.365
INFO:tensorflow:loss = 0.006783499, step = 85401 (0.217 sec)
INFO:tensorflow:global_step/sec: 436.975
INFO:tensorflow:loss = 0.006241713, step = 85501 (0.229 sec)
INFO:tensorflow:global_step/sec: 460.003
INFO:tensorflow:loss = 0.012918718, step = 85601 (0.217 sec)
INFO:tensorflow:global_step/sec: 426.345
INFO:tensorflow:loss = 0.008487627, step = 85701 (0.235 sec)
INFO:tensorflow:global_step/sec: 444.27
INFO:tensorflow:loss = 0.008613524, step = 85801 (0.225 sec)
INFO:tensorflow:global_step/sec: 449.667
INFO:tensorflow:loss = 0.006900057, step = 85901 (0.222 sec)
INFO:tensorflow:global_step/sec: 429.614
INFO:tensorflow:loss = 0.009854027, step = 86001 (0.233 sec)
INFO:tensorflow:global_step/sec: 445.738
INFO:tensorflow:loss = 0.01331093, step = 86101 (0.224 sec)
INFO:tensorflow:global_step/sec: 459.747
INFO:tensorflow:loss = 0.010404253, step = 86201 (0.218 sec)
INFO:tensorflow:global_step/sec: 449.715
INFO:tensorflow:loss = 0.0046990267, step = 86301 (0.222 sec)
INFO:tensorflow:global_step/sec: 467.432
INFO:tensorflow:loss = 0.0064316783, step = 86401 (0.214 sec)
INFO:tensorflow:global_step/sec: 459.379
INFO:tensorflow:loss = 0.0120327715, step = 86501 (0.218 sec)
INFO:tensorflow:global_step/sec: 448.461
INFO:tensorflow:loss = 0.006700837, step = 86601 (0.223 sec)
INFO:tensorflow:global_step/sec: 440.857
INFO:tensorflow:loss = 0.008075792, step = 86701 (0.227 sec)
INFO:tensorflow:global_step/sec: 419.799
INFO:tensorflow:loss = 0.0068634166, step = 86801 (0.238 sec)
INFO:tensorflow:global_step/sec: 456.038
INFO:tensorflow:loss = 0.006373025, step = 86901 (0.219 sec)
INFO:tensorflow:global_step/sec: 435.591
INFO:tensorflow:loss = 0.0049459804, step = 87001 (0.230 sec)
INFO:tensorflow:global_step/sec: 430.734
INFO:tensorflow:loss = 0.007862547, step = 87101 (0.232 sec)
INFO:tensorflow:global_step/sec: 454.916
INFO:tensorflow:loss = 0.00981736, step = 87201 (0.220 sec)
INFO:tensorflow:global_step/sec: 446.329
INFO:tensorflow:loss = 0.011696544, step = 87301 (0.224 sec)
INFO:tensorflow:global_step/sec: 434.412
INFO:tensorflow:loss = 0.0095561575, step = 87401 (0.230 sec)
INFO:tensorflow:global_step/sec: 456.45
INFO:tensorflow:loss = 0.0037475978, step = 87501 (0.219 sec)
INFO:tensorflow:global_step/sec: 444.852
INFO:tensorflow:loss = 0.0091656465, step = 87601 (0.225 sec)
INFO:tensorflow:global_step/sec: 437.065
INFO:tensorflow:loss = 0.014315022, step = 87701 (0.229 sec)
INFO:tensorflow:global_step/sec: 429.411
INFO:tensorflow:loss = 0.012980651, step = 87801 (0.233 sec)
INFO:tensorflow:global_step/sec: 441.531
INFO:tensorflow:loss = 0.0048240935, step = 87901 (0.226 sec)
INFO:tensorflow:global_step/sec: 466.352
INFO:tensorflow:loss = 0.015078744, step = 88001 (0.214 sec)
INFO:tensorflow:global_step/sec: 405.053
INFO:tensorflow:loss = 0.011876026, step = 88101 (0.247 sec)
INFO:tensorflow:global_step/sec: 452.976
INFO:tensorflow:loss = 0.010125502, step = 88201 (0.221 sec)
INFO:tensorflow:global_step/sec: 458.186
INFO:tensorflow:loss = 0.013823442, step = 88301 (0.219 sec)
INFO:tensorflow:global_step/sec: 448.392
INFO:tensorflow:loss = 0.0053028753, step = 88401 (0.223 sec)
INFO:tensorflow:global_step/sec: 452.16
INFO:tensorflow:loss = 0.007541458, step = 88501 (0.221 sec)
INFO:tensorflow:global_step/sec: 457.381
INFO:tensorflow:loss = 0.008431977, step = 88601 (0.219 sec)
INFO:tensorflow:global_step/sec: 455.766
INFO:tensorflow:loss = 0.010850932, step = 88701 (0.219 sec)
INFO:tensorflow:global_step/sec: 436.296
INFO:tensorflow:loss = 0.017362352, step = 88801 (0.229 sec)
INFO:tensorflow:global_step/sec: 450.779
INFO:tensorflow:loss = 0.017081048, step = 88901 (0.222 sec)
INFO:tensorflow:global_step/sec: 423.594
INFO:tensorflow:loss = 0.01579927, step = 89001 (0.237 sec)
INFO:tensorflow:global_step/sec: 450.869
INFO:tensorflow:loss = 0.009363698, step = 89101 (0.220 sec)
INFO:tensorflow:global_step/sec: 470.537
INFO:tensorflow:loss = 0.009390919, step = 89201 (0.213 sec)
INFO:tensorflow:global_step/sec: 443.465
INFO:tensorflow:loss = 0.0060469382, step = 89301 (0.225 sec)
INFO:tensorflow:global_step/sec: 463.372
INFO:tensorflow:loss = 0.009950854, step = 89401 (0.221 sec)
INFO:tensorflow:global_step/sec: 410.127
INFO:tensorflow:loss = 0.0071807606, step = 89501 (0.239 sec)
INFO:tensorflow:global_step/sec: 459.375
INFO:tensorflow:loss = 0.0075567663, step = 89601 (0.218 sec)
INFO:tensorflow:global_step/sec: 480.372
INFO:tensorflow:loss = 0.004868718, step = 89701 (0.208 sec)
INFO:tensorflow:global_step/sec: 468.689
INFO:tensorflow:loss = 0.008060325, step = 89801 (0.213 sec)
INFO:tensorflow:global_step/sec: 456.521
INFO:tensorflow:loss = 0.010573608, step = 89901 (0.219 sec)
INFO:tensorflow:global_step/sec: 420.315
INFO:tensorflow:loss = 0.010056749, step = 90001 (0.238 sec)
INFO:tensorflow:global_step/sec: 475.448
INFO:tensorflow:loss = 0.004914472, step = 90101 (0.210 sec)
INFO:tensorflow:global_step/sec: 445.567
INFO:tensorflow:loss = 0.008606965, step = 90201 (0.224 sec)
INFO:tensorflow:global_step/sec: 448.602
INFO:tensorflow:loss = 0.008953879, step = 90301 (0.223 sec)
INFO:tensorflow:global_step/sec: 455.711
INFO:tensorflow:loss = 0.00606883, step = 90401 (0.220 sec)
INFO:tensorflow:global_step/sec: 439.607
INFO:tensorflow:loss = 0.0047713965, step = 90501 (0.228 sec)
INFO:tensorflow:global_step/sec: 465.712
INFO:tensorflow:loss = 0.008342918, step = 90601 (0.214 sec)
INFO:tensorflow:global_step/sec: 478.801
INFO:tensorflow:loss = 0.0068472945, step = 90701 (0.209 sec)
INFO:tensorflow:global_step/sec: 438.214
INFO:tensorflow:loss = 0.010479212, step = 90801 (0.228 sec)
INFO:tensorflow:global_step/sec: 449.317
INFO:tensorflow:loss = 0.0111814905, step = 90901 (0.223 sec)
INFO:tensorflow:global_step/sec: 441.287
INFO:tensorflow:loss = 0.006364339, step = 91001 (0.227 sec)
INFO:tensorflow:global_step/sec: 418.224
INFO:tensorflow:loss = 0.012501595, step = 91101 (0.239 sec)
INFO:tensorflow:global_step/sec: 421.417
INFO:tensorflow:loss = 0.007942257, step = 91201 (0.237 sec)
INFO:tensorflow:global_step/sec: 411.007
INFO:tensorflow:loss = 0.009648362, step = 91301 (0.243 sec)
INFO:tensorflow:global_step/sec: 441.102
INFO:tensorflow:loss = 0.004326268, step = 91401 (0.227 sec)
INFO:tensorflow:global_step/sec: 425.064
INFO:tensorflow:loss = 0.014749368, step = 91501 (0.235 sec)
INFO:tensorflow:global_step/sec: 425.8
INFO:tensorflow:loss = 0.005853046, step = 91601 (0.235 sec)
INFO:tensorflow:global_step/sec: 440.121
INFO:tensorflow:loss = 0.012736705, step = 91701 (0.227 sec)
INFO:tensorflow:global_step/sec: 380.625
INFO:tensorflow:loss = 0.006685883, step = 91801 (0.263 sec)
INFO:tensorflow:global_step/sec: 440.184
INFO:tensorflow:loss = 0.006259484, step = 91901 (0.227 sec)
INFO:tensorflow:global_step/sec: 440.847
INFO:tensorflow:loss = 0.009421283, step = 92001 (0.227 sec)
INFO:tensorflow:global_step/sec: 428.61
INFO:tensorflow:loss = 0.0056731515, step = 92101 (0.234 sec)
INFO:tensorflow:global_step/sec: 450.369
INFO:tensorflow:loss = 0.0067383423, step = 92201 (0.222 sec)
INFO:tensorflow:global_step/sec: 454.783
INFO:tensorflow:loss = 0.008340258, step = 92301 (0.220 sec)
INFO:tensorflow:global_step/sec: 446.281
INFO:tensorflow:loss = 0.007261906, step = 92401 (0.225 sec)
INFO:tensorflow:global_step/sec: 445.353
INFO:tensorflow:loss = 0.008833823, step = 92501 (0.224 sec)
INFO:tensorflow:global_step/sec: 454.267
INFO:tensorflow:loss = 0.0042939773, step = 92601 (0.220 sec)
INFO:tensorflow:global_step/sec: 453.377
INFO:tensorflow:loss = 0.012260348, step = 92701 (0.220 sec)
INFO:tensorflow:global_step/sec: 438.672
INFO:tensorflow:loss = 0.011230526, step = 92801 (0.228 sec)
INFO:tensorflow:global_step/sec: 438.941
INFO:tensorflow:loss = 0.008917751, step = 92901 (0.228 sec)
INFO:tensorflow:global_step/sec: 440.568
INFO:tensorflow:loss = 0.011714136, step = 93001 (0.227 sec)
INFO:tensorflow:global_step/sec: 466.014
INFO:tensorflow:loss = 0.0073758443, step = 93101 (0.215 sec)
INFO:tensorflow:global_step/sec: 468.119
INFO:tensorflow:loss = 0.003955289, step = 93201 (0.213 sec)
INFO:tensorflow:global_step/sec: 470.834
INFO:tensorflow:loss = 0.0046369513, step = 93301 (0.213 sec)
INFO:tensorflow:global_step/sec: 469.162
INFO:tensorflow:loss = 0.007927978, step = 93401 (0.213 sec)
INFO:tensorflow:global_step/sec: 458.322
INFO:tensorflow:loss = 0.008177168, step = 93501 (0.219 sec)
INFO:tensorflow:global_step/sec: 476.606
INFO:tensorflow:loss = 0.007055303, step = 93601 (0.209 sec)
INFO:tensorflow:global_step/sec: 471.298
INFO:tensorflow:loss = 0.0064525036, step = 93701 (0.212 sec)
INFO:tensorflow:global_step/sec: 485.527
INFO:tensorflow:loss = 0.010708123, step = 93801 (0.206 sec)
INFO:tensorflow:global_step/sec: 442.813
INFO:tensorflow:loss = 0.007946094, step = 93901 (0.226 sec)
INFO:tensorflow:global_step/sec: 468.186
INFO:tensorflow:loss = 0.0055915033, step = 94001 (0.214 sec)
INFO:tensorflow:global_step/sec: 464.727
INFO:tensorflow:loss = 0.009732682, step = 94101 (0.215 sec)
INFO:tensorflow:global_step/sec: 466.611
INFO:tensorflow:loss = 0.009899803, step = 94201 (0.214 sec)
INFO:tensorflow:global_step/sec: 451.482
INFO:tensorflow:loss = 0.007355769, step = 94301 (0.221 sec)
INFO:tensorflow:global_step/sec: 449.604
INFO:tensorflow:loss = 0.006268471, step = 94401 (0.223 sec)
INFO:tensorflow:global_step/sec: 465.948
INFO:tensorflow:loss = 0.0055277785, step = 94501 (0.215 sec)
INFO:tensorflow:global_step/sec: 449.761
INFO:tensorflow:loss = 0.008253826, step = 94601 (0.222 sec)
INFO:tensorflow:global_step/sec: 457.675
INFO:tensorflow:loss = 0.008863449, step = 94701 (0.219 sec)
INFO:tensorflow:global_step/sec: 476.456
INFO:tensorflow:loss = 0.010726278, step = 94801 (0.210 sec)
INFO:tensorflow:global_step/sec: 430.424
INFO:tensorflow:loss = 0.006165526, step = 94901 (0.232 sec)
INFO:tensorflow:global_step/sec: 454.45
INFO:tensorflow:loss = 0.014830342, step = 95001 (0.220 sec)
INFO:tensorflow:global_step/sec: 456.236
INFO:tensorflow:loss = 0.0061218496, step = 95101 (0.219 sec)
INFO:tensorflow:global_step/sec: 456.612
INFO:tensorflow:loss = 0.0061841793, step = 95201 (0.219 sec)
INFO:tensorflow:global_step/sec: 458.266
INFO:tensorflow:loss = 0.013126951, step = 95301 (0.218 sec)
INFO:tensorflow:global_step/sec: 468.801
INFO:tensorflow:loss = 0.0057089217, step = 95401 (0.213 sec)
INFO:tensorflow:global_step/sec: 473.514
INFO:tensorflow:loss = 0.010399368, step = 95501 (0.216 sec)
INFO:tensorflow:global_step/sec: 441.127
INFO:tensorflow:loss = 0.0056310957, step = 95601 (0.222 sec)
INFO:tensorflow:global_step/sec: 451.263
INFO:tensorflow:loss = 0.0053259893, step = 95701 (0.222 sec)
INFO:tensorflow:global_step/sec: 439.391
INFO:tensorflow:loss = 0.015028892, step = 95801 (0.227 sec)
INFO:tensorflow:global_step/sec: 466.444
INFO:tensorflow:loss = 0.0061596353, step = 95901 (0.214 sec)
INFO:tensorflow:global_step/sec: 450.024
INFO:tensorflow:loss = 0.007637582, step = 96001 (0.222 sec)
INFO:tensorflow:global_step/sec: 460.299
INFO:tensorflow:loss = 0.008166807, step = 96101 (0.217 sec)
INFO:tensorflow:global_step/sec: 442.73
INFO:tensorflow:loss = 0.0056512663, step = 96201 (0.226 sec)
INFO:tensorflow:global_step/sec: 471.994
INFO:tensorflow:loss = 0.0050436864, step = 96301 (0.212 sec)
INFO:tensorflow:global_step/sec: 467.734
INFO:tensorflow:loss = 0.009083891, step = 96401 (0.214 sec)
INFO:tensorflow:global_step/sec: 463.82
INFO:tensorflow:loss = 0.0092610065, step = 96501 (0.216 sec)
INFO:tensorflow:global_step/sec: 449.634
INFO:tensorflow:loss = 0.0062198066, step = 96601 (0.223 sec)
INFO:tensorflow:global_step/sec: 452.253
INFO:tensorflow:loss = 0.01348327, step = 96701 (0.221 sec)
INFO:tensorflow:global_step/sec: 451.927
INFO:tensorflow:loss = 0.0075592278, step = 96801 (0.221 sec)
INFO:tensorflow:global_step/sec: 454.775
INFO:tensorflow:loss = 0.0052253455, step = 96901 (0.220 sec)
INFO:tensorflow:global_step/sec: 457.882
INFO:tensorflow:loss = 0.009982036, step = 97001 (0.219 sec)
INFO:tensorflow:global_step/sec: 453.202
INFO:tensorflow:loss = 0.014037208, step = 97101 (0.225 sec)
INFO:tensorflow:global_step/sec: 423.68
INFO:tensorflow:loss = 0.0053974064, step = 97201 (0.232 sec)
INFO:tensorflow:global_step/sec: 440.476
INFO:tensorflow:loss = 0.004785974, step = 97301 (0.227 sec)
INFO:tensorflow:global_step/sec: 450.995
INFO:tensorflow:loss = 0.006669085, step = 97401 (0.221 sec)
INFO:tensorflow:global_step/sec: 441.568
INFO:tensorflow:loss = 0.010448052, step = 97501 (0.226 sec)
INFO:tensorflow:global_step/sec: 450.503
INFO:tensorflow:loss = 0.008053826, step = 97601 (0.222 sec)
INFO:tensorflow:global_step/sec: 442.894
INFO:tensorflow:loss = 0.0048241923, step = 97701 (0.226 sec)
INFO:tensorflow:global_step/sec: 435.682
INFO:tensorflow:loss = 0.010347674, step = 97801 (0.229 sec)
INFO:tensorflow:global_step/sec: 479.042
INFO:tensorflow:loss = 0.0047979965, step = 97901 (0.209 sec)
INFO:tensorflow:global_step/sec: 447.66
INFO:tensorflow:loss = 0.010023495, step = 98001 (0.223 sec)
INFO:tensorflow:global_step/sec: 464.753
INFO:tensorflow:loss = 0.009690805, step = 98101 (0.215 sec)
INFO:tensorflow:global_step/sec: 467.06
INFO:tensorflow:loss = 0.008275158, step = 98201 (0.214 sec)
INFO:tensorflow:global_step/sec: 465.55
INFO:tensorflow:loss = 0.011195747, step = 98301 (0.215 sec)
INFO:tensorflow:global_step/sec: 458.14
INFO:tensorflow:loss = 0.009275818, step = 98401 (0.218 sec)
INFO:tensorflow:global_step/sec: 456.97
INFO:tensorflow:loss = 0.008696778, step = 98501 (0.219 sec)
INFO:tensorflow:global_step/sec: 473.882
INFO:tensorflow:loss = 0.0084723625, step = 98601 (0.211 sec)
INFO:tensorflow:global_step/sec: 463.555
INFO:tensorflow:loss = 0.005408332, step = 98701 (0.216 sec)
INFO:tensorflow:global_step/sec: 472.934
INFO:tensorflow:loss = 0.006189944, step = 98801 (0.212 sec)
INFO:tensorflow:global_step/sec: 474.264
INFO:tensorflow:loss = 0.013700008, step = 98901 (0.210 sec)
INFO:tensorflow:global_step/sec: 468.353
INFO:tensorflow:loss = 0.009340456, step = 99001 (0.214 sec)
INFO:tensorflow:global_step/sec: 466.54
INFO:tensorflow:loss = 0.008566959, step = 99101 (0.214 sec)
INFO:tensorflow:global_step/sec: 458.68
INFO:tensorflow:loss = 0.0067969724, step = 99201 (0.218 sec)
INFO:tensorflow:global_step/sec: 428.467
INFO:tensorflow:loss = 0.008139711, step = 99301 (0.233 sec)
INFO:tensorflow:global_step/sec: 443.178
INFO:tensorflow:loss = 0.0058223605, step = 99401 (0.226 sec)
INFO:tensorflow:global_step/sec: 424.465
INFO:tensorflow:loss = 0.005538292, step = 99501 (0.236 sec)
INFO:tensorflow:global_step/sec: 453.76
INFO:tensorflow:loss = 0.0075483248, step = 99601 (0.220 sec)
INFO:tensorflow:global_step/sec: 460.636
INFO:tensorflow:loss = 0.008077534, step = 99701 (0.217 sec)
INFO:tensorflow:global_step/sec: 450.708
INFO:tensorflow:loss = 0.01044227, step = 99801 (0.222 sec)
INFO:tensorflow:global_step/sec: 460.872
INFO:tensorflow:loss = 0.009272017, step = 99901 (0.217 sec)
INFO:tensorflow:Saving checkpoints for 100000 into /tmp/tmpBX73lD/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpBX73lD/architecture-3.txt: ['0:1_layer_dnn', '1:2_layer_dnn', '2:3_layer_dnn', '3:4_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Rebuilding iteration 3
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Building iteration 4
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Building subnetwork '5_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:27:49
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpBX73lD/model.ckpt-100000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't3_4_layer_dnn' dict for global step 100000: architecture/adanet/ensembles =
�
>adanet/iteration_3/ensemble_t3_4_layer_dnn/architecture/adanetB? B9| 1_layer_dnn | 2_layer_dnn | 3_layer_dnn | 4_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.031587753, average_loss/adanet/subnetwork = 0.03348904, average_loss/adanet/uniform_average_ensemble = 0.031587753, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.04207115, loss/adanet/subnetwork = 0.041055106, loss/adanet/uniform_average_ensemble = 0.04207115, prediction/mean/adanet/adanet_weighted_ensemble = 3.1415138, prediction/mean/adanet/subnetwork = 3.1287208, prediction/mean/adanet/uniform_average_ensemble = 3.1415138
INFO:tensorflow:Saving candidate 't4_4_layer_dnn' dict for global step 100000: architecture/adanet/ensembles =
�
>adanet/iteration_4/ensemble_t4_4_layer_dnn/architecture/adanetBM BG| 1_layer_dnn | 2_layer_dnn | 3_layer_dnn | 4_layer_dnn | 4_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.031320337, average_loss/adanet/subnetwork = 0.036771663, average_loss/adanet/uniform_average_ensemble = 0.031320345, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.042101234, loss/adanet/subnetwork = 0.049315907, loss/adanet/uniform_average_ensemble = 0.04210123, prediction/mean/adanet/adanet_weighted_ensemble = 3.1364617, prediction/mean/adanet/subnetwork = 3.116253, prediction/mean/adanet/uniform_average_ensemble = 3.1364617
INFO:tensorflow:Saving candidate 't4_5_layer_dnn' dict for global step 100000: architecture/adanet/ensembles =
�
>adanet/iteration_4/ensemble_t4_5_layer_dnn/architecture/adanetBM BG| 1_layer_dnn | 2_layer_dnn | 3_layer_dnn | 4_layer_dnn | 5_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.032695297, average_loss/adanet/subnetwork = 0.0495253, average_loss/adanet/uniform_average_ensemble = 0.032695293, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.043896608, loss/adanet/subnetwork = 0.06338713, loss/adanet/uniform_average_ensemble = 0.0438966, prediction/mean/adanet/adanet_weighted_ensemble = 3.1284606, prediction/mean/adanet/subnetwork = 3.0762491, prediction/mean/adanet/uniform_average_ensemble = 3.1284606
INFO:tensorflow:Finished evaluation at 2018-12-13-19:27:54
INFO:tensorflow:Saving dict for global step 100000: average_loss = 0.032695297, average_loss/adanet/adanet_weighted_ensemble = 0.032695297, average_loss/adanet/subnetwork = 0.0495253, average_loss/adanet/uniform_average_ensemble = 0.032695293, global_step = 100000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.043896608, loss/adanet/adanet_weighted_ensemble = 0.043896608, loss/adanet/subnetwork = 0.06338713, loss/adanet/uniform_average_ensemble = 0.0438966, prediction/mean = 3.1284606, prediction/mean/adanet/adanet_weighted_ensemble = 3.1284606, prediction/mean/adanet/subnetwork = 3.0762491, prediction/mean/adanet/uniform_average_ensemble = 3.1284606
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 100000: /tmp/tmpBX73lD/model.ckpt-100000
INFO:tensorflow:Loss for final step: 0.0064807483.
INFO:tensorflow:Finished training Adanet iteration 4
Loss: 0.032695297
Architecture: | 1_layer_dnn | 2_layer_dnn | 3_layer_dnn | 4_layer_dnn | 5_layer_dnn |
| Apache-2.0 | adanet/examples/tutorials/adanet_objective.ipynb | sararob/adanet |
These hyperparameters preduce a model that achieves **0.0348** MSE on the testset (exact MSE will vary depending on the hardware you're using to train the model). Notice that the ensemble is composed of 5 subnetworks, each one a hiddenlayer deeper than the previous. The most complex subnetwork is made of 5 hiddenlayers.Since `SimpleDNNGenerator` produces subnetworks of varying complexity, and ourmodel gives each one an equal weight, AdaNet selected the subnetwork that mostlowered the ensemble's training loss at each iteration, likely the one with themost hidden layers, since it has the most capacity, and we aren't penalizingmore complex subnetworks (yet).Next, instead of assigning equal weight to each subnetwork, let's learn themixture weights as a convex optimization problem using SGD: | #@test {"skip": true}
results, _ = train_and_evaluate(learn_mixture_weights=True)
print("Loss:", results["average_loss"])
print("Uniform average loss:", results["average_loss/adanet/uniform_average_ensemble"])
print("Architecture:", ensemble_architecture(results)) | WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpDexXZd
INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f2794692a50>, '_model_dir': '/tmp/tmpDexXZd', '_protocol': None, '_save_checkpoints_steps': 50000, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_tf_random_seed': 42, '_save_summary_steps': 50000, '_device_fn': None, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_evaluation_master': '', '_eval_distribute': None, '_train_distribute': None, '_master': ''}
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 50000 or save_checkpoints_secs None.
INFO:tensorflow:Beginning training AdaNet iteration 0
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Building iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpDexXZd/model.ckpt.
INFO:tensorflow:loss = 21.773132, step = 1
INFO:tensorflow:global_step/sec: 174.652
INFO:tensorflow:loss = 0.6285208, step = 101 (0.574 sec)
INFO:tensorflow:global_step/sec: 556.031
INFO:tensorflow:loss = 0.56869733, step = 201 (0.180 sec)
INFO:tensorflow:global_step/sec: 557.64
INFO:tensorflow:loss = 0.07774231, step = 301 (0.179 sec)
INFO:tensorflow:global_step/sec: 536.303
INFO:tensorflow:loss = 0.08270252, step = 401 (0.186 sec)
INFO:tensorflow:global_step/sec: 544.736
INFO:tensorflow:loss = 0.08153409, step = 501 (0.184 sec)
INFO:tensorflow:global_step/sec: 511.091
INFO:tensorflow:loss = 0.056552373, step = 601 (0.195 sec)
INFO:tensorflow:global_step/sec: 550.621
INFO:tensorflow:loss = 0.025883075, step = 701 (0.182 sec)
INFO:tensorflow:global_step/sec: 559.246
INFO:tensorflow:loss = 0.030127663, step = 801 (0.179 sec)
INFO:tensorflow:global_step/sec: 548.297
INFO:tensorflow:loss = 0.03756211, step = 901 (0.185 sec)
INFO:tensorflow:global_step/sec: 536.855
INFO:tensorflow:loss = 0.06788661, step = 1001 (0.184 sec)
INFO:tensorflow:global_step/sec: 556.328
INFO:tensorflow:loss = 0.036306266, step = 1101 (0.180 sec)
INFO:tensorflow:global_step/sec: 552.101
INFO:tensorflow:loss = 0.05074877, step = 1201 (0.181 sec)
INFO:tensorflow:global_step/sec: 571.497
INFO:tensorflow:loss = 0.10058474, step = 1301 (0.175 sec)
INFO:tensorflow:global_step/sec: 567.55
INFO:tensorflow:loss = 0.026643533, step = 1401 (0.176 sec)
INFO:tensorflow:global_step/sec: 546.287
INFO:tensorflow:loss = 0.020885473, step = 1501 (0.183 sec)
INFO:tensorflow:global_step/sec: 543.292
INFO:tensorflow:loss = 0.032396816, step = 1601 (0.184 sec)
INFO:tensorflow:global_step/sec: 543.632
INFO:tensorflow:loss = 0.041603133, step = 1701 (0.185 sec)
INFO:tensorflow:global_step/sec: 543.496
INFO:tensorflow:loss = 0.035292536, step = 1801 (0.183 sec)
INFO:tensorflow:global_step/sec: 539.173
INFO:tensorflow:loss = 0.04474579, step = 1901 (0.185 sec)
INFO:tensorflow:global_step/sec: 552.816
INFO:tensorflow:loss = 0.029937664, step = 2001 (0.181 sec)
INFO:tensorflow:global_step/sec: 540.912
INFO:tensorflow:loss = 0.047246538, step = 2101 (0.185 sec)
INFO:tensorflow:global_step/sec: 534.233
INFO:tensorflow:loss = 0.024866749, step = 2201 (0.187 sec)
INFO:tensorflow:global_step/sec: 531.472
INFO:tensorflow:loss = 0.025053516, step = 2301 (0.188 sec)
INFO:tensorflow:global_step/sec: 539.714
INFO:tensorflow:loss = 0.022536632, step = 2401 (0.186 sec)
INFO:tensorflow:global_step/sec: 544.71
INFO:tensorflow:loss = 0.047800276, step = 2501 (0.184 sec)
INFO:tensorflow:global_step/sec: 579.096
INFO:tensorflow:loss = 0.03202751, step = 2601 (0.173 sec)
INFO:tensorflow:global_step/sec: 517.886
INFO:tensorflow:loss = 0.033754677, step = 2701 (0.193 sec)
INFO:tensorflow:global_step/sec: 534.408
INFO:tensorflow:loss = 0.014495829, step = 2801 (0.187 sec)
INFO:tensorflow:global_step/sec: 524.406
INFO:tensorflow:loss = 0.031205812, step = 2901 (0.191 sec)
INFO:tensorflow:global_step/sec: 540.38
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INFO:tensorflow:global_step/sec: 527.852
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INFO:tensorflow:global_step/sec: 544.654
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INFO:tensorflow:global_step/sec: 552.193
INFO:tensorflow:loss = 0.036313385, step = 8801 (0.181 sec)
INFO:tensorflow:global_step/sec: 544.182
INFO:tensorflow:loss = 0.05480792, step = 8901 (0.184 sec)
INFO:tensorflow:global_step/sec: 539.852
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INFO:tensorflow:global_step/sec: 549.185
INFO:tensorflow:loss = 0.01780463, step = 9401 (0.182 sec)
INFO:tensorflow:global_step/sec: 544.89
INFO:tensorflow:loss = 0.03639295, step = 9501 (0.184 sec)
INFO:tensorflow:global_step/sec: 550.291
INFO:tensorflow:loss = 0.032045305, step = 9601 (0.182 sec)
INFO:tensorflow:global_step/sec: 527.507
INFO:tensorflow:loss = 0.011883362, step = 9701 (0.189 sec)
INFO:tensorflow:global_step/sec: 523.634
INFO:tensorflow:loss = 0.021471867, step = 9801 (0.191 sec)
INFO:tensorflow:global_step/sec: 529.498
INFO:tensorflow:loss = 0.022176802, step = 9901 (0.188 sec)
INFO:tensorflow:global_step/sec: 559.493
INFO:tensorflow:loss = 0.029772094, step = 10001 (0.179 sec)
INFO:tensorflow:global_step/sec: 544.639
INFO:tensorflow:loss = 0.021319227, step = 10101 (0.184 sec)
INFO:tensorflow:global_step/sec: 557.075
INFO:tensorflow:loss = 0.04389864, step = 10201 (0.179 sec)
INFO:tensorflow:global_step/sec: 487.984
INFO:tensorflow:loss = 0.03191474, step = 10301 (0.205 sec)
INFO:tensorflow:global_step/sec: 547.975
INFO:tensorflow:loss = 0.03408063, step = 10401 (0.183 sec)
INFO:tensorflow:global_step/sec: 548.273
INFO:tensorflow:loss = 0.039806467, step = 10501 (0.182 sec)
INFO:tensorflow:global_step/sec: 565.518
INFO:tensorflow:loss = 0.025473367, step = 10601 (0.177 sec)
INFO:tensorflow:global_step/sec: 524.134
INFO:tensorflow:loss = 0.012293407, step = 10701 (0.191 sec)
INFO:tensorflow:global_step/sec: 502.922
INFO:tensorflow:loss = 0.021352112, step = 10801 (0.199 sec)
INFO:tensorflow:global_step/sec: 525.237
INFO:tensorflow:loss = 0.026855603, step = 10901 (0.190 sec)
INFO:tensorflow:global_step/sec: 556.149
INFO:tensorflow:loss = 0.009965676, step = 11001 (0.180 sec)
INFO:tensorflow:global_step/sec: 564.902
INFO:tensorflow:loss = 0.028150808, step = 11101 (0.177 sec)
INFO:tensorflow:global_step/sec: 568.818
INFO:tensorflow:loss = 0.013019005, step = 11201 (0.176 sec)
INFO:tensorflow:global_step/sec: 540.617
INFO:tensorflow:loss = 0.04203432, step = 11301 (0.185 sec)
INFO:tensorflow:global_step/sec: 531.924
INFO:tensorflow:loss = 0.018489825, step = 11401 (0.188 sec)
INFO:tensorflow:global_step/sec: 536.429
INFO:tensorflow:loss = 0.060697384, step = 11501 (0.186 sec)
INFO:tensorflow:global_step/sec: 510.345
INFO:tensorflow:loss = 0.014159491, step = 11601 (0.196 sec)
INFO:tensorflow:global_step/sec: 546.696
INFO:tensorflow:loss = 0.024260698, step = 11701 (0.183 sec)
INFO:tensorflow:global_step/sec: 513.672
INFO:tensorflow:loss = 0.013246283, step = 11801 (0.194 sec)
INFO:tensorflow:global_step/sec: 537.891
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INFO:tensorflow:global_step/sec: 566.473
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INFO:tensorflow:global_step/sec: 542.481
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INFO:tensorflow:global_step/sec: 551.542
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INFO:tensorflow:global_step/sec: 533.86
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INFO:tensorflow:global_step/sec: 548.606
INFO:tensorflow:loss = 0.016508352, step = 12401 (0.182 sec)
INFO:tensorflow:global_step/sec: 531.686
INFO:tensorflow:loss = 0.023406351, step = 12501 (0.189 sec)
INFO:tensorflow:global_step/sec: 490.28
INFO:tensorflow:loss = 0.02101646, step = 12601 (0.203 sec)
INFO:tensorflow:global_step/sec: 542.712
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INFO:tensorflow:global_step/sec: 530.763
INFO:tensorflow:loss = 0.02032728, step = 12801 (0.185 sec)
INFO:tensorflow:global_step/sec: 519.343
INFO:tensorflow:loss = 0.026288046, step = 12901 (0.193 sec)
INFO:tensorflow:global_step/sec: 531.485
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INFO:tensorflow:global_step/sec: 534.531
INFO:tensorflow:loss = 0.029657403, step = 13101 (0.187 sec)
INFO:tensorflow:global_step/sec: 537.62
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INFO:tensorflow:global_step/sec: 541.298
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INFO:tensorflow:global_step/sec: 545.59
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INFO:tensorflow:global_step/sec: 500.566
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INFO:tensorflow:global_step/sec: 512.76
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INFO:tensorflow:global_step/sec: 551.493
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INFO:tensorflow:global_step/sec: 532.155
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INFO:tensorflow:global_step/sec: 548.51
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INFO:tensorflow:global_step/sec: 523.196
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INFO:tensorflow:global_step/sec: 516.534
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INFO:tensorflow:global_step/sec: 520.536
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INFO:tensorflow:global_step/sec: 520.798
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INFO:tensorflow:global_step/sec: 460.524
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INFO:tensorflow:global_step/sec: 534.468
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INFO:tensorflow:global_step/sec: 541.968
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INFO:tensorflow:global_step/sec: 511.59
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INFO:tensorflow:global_step/sec: 555.753
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INFO:tensorflow:global_step/sec: 528.687
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INFO:tensorflow:global_step/sec: 536.075
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INFO:tensorflow:global_step/sec: 535.843
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INFO:tensorflow:global_step/sec: 517.095
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INFO:tensorflow:global_step/sec: 540.246
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INFO:tensorflow:global_step/sec: 533.911
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INFO:tensorflow:global_step/sec: 558.756
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INFO:tensorflow:global_step/sec: 545.384
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INFO:tensorflow:global_step/sec: 495.314
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INFO:tensorflow:global_step/sec: 514.602
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INFO:tensorflow:global_step/sec: 552.877
INFO:tensorflow:loss = 0.029320031, step = 16801 (0.181 sec)
INFO:tensorflow:global_step/sec: 526.172
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INFO:tensorflow:global_step/sec: 563.187
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INFO:tensorflow:global_step/sec: 559.785
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INFO:tensorflow:global_step/sec: 550.152
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INFO:tensorflow:global_step/sec: 562.37
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INFO:tensorflow:global_step/sec: 517.724
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INFO:tensorflow:global_step/sec: 554.139
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INFO:tensorflow:global_step/sec: 553.747
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INFO:tensorflow:global_step/sec: 545.114
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INFO:tensorflow:global_step/sec: 569.788
INFO:tensorflow:loss = 0.017028531, step = 17801 (0.175 sec)
INFO:tensorflow:global_step/sec: 540.353
INFO:tensorflow:loss = 0.01566209, step = 17901 (0.185 sec)
INFO:tensorflow:global_step/sec: 586.696
INFO:tensorflow:loss = 0.026907403, step = 18001 (0.171 sec)
INFO:tensorflow:global_step/sec: 540.879
INFO:tensorflow:loss = 0.029422838, step = 18101 (0.185 sec)
INFO:tensorflow:global_step/sec: 574.755
INFO:tensorflow:loss = 0.02157263, step = 18201 (0.174 sec)
INFO:tensorflow:global_step/sec: 526.621
INFO:tensorflow:loss = 0.02905935, step = 18301 (0.190 sec)
INFO:tensorflow:global_step/sec: 536.11
INFO:tensorflow:loss = 0.030221801, step = 18401 (0.187 sec)
INFO:tensorflow:global_step/sec: 546.34
INFO:tensorflow:loss = 0.017446585, step = 18501 (0.183 sec)
INFO:tensorflow:global_step/sec: 537.054
INFO:tensorflow:loss = 0.018040529, step = 18601 (0.186 sec)
INFO:tensorflow:global_step/sec: 531.011
INFO:tensorflow:loss = 0.04388584, step = 18701 (0.188 sec)
INFO:tensorflow:global_step/sec: 534.094
INFO:tensorflow:loss = 0.009870393, step = 18801 (0.187 sec)
INFO:tensorflow:global_step/sec: 547.51
INFO:tensorflow:loss = 0.02640358, step = 18901 (0.183 sec)
INFO:tensorflow:global_step/sec: 538.756
INFO:tensorflow:loss = 0.014067678, step = 19001 (0.186 sec)
INFO:tensorflow:global_step/sec: 533.325
INFO:tensorflow:loss = 0.029862395, step = 19101 (0.187 sec)
INFO:tensorflow:global_step/sec: 545.887
INFO:tensorflow:loss = 0.024341501, step = 19201 (0.183 sec)
INFO:tensorflow:global_step/sec: 550.327
INFO:tensorflow:loss = 0.01970948, step = 19301 (0.181 sec)
INFO:tensorflow:global_step/sec: 541.683
INFO:tensorflow:loss = 0.01575839, step = 19401 (0.185 sec)
INFO:tensorflow:global_step/sec: 536.115
INFO:tensorflow:loss = 0.014000012, step = 19501 (0.186 sec)
INFO:tensorflow:global_step/sec: 554.613
INFO:tensorflow:loss = 0.011808527, step = 19601 (0.180 sec)
INFO:tensorflow:global_step/sec: 548.35
INFO:tensorflow:loss = 0.011488184, step = 19701 (0.183 sec)
INFO:tensorflow:global_step/sec: 549.668
INFO:tensorflow:loss = 0.017856855, step = 19801 (0.182 sec)
INFO:tensorflow:global_step/sec: 541.225
INFO:tensorflow:loss = 0.04791218, step = 19901 (0.185 sec)
INFO:tensorflow:Saving checkpoints for 20000 into /tmp/tmpDexXZd/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Building iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:28:36
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpDexXZd/model.ckpt-20000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't0_linear' dict for global step 20000: architecture/adanet/ensembles =
W
9adanet/iteration_0/ensemble_t0_linear/architecture/adanetB B
| linear |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.049419947, average_loss/adanet/subnetwork = 0.049421377, average_loss/adanet/uniform_average_ensemble = 0.049421377, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.0625109, loss/adanet/subnetwork = 0.062442042, loss/adanet/uniform_average_ensemble = 0.062442042, prediction/mean/adanet/adanet_weighted_ensemble = 3.1072564, prediction/mean/adanet/subnetwork = 3.105895, prediction/mean/adanet/uniform_average_ensemble = 3.105895
INFO:tensorflow:Saving candidate 't0_1_layer_dnn' dict for global step 20000: architecture/adanet/ensembles =
a
>adanet/iteration_0/ensemble_t0_1_layer_dnn/architecture/adanetB B| 1_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.04015306, average_loss/adanet/subnetwork = 0.03993654, average_loss/adanet/uniform_average_ensemble = 0.03993654, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.054008663, loss/adanet/subnetwork = 0.053605493, loss/adanet/uniform_average_ensemble = 0.053605493, prediction/mean/adanet/adanet_weighted_ensemble = 3.1601584, prediction/mean/adanet/subnetwork = 3.1580222, prediction/mean/adanet/uniform_average_ensemble = 3.1580222
INFO:tensorflow:Finished evaluation at 2018-12-13-19:28:38
INFO:tensorflow:Saving dict for global step 20000: average_loss = 0.04015306, average_loss/adanet/adanet_weighted_ensemble = 0.04015306, average_loss/adanet/subnetwork = 0.03993654, average_loss/adanet/uniform_average_ensemble = 0.03993654, global_step = 20000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.054008663, loss/adanet/adanet_weighted_ensemble = 0.054008663, loss/adanet/subnetwork = 0.053605493, loss/adanet/uniform_average_ensemble = 0.053605493, prediction/mean = 3.1601584, prediction/mean/adanet/adanet_weighted_ensemble = 3.1601584, prediction/mean/adanet/subnetwork = 3.1580222, prediction/mean/adanet/uniform_average_ensemble = 3.1580222
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20000: /tmp/tmpDexXZd/model.ckpt-20000
INFO:tensorflow:Loss for final step: 0.034532204.
INFO:tensorflow:Finished training Adanet iteration 0
INFO:tensorflow:Beginning bookkeeping phase for iteration 0
INFO:tensorflow:Building iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Starting ensemble evaluation for iteration 0
INFO:tensorflow:Restoring parameters from /tmp/tmpDexXZd/model.ckpt-20000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Encountered end of input after 14 evaluations
INFO:tensorflow:Computed ensemble metrics: adanet_loss/t0_linear = 0.035082, adanet_loss/t0_1_layer_dnn = 0.021061
INFO:tensorflow:Finished ensemble evaluation for iteration 0
INFO:tensorflow:'t0_1_layer_dnn' at index 1 is moving onto the next iteration
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-0.txt: ['0:1_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Warm-starting from: (u'/tmp/tmpDexXZd/model.ckpt-20000',)
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: global_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Building iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Overwriting checkpoint with new graph for iteration 1 to /tmp/tmpDexXZd/model.ckpt-20000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Finished bookkeeping phase for iteration 0
INFO:tensorflow:Beginning training AdaNet iteration 1
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-0.txt: ['0:1_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpDexXZd/increment.ckpt-1
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 20000 into /tmp/tmpDexXZd/model.ckpt.
INFO:tensorflow:loss = 0.027296644, step = 20001
INFO:tensorflow:global_step/sec: 125.556
INFO:tensorflow:loss = 0.02694778, step = 20101 (0.798 sec)
INFO:tensorflow:global_step/sec: 514.298
INFO:tensorflow:loss = 0.02080372, step = 20201 (0.194 sec)
INFO:tensorflow:global_step/sec: 509.448
INFO:tensorflow:loss = 0.018986495, step = 20301 (0.196 sec)
INFO:tensorflow:global_step/sec: 501.6
INFO:tensorflow:loss = 0.027806558, step = 20401 (0.200 sec)
INFO:tensorflow:global_step/sec: 491.988
INFO:tensorflow:loss = 0.016540758, step = 20501 (0.209 sec)
INFO:tensorflow:global_step/sec: 475.348
INFO:tensorflow:loss = 0.01869046, step = 20601 (0.204 sec)
INFO:tensorflow:global_step/sec: 477.975
INFO:tensorflow:loss = 0.011502648, step = 20701 (0.210 sec)
INFO:tensorflow:global_step/sec: 461.406
INFO:tensorflow:loss = 0.019393912, step = 20801 (0.217 sec)
INFO:tensorflow:global_step/sec: 461.474
INFO:tensorflow:loss = 0.01570483, step = 20901 (0.217 sec)
INFO:tensorflow:global_step/sec: 430.998
INFO:tensorflow:loss = 0.027598895, step = 21001 (0.232 sec)
INFO:tensorflow:global_step/sec: 500.683
INFO:tensorflow:loss = 0.02409819, step = 21101 (0.200 sec)
INFO:tensorflow:global_step/sec: 487.372
INFO:tensorflow:loss = 0.024426196, step = 21201 (0.205 sec)
INFO:tensorflow:global_step/sec: 492.126
INFO:tensorflow:loss = 0.028549444, step = 21301 (0.203 sec)
INFO:tensorflow:global_step/sec: 529.692
INFO:tensorflow:loss = 0.010229438, step = 21401 (0.189 sec)
INFO:tensorflow:global_step/sec: 491.683
INFO:tensorflow:loss = 0.010179108, step = 21501 (0.203 sec)
INFO:tensorflow:global_step/sec: 513.555
INFO:tensorflow:loss = 0.016775038, step = 21601 (0.194 sec)
INFO:tensorflow:global_step/sec: 474.053
INFO:tensorflow:loss = 0.023403853, step = 21701 (0.211 sec)
INFO:tensorflow:global_step/sec: 496.196
INFO:tensorflow:loss = 0.017619435, step = 21801 (0.202 sec)
INFO:tensorflow:global_step/sec: 469.149
INFO:tensorflow:loss = 0.023911498, step = 21901 (0.213 sec)
INFO:tensorflow:global_step/sec: 504.579
INFO:tensorflow:loss = 0.011964874, step = 22001 (0.198 sec)
INFO:tensorflow:global_step/sec: 516.735
INFO:tensorflow:loss = 0.030546013, step = 22101 (0.194 sec)
INFO:tensorflow:global_step/sec: 495.864
INFO:tensorflow:loss = 0.023705944, step = 22201 (0.202 sec)
INFO:tensorflow:global_step/sec: 503.329
INFO:tensorflow:loss = 0.013165451, step = 22301 (0.199 sec)
INFO:tensorflow:global_step/sec: 487.729
INFO:tensorflow:loss = 0.012252132, step = 22401 (0.205 sec)
INFO:tensorflow:global_step/sec: 481.616
INFO:tensorflow:loss = 0.025068112, step = 22501 (0.207 sec)
INFO:tensorflow:global_step/sec: 475.901
INFO:tensorflow:loss = 0.018972568, step = 22601 (0.210 sec)
INFO:tensorflow:global_step/sec: 508.313
INFO:tensorflow:loss = 0.020364989, step = 22701 (0.197 sec)
INFO:tensorflow:global_step/sec: 512.797
INFO:tensorflow:loss = 0.012045037, step = 22801 (0.195 sec)
INFO:tensorflow:global_step/sec: 475.306
INFO:tensorflow:loss = 0.022771345, step = 22901 (0.210 sec)
INFO:tensorflow:global_step/sec: 511.145
INFO:tensorflow:loss = 0.015176754, step = 23001 (0.196 sec)
INFO:tensorflow:global_step/sec: 508.549
INFO:tensorflow:loss = 0.008884909, step = 23101 (0.197 sec)
INFO:tensorflow:global_step/sec: 504.582
INFO:tensorflow:loss = 0.01678998, step = 23201 (0.198 sec)
INFO:tensorflow:global_step/sec: 502.719
INFO:tensorflow:loss = 0.028632753, step = 23301 (0.199 sec)
INFO:tensorflow:global_step/sec: 507.218
INFO:tensorflow:loss = 0.01956751, step = 23401 (0.197 sec)
INFO:tensorflow:global_step/sec: 526.078
INFO:tensorflow:loss = 0.03295834, step = 23501 (0.190 sec)
INFO:tensorflow:global_step/sec: 511.098
INFO:tensorflow:loss = 0.013111612, step = 23601 (0.196 sec)
INFO:tensorflow:global_step/sec: 529.212
INFO:tensorflow:loss = 0.010788411, step = 23701 (0.189 sec)
INFO:tensorflow:global_step/sec: 490.3
INFO:tensorflow:loss = 0.016299147, step = 23801 (0.204 sec)
INFO:tensorflow:global_step/sec: 520.521
INFO:tensorflow:loss = 0.011471503, step = 23901 (0.192 sec)
INFO:tensorflow:global_step/sec: 523.681
INFO:tensorflow:loss = 0.021366708, step = 24001 (0.191 sec)
INFO:tensorflow:global_step/sec: 519.454
INFO:tensorflow:loss = 0.018561717, step = 24101 (0.193 sec)
INFO:tensorflow:global_step/sec: 503.969
INFO:tensorflow:loss = 0.010790765, step = 24201 (0.198 sec)
INFO:tensorflow:global_step/sec: 487.36
INFO:tensorflow:loss = 0.012166491, step = 24301 (0.205 sec)
INFO:tensorflow:global_step/sec: 507.4
INFO:tensorflow:loss = 0.012328864, step = 24401 (0.197 sec)
INFO:tensorflow:global_step/sec: 518.985
INFO:tensorflow:loss = 0.01842606, step = 24501 (0.193 sec)
INFO:tensorflow:global_step/sec: 504.587
INFO:tensorflow:loss = 0.017016035, step = 24601 (0.198 sec)
INFO:tensorflow:global_step/sec: 466.662
INFO:tensorflow:loss = 0.026989652, step = 24701 (0.215 sec)
INFO:tensorflow:global_step/sec: 498.432
INFO:tensorflow:loss = 0.01762497, step = 24801 (0.201 sec)
INFO:tensorflow:global_step/sec: 513.084
INFO:tensorflow:loss = 0.014085125, step = 24901 (0.195 sec)
INFO:tensorflow:global_step/sec: 473.655
INFO:tensorflow:loss = 0.010767302, step = 25001 (0.211 sec)
INFO:tensorflow:global_step/sec: 513.439
INFO:tensorflow:loss = 0.010639524, step = 25101 (0.195 sec)
INFO:tensorflow:global_step/sec: 511.389
INFO:tensorflow:loss = 0.0118070785, step = 25201 (0.196 sec)
INFO:tensorflow:global_step/sec: 465.125
INFO:tensorflow:loss = 0.029877687, step = 25301 (0.214 sec)
INFO:tensorflow:global_step/sec: 499.912
INFO:tensorflow:loss = 0.011379703, step = 25401 (0.200 sec)
INFO:tensorflow:global_step/sec: 475.197
INFO:tensorflow:loss = 0.0078331195, step = 25501 (0.210 sec)
INFO:tensorflow:global_step/sec: 485.951
INFO:tensorflow:loss = 0.024221335, step = 25601 (0.206 sec)
INFO:tensorflow:global_step/sec: 456.05
INFO:tensorflow:loss = 0.015124493, step = 25701 (0.220 sec)
INFO:tensorflow:global_step/sec: 501.311
INFO:tensorflow:loss = 0.014690703, step = 25801 (0.200 sec)
INFO:tensorflow:global_step/sec: 491.787
INFO:tensorflow:loss = 0.011645423, step = 25901 (0.203 sec)
INFO:tensorflow:global_step/sec: 523.396
INFO:tensorflow:loss = 0.012304948, step = 26001 (0.191 sec)
INFO:tensorflow:global_step/sec: 502.75
INFO:tensorflow:loss = 0.023488127, step = 26101 (0.199 sec)
INFO:tensorflow:global_step/sec: 509.463
INFO:tensorflow:loss = 0.022057274, step = 26201 (0.196 sec)
INFO:tensorflow:global_step/sec: 507.54
INFO:tensorflow:loss = 0.015472866, step = 26301 (0.197 sec)
INFO:tensorflow:global_step/sec: 517.92
INFO:tensorflow:loss = 0.020114496, step = 26401 (0.193 sec)
INFO:tensorflow:global_step/sec: 516.118
INFO:tensorflow:loss = 0.028981863, step = 26501 (0.194 sec)
INFO:tensorflow:global_step/sec: 520.687
INFO:tensorflow:loss = 0.016902642, step = 26601 (0.192 sec)
INFO:tensorflow:global_step/sec: 491.938
INFO:tensorflow:loss = 0.014692128, step = 26701 (0.203 sec)
INFO:tensorflow:global_step/sec: 505.454
INFO:tensorflow:loss = 0.012283293, step = 26801 (0.198 sec)
INFO:tensorflow:global_step/sec: 489.599
INFO:tensorflow:loss = 0.0076038223, step = 26901 (0.204 sec)
INFO:tensorflow:global_step/sec: 520.524
INFO:tensorflow:loss = 0.013220595, step = 27001 (0.192 sec)
INFO:tensorflow:global_step/sec: 512.19
INFO:tensorflow:loss = 0.012533921, step = 27101 (0.195 sec)
INFO:tensorflow:global_step/sec: 512.319
INFO:tensorflow:loss = 0.011515586, step = 27201 (0.195 sec)
INFO:tensorflow:global_step/sec: 500.666
INFO:tensorflow:loss = 0.030524896, step = 27301 (0.200 sec)
INFO:tensorflow:global_step/sec: 520.01
INFO:tensorflow:loss = 0.015720565, step = 27401 (0.192 sec)
INFO:tensorflow:global_step/sec: 522.893
INFO:tensorflow:loss = 0.011721436, step = 27501 (0.192 sec)
INFO:tensorflow:global_step/sec: 468.43
INFO:tensorflow:loss = 0.009658318, step = 27601 (0.213 sec)
INFO:tensorflow:global_step/sec: 520.321
INFO:tensorflow:loss = 0.01778774, step = 27701 (0.193 sec)
INFO:tensorflow:global_step/sec: 490.413
INFO:tensorflow:loss = 0.015258025, step = 27801 (0.203 sec)
INFO:tensorflow:global_step/sec: 499.715
INFO:tensorflow:loss = 0.00996981, step = 27901 (0.203 sec)
INFO:tensorflow:global_step/sec: 495.616
INFO:tensorflow:loss = 0.017968249, step = 28001 (0.199 sec)
INFO:tensorflow:global_step/sec: 485.9
INFO:tensorflow:loss = 0.018120103, step = 28101 (0.206 sec)
INFO:tensorflow:global_step/sec: 512.061
INFO:tensorflow:loss = 0.03020626, step = 28201 (0.195 sec)
INFO:tensorflow:global_step/sec: 500.553
INFO:tensorflow:loss = 0.016781444, step = 28301 (0.200 sec)
INFO:tensorflow:global_step/sec: 508.764
INFO:tensorflow:loss = 0.019098205, step = 28401 (0.202 sec)
INFO:tensorflow:global_step/sec: 480.136
INFO:tensorflow:loss = 0.0102314055, step = 28501 (0.203 sec)
INFO:tensorflow:global_step/sec: 521.257
INFO:tensorflow:loss = 0.018879682, step = 28601 (0.191 sec)
INFO:tensorflow:global_step/sec: 502.217
INFO:tensorflow:loss = 0.0128694195, step = 28701 (0.200 sec)
INFO:tensorflow:global_step/sec: 492.548
INFO:tensorflow:loss = 0.023393746, step = 28801 (0.203 sec)
INFO:tensorflow:global_step/sec: 502.647
INFO:tensorflow:loss = 0.039639026, step = 28901 (0.199 sec)
INFO:tensorflow:global_step/sec: 504.36
INFO:tensorflow:loss = 0.02720677, step = 29001 (0.198 sec)
INFO:tensorflow:global_step/sec: 513.542
INFO:tensorflow:loss = 0.012253448, step = 29101 (0.195 sec)
INFO:tensorflow:global_step/sec: 492.492
INFO:tensorflow:loss = 0.022054993, step = 29201 (0.203 sec)
INFO:tensorflow:global_step/sec: 537.302
INFO:tensorflow:loss = 0.0084997425, step = 29301 (0.186 sec)
INFO:tensorflow:global_step/sec: 510.498
INFO:tensorflow:loss = 0.011618842, step = 29401 (0.196 sec)
INFO:tensorflow:global_step/sec: 496.734
INFO:tensorflow:loss = 0.02253382, step = 29501 (0.201 sec)
INFO:tensorflow:global_step/sec: 503.193
INFO:tensorflow:loss = 0.019953515, step = 29601 (0.199 sec)
INFO:tensorflow:global_step/sec: 487.841
INFO:tensorflow:loss = 0.008872455, step = 29701 (0.205 sec)
INFO:tensorflow:global_step/sec: 456.171
INFO:tensorflow:loss = 0.012030635, step = 29801 (0.219 sec)
INFO:tensorflow:global_step/sec: 474.356
INFO:tensorflow:loss = 0.020582441, step = 29901 (0.211 sec)
INFO:tensorflow:global_step/sec: 506.186
INFO:tensorflow:loss = 0.020316554, step = 30001 (0.198 sec)
INFO:tensorflow:global_step/sec: 500.506
INFO:tensorflow:loss = 0.010370528, step = 30101 (0.200 sec)
INFO:tensorflow:global_step/sec: 487.453
INFO:tensorflow:loss = 0.023312874, step = 30201 (0.205 sec)
INFO:tensorflow:global_step/sec: 479.922
INFO:tensorflow:loss = 0.021624638, step = 30301 (0.208 sec)
INFO:tensorflow:global_step/sec: 468.931
INFO:tensorflow:loss = 0.013914967, step = 30401 (0.214 sec)
INFO:tensorflow:global_step/sec: 496.219
INFO:tensorflow:loss = 0.014039486, step = 30501 (0.202 sec)
INFO:tensorflow:global_step/sec: 489.002
INFO:tensorflow:loss = 0.010958173, step = 30601 (0.209 sec)
INFO:tensorflow:global_step/sec: 493.347
INFO:tensorflow:loss = 0.00800906, step = 30701 (0.198 sec)
INFO:tensorflow:global_step/sec: 502.293
INFO:tensorflow:loss = 0.016856924, step = 30801 (0.199 sec)
INFO:tensorflow:global_step/sec: 478.464
INFO:tensorflow:loss = 0.019304685, step = 30901 (0.209 sec)
INFO:tensorflow:global_step/sec: 504.798
INFO:tensorflow:loss = 0.007980205, step = 31001 (0.198 sec)
INFO:tensorflow:global_step/sec: 484.351
INFO:tensorflow:loss = 0.017069487, step = 31101 (0.206 sec)
INFO:tensorflow:global_step/sec: 486.249
INFO:tensorflow:loss = 0.010463448, step = 31201 (0.206 sec)
INFO:tensorflow:global_step/sec: 483.141
INFO:tensorflow:loss = 0.023204867, step = 31301 (0.207 sec)
INFO:tensorflow:global_step/sec: 496.078
INFO:tensorflow:loss = 0.00646232, step = 31401 (0.202 sec)
INFO:tensorflow:global_step/sec: 497.374
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INFO:tensorflow:global_step/sec: 504.05
INFO:tensorflow:loss = 0.009177749, step = 31601 (0.198 sec)
INFO:tensorflow:global_step/sec: 475.703
INFO:tensorflow:loss = 0.017115649, step = 31701 (0.210 sec)
INFO:tensorflow:global_step/sec: 496.268
INFO:tensorflow:loss = 0.009676232, step = 31801 (0.202 sec)
INFO:tensorflow:global_step/sec: 481.814
INFO:tensorflow:loss = 0.022674382, step = 31901 (0.208 sec)
INFO:tensorflow:global_step/sec: 489.759
INFO:tensorflow:loss = 0.0146349855, step = 32001 (0.203 sec)
INFO:tensorflow:global_step/sec: 500.441
INFO:tensorflow:loss = 0.009005962, step = 32101 (0.200 sec)
INFO:tensorflow:global_step/sec: 488.014
INFO:tensorflow:loss = 0.008703032, step = 32201 (0.207 sec)
INFO:tensorflow:global_step/sec: 482.009
INFO:tensorflow:loss = 0.013115581, step = 32301 (0.205 sec)
INFO:tensorflow:global_step/sec: 511.913
INFO:tensorflow:loss = 0.00883637, step = 32401 (0.196 sec)
INFO:tensorflow:global_step/sec: 510.892
INFO:tensorflow:loss = 0.013913523, step = 32501 (0.195 sec)
INFO:tensorflow:global_step/sec: 504.111
INFO:tensorflow:loss = 0.014914172, step = 32601 (0.198 sec)
INFO:tensorflow:global_step/sec: 480.622
INFO:tensorflow:loss = 0.012681922, step = 32701 (0.208 sec)
INFO:tensorflow:global_step/sec: 496.167
INFO:tensorflow:loss = 0.016758347, step = 32801 (0.202 sec)
INFO:tensorflow:global_step/sec: 506.027
INFO:tensorflow:loss = 0.015428397, step = 32901 (0.198 sec)
INFO:tensorflow:global_step/sec: 482.295
INFO:tensorflow:loss = 0.018653603, step = 33001 (0.207 sec)
INFO:tensorflow:global_step/sec: 516.332
INFO:tensorflow:loss = 0.016913021, step = 33101 (0.194 sec)
INFO:tensorflow:global_step/sec: 479.478
INFO:tensorflow:loss = 0.009802844, step = 33201 (0.209 sec)
INFO:tensorflow:global_step/sec: 515.182
INFO:tensorflow:loss = 0.00943646, step = 33301 (0.194 sec)
INFO:tensorflow:global_step/sec: 511.211
INFO:tensorflow:loss = 0.016679987, step = 33401 (0.196 sec)
INFO:tensorflow:global_step/sec: 462.924
INFO:tensorflow:loss = 0.017915051, step = 33501 (0.216 sec)
INFO:tensorflow:global_step/sec: 516.124
INFO:tensorflow:loss = 0.018016018, step = 33601 (0.194 sec)
INFO:tensorflow:global_step/sec: 493.51
INFO:tensorflow:loss = 0.0066340254, step = 33701 (0.203 sec)
INFO:tensorflow:global_step/sec: 484.344
INFO:tensorflow:loss = 0.0236358, step = 33801 (0.206 sec)
INFO:tensorflow:global_step/sec: 506.222
INFO:tensorflow:loss = 0.0104007255, step = 33901 (0.198 sec)
INFO:tensorflow:global_step/sec: 487.86
INFO:tensorflow:loss = 0.008748459, step = 34001 (0.205 sec)
INFO:tensorflow:global_step/sec: 486.604
INFO:tensorflow:loss = 0.0130307125, step = 34101 (0.205 sec)
INFO:tensorflow:global_step/sec: 482.551
INFO:tensorflow:loss = 0.01137045, step = 34201 (0.207 sec)
INFO:tensorflow:global_step/sec: 508.045
INFO:tensorflow:loss = 0.011736378, step = 34301 (0.196 sec)
INFO:tensorflow:global_step/sec: 505.107
INFO:tensorflow:loss = 0.0110898595, step = 34401 (0.198 sec)
INFO:tensorflow:global_step/sec: 517.398
INFO:tensorflow:loss = 0.011882299, step = 34501 (0.193 sec)
INFO:tensorflow:global_step/sec: 517.837
INFO:tensorflow:loss = 0.016648699, step = 34601 (0.193 sec)
INFO:tensorflow:global_step/sec: 541.204
INFO:tensorflow:loss = 0.015699964, step = 34701 (0.185 sec)
INFO:tensorflow:global_step/sec: 515.717
INFO:tensorflow:loss = 0.019044194, step = 34801 (0.194 sec)
INFO:tensorflow:global_step/sec: 500.927
INFO:tensorflow:loss = 0.02375797, step = 34901 (0.200 sec)
INFO:tensorflow:global_step/sec: 513.173
INFO:tensorflow:loss = 0.02138744, step = 35001 (0.196 sec)
INFO:tensorflow:global_step/sec: 527.977
INFO:tensorflow:loss = 0.011620376, step = 35101 (0.188 sec)
INFO:tensorflow:global_step/sec: 513.909
INFO:tensorflow:loss = 0.012534291, step = 35201 (0.194 sec)
INFO:tensorflow:global_step/sec: 515.618
INFO:tensorflow:loss = 0.017634407, step = 35301 (0.194 sec)
INFO:tensorflow:global_step/sec: 495.432
INFO:tensorflow:loss = 0.009034702, step = 35401 (0.202 sec)
INFO:tensorflow:global_step/sec: 510.194
INFO:tensorflow:loss = 0.017300691, step = 35501 (0.196 sec)
INFO:tensorflow:global_step/sec: 503.662
INFO:tensorflow:loss = 0.019155424, step = 35601 (0.200 sec)
INFO:tensorflow:global_step/sec: 485.322
INFO:tensorflow:loss = 0.014960597, step = 35701 (0.205 sec)
INFO:tensorflow:global_step/sec: 495.975
INFO:tensorflow:loss = 0.018164353, step = 35801 (0.203 sec)
INFO:tensorflow:global_step/sec: 511.399
INFO:tensorflow:loss = 0.01751562, step = 35901 (0.194 sec)
INFO:tensorflow:global_step/sec: 504.51
INFO:tensorflow:loss = 0.016572908, step = 36001 (0.198 sec)
INFO:tensorflow:global_step/sec: 495.627
INFO:tensorflow:loss = 0.020869441, step = 36101 (0.202 sec)
INFO:tensorflow:global_step/sec: 500.488
INFO:tensorflow:loss = 0.006655407, step = 36201 (0.200 sec)
INFO:tensorflow:global_step/sec: 505.071
INFO:tensorflow:loss = 0.012891432, step = 36301 (0.197 sec)
INFO:tensorflow:global_step/sec: 525.276
INFO:tensorflow:loss = 0.02040265, step = 36401 (0.191 sec)
INFO:tensorflow:global_step/sec: 486.173
INFO:tensorflow:loss = 0.0075813686, step = 36501 (0.206 sec)
INFO:tensorflow:global_step/sec: 515.355
INFO:tensorflow:loss = 0.01294738, step = 36601 (0.199 sec)
INFO:tensorflow:global_step/sec: 483.788
INFO:tensorflow:loss = 0.016664455, step = 36701 (0.201 sec)
INFO:tensorflow:global_step/sec: 500.33
INFO:tensorflow:loss = 0.01150747, step = 36801 (0.200 sec)
INFO:tensorflow:global_step/sec: 491.778
INFO:tensorflow:loss = 0.007865945, step = 36901 (0.203 sec)
INFO:tensorflow:global_step/sec: 521.844
INFO:tensorflow:loss = 0.015020737, step = 37001 (0.192 sec)
INFO:tensorflow:global_step/sec: 511.668
INFO:tensorflow:loss = 0.018812396, step = 37101 (0.195 sec)
INFO:tensorflow:global_step/sec: 506.216
INFO:tensorflow:loss = 0.010153979, step = 37201 (0.197 sec)
INFO:tensorflow:global_step/sec: 505.784
INFO:tensorflow:loss = 0.011845584, step = 37301 (0.198 sec)
INFO:tensorflow:global_step/sec: 414.422
INFO:tensorflow:loss = 0.008618769, step = 37401 (0.242 sec)
INFO:tensorflow:global_step/sec: 436.474
INFO:tensorflow:loss = 0.012981546, step = 37501 (0.228 sec)
INFO:tensorflow:global_step/sec: 425.313
INFO:tensorflow:loss = 0.014249604, step = 37601 (0.235 sec)
INFO:tensorflow:global_step/sec: 432.724
INFO:tensorflow:loss = 0.008844063, step = 37701 (0.231 sec)
INFO:tensorflow:global_step/sec: 433.674
INFO:tensorflow:loss = 0.0117831705, step = 37801 (0.231 sec)
INFO:tensorflow:global_step/sec: 424.484
INFO:tensorflow:loss = 0.011038644, step = 37901 (0.236 sec)
INFO:tensorflow:global_step/sec: 407.579
INFO:tensorflow:loss = 0.01588466, step = 38001 (0.245 sec)
INFO:tensorflow:global_step/sec: 431.131
INFO:tensorflow:loss = 0.01634461, step = 38101 (0.232 sec)
INFO:tensorflow:global_step/sec: 455.201
INFO:tensorflow:loss = 0.015869806, step = 38201 (0.220 sec)
INFO:tensorflow:global_step/sec: 434.166
INFO:tensorflow:loss = 0.021173127, step = 38301 (0.234 sec)
INFO:tensorflow:global_step/sec: 451.6
INFO:tensorflow:loss = 0.01940126, step = 38401 (0.218 sec)
INFO:tensorflow:global_step/sec: 436.3
INFO:tensorflow:loss = 0.010010801, step = 38501 (0.230 sec)
INFO:tensorflow:global_step/sec: 429.754
INFO:tensorflow:loss = 0.014012074, step = 38601 (0.232 sec)
INFO:tensorflow:global_step/sec: 426.216
INFO:tensorflow:loss = 0.013948511, step = 38701 (0.235 sec)
INFO:tensorflow:global_step/sec: 416.545
INFO:tensorflow:loss = 0.009588575, step = 38801 (0.240 sec)
INFO:tensorflow:global_step/sec: 443.017
INFO:tensorflow:loss = 0.016781844, step = 38901 (0.226 sec)
INFO:tensorflow:global_step/sec: 425.593
INFO:tensorflow:loss = 0.012341503, step = 39001 (0.235 sec)
INFO:tensorflow:global_step/sec: 436.329
INFO:tensorflow:loss = 0.01744554, step = 39101 (0.230 sec)
INFO:tensorflow:global_step/sec: 448.549
INFO:tensorflow:loss = 0.01344978, step = 39201 (0.222 sec)
INFO:tensorflow:global_step/sec: 442.085
INFO:tensorflow:loss = 0.0075440723, step = 39301 (0.226 sec)
INFO:tensorflow:global_step/sec: 416.82
INFO:tensorflow:loss = 0.012211935, step = 39401 (0.240 sec)
INFO:tensorflow:global_step/sec: 444.842
INFO:tensorflow:loss = 0.008865875, step = 39501 (0.224 sec)
INFO:tensorflow:global_step/sec: 426.612
INFO:tensorflow:loss = 0.010440472, step = 39601 (0.237 sec)
INFO:tensorflow:global_step/sec: 431.079
INFO:tensorflow:loss = 0.0091326535, step = 39701 (0.230 sec)
INFO:tensorflow:global_step/sec: 445.569
INFO:tensorflow:loss = 0.014855575, step = 39801 (0.225 sec)
INFO:tensorflow:global_step/sec: 429.166
INFO:tensorflow:loss = 0.017114088, step = 39901 (0.234 sec)
INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmpDexXZd/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-0.txt: ['0:1_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:29:36
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpDexXZd/model.ckpt-40000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't0_1_layer_dnn' dict for global step 40000: architecture/adanet/ensembles =
a
>adanet/iteration_0/ensemble_t0_1_layer_dnn/architecture/adanetB B| 1_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.04015306, average_loss/adanet/subnetwork = 0.03993654, average_loss/adanet/uniform_average_ensemble = 0.03993654, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.054008663, loss/adanet/subnetwork = 0.053605493, loss/adanet/uniform_average_ensemble = 0.053605493, prediction/mean/adanet/adanet_weighted_ensemble = 3.1601584, prediction/mean/adanet/subnetwork = 3.1580222, prediction/mean/adanet/uniform_average_ensemble = 3.1580222
INFO:tensorflow:Saving candidate 't1_1_layer_dnn' dict for global step 40000: architecture/adanet/ensembles =
o
>adanet/iteration_1/ensemble_t1_1_layer_dnn/architecture/adanetB# B | 1_layer_dnn | 1_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.03752409, average_loss/adanet/subnetwork = 0.044653624, average_loss/adanet/uniform_average_ensemble = 0.04097581, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.048775025, loss/adanet/subnetwork = 0.06800773, loss/adanet/uniform_average_ensemble = 0.059345886, prediction/mean/adanet/adanet_weighted_ensemble = 3.1091015, prediction/mean/adanet/subnetwork = 3.1593368, prediction/mean/adanet/uniform_average_ensemble = 3.1586797
INFO:tensorflow:Saving candidate 't1_2_layer_dnn' dict for global step 40000: architecture/adanet/ensembles =
o
>adanet/iteration_1/ensemble_t1_2_layer_dnn/architecture/adanetB# B | 1_layer_dnn | 2_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.03245418, average_loss/adanet/subnetwork = 0.032510567, average_loss/adanet/uniform_average_ensemble = 0.034043197, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.042194255, loss/adanet/subnetwork = 0.042689238, loss/adanet/uniform_average_ensemble = 0.045813102, prediction/mean/adanet/adanet_weighted_ensemble = 3.122271, prediction/mean/adanet/subnetwork = 3.1452672, prediction/mean/adanet/uniform_average_ensemble = 3.151645
INFO:tensorflow:Finished evaluation at 2018-12-13-19:29:39
INFO:tensorflow:Saving dict for global step 40000: average_loss = 0.03245418, average_loss/adanet/adanet_weighted_ensemble = 0.03245418, average_loss/adanet/subnetwork = 0.032510567, average_loss/adanet/uniform_average_ensemble = 0.034043197, global_step = 40000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.042194255, loss/adanet/adanet_weighted_ensemble = 0.042194255, loss/adanet/subnetwork = 0.042689238, loss/adanet/uniform_average_ensemble = 0.045813102, prediction/mean = 3.122271, prediction/mean/adanet/adanet_weighted_ensemble = 3.122271, prediction/mean/adanet/subnetwork = 3.1452672, prediction/mean/adanet/uniform_average_ensemble = 3.151645
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 40000: /tmp/tmpDexXZd/model.ckpt-40000
INFO:tensorflow:Loss for final step: 0.013128125.
INFO:tensorflow:Finished training Adanet iteration 1
INFO:tensorflow:Beginning bookkeeping phase for iteration 1
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-0.txt: ['0:1_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Starting ensemble evaluation for iteration 1
INFO:tensorflow:Restoring parameters from /tmp/tmpDexXZd/model.ckpt-40000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Encountered end of input after 14 evaluations
INFO:tensorflow:Computed ensemble metrics: adanet_loss/t0_1_layer_dnn = 0.021061, adanet_loss/t1_1_layer_dnn = 0.016978, adanet_loss/t1_2_layer_dnn = 0.011639
INFO:tensorflow:Finished ensemble evaluation for iteration 1
INFO:tensorflow:'t1_2_layer_dnn' at index 2 is moving onto the next iteration
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-1.txt: ['0:1_layer_dnn', '1:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Warm-starting from: (u'/tmp/tmpDexXZd/model.ckpt-40000',)
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: global_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Building iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Overwriting checkpoint with new graph for iteration 2 to /tmp/tmpDexXZd/model.ckpt-40000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Finished bookkeeping phase for iteration 1
INFO:tensorflow:Beginning training AdaNet iteration 2
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-1.txt: ['0:1_layer_dnn', '1:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpDexXZd/increment.ckpt-2
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmpDexXZd/model.ckpt.
INFO:tensorflow:loss = 0.0137471, step = 40001
INFO:tensorflow:global_step/sec: 101.146
INFO:tensorflow:loss = 0.01532535, step = 40101 (0.990 sec)
INFO:tensorflow:global_step/sec: 483.735
INFO:tensorflow:loss = 0.013514066, step = 40201 (0.212 sec)
INFO:tensorflow:global_step/sec: 466.098
INFO:tensorflow:loss = 0.009523302, step = 40301 (0.210 sec)
INFO:tensorflow:global_step/sec: 474.802
INFO:tensorflow:loss = 0.0171542, step = 40401 (0.210 sec)
INFO:tensorflow:global_step/sec: 490.8
INFO:tensorflow:loss = 0.0076334905, step = 40501 (0.204 sec)
INFO:tensorflow:global_step/sec: 473.496
INFO:tensorflow:loss = 0.008361066, step = 40601 (0.211 sec)
INFO:tensorflow:global_step/sec: 468.856
INFO:tensorflow:loss = 0.0078119845, step = 40701 (0.213 sec)
INFO:tensorflow:global_step/sec: 469.805
INFO:tensorflow:loss = 0.010584909, step = 40801 (0.213 sec)
INFO:tensorflow:global_step/sec: 476.379
INFO:tensorflow:loss = 0.010545989, step = 40901 (0.210 sec)
INFO:tensorflow:global_step/sec: 461.239
INFO:tensorflow:loss = 0.014772711, step = 41001 (0.217 sec)
INFO:tensorflow:global_step/sec: 495.958
INFO:tensorflow:loss = 0.014564341, step = 41101 (0.202 sec)
INFO:tensorflow:global_step/sec: 479.411
INFO:tensorflow:loss = 0.015027757, step = 41201 (0.208 sec)
INFO:tensorflow:global_step/sec: 462.648
INFO:tensorflow:loss = 0.013337929, step = 41301 (0.216 sec)
INFO:tensorflow:global_step/sec: 475.138
INFO:tensorflow:loss = 0.0073587466, step = 41401 (0.210 sec)
INFO:tensorflow:global_step/sec: 475.871
INFO:tensorflow:loss = 0.0054927142, step = 41501 (0.210 sec)
INFO:tensorflow:global_step/sec: 479.389
INFO:tensorflow:loss = 0.007360665, step = 41601 (0.209 sec)
INFO:tensorflow:global_step/sec: 457.053
INFO:tensorflow:loss = 0.014645707, step = 41701 (0.219 sec)
INFO:tensorflow:global_step/sec: 479.476
INFO:tensorflow:loss = 0.012285827, step = 41801 (0.209 sec)
INFO:tensorflow:global_step/sec: 472.166
INFO:tensorflow:loss = 0.012192512, step = 41901 (0.211 sec)
INFO:tensorflow:global_step/sec: 454.605
INFO:tensorflow:loss = 0.0075900974, step = 42001 (0.220 sec)
INFO:tensorflow:global_step/sec: 471.22
INFO:tensorflow:loss = 0.018640764, step = 42101 (0.212 sec)
INFO:tensorflow:global_step/sec: 459.797
INFO:tensorflow:loss = 0.008761501, step = 42201 (0.218 sec)
INFO:tensorflow:global_step/sec: 471.581
INFO:tensorflow:loss = 0.009257115, step = 42301 (0.212 sec)
INFO:tensorflow:global_step/sec: 468.698
INFO:tensorflow:loss = 0.0061153397, step = 42401 (0.213 sec)
INFO:tensorflow:global_step/sec: 473.882
INFO:tensorflow:loss = 0.017473524, step = 42501 (0.211 sec)
INFO:tensorflow:global_step/sec: 449.238
INFO:tensorflow:loss = 0.0081728585, step = 42601 (0.222 sec)
INFO:tensorflow:global_step/sec: 454.959
INFO:tensorflow:loss = 0.012855861, step = 42701 (0.220 sec)
INFO:tensorflow:global_step/sec: 464.764
INFO:tensorflow:loss = 0.007984189, step = 42801 (0.215 sec)
INFO:tensorflow:global_step/sec: 446.841
INFO:tensorflow:loss = 0.009112917, step = 42901 (0.224 sec)
INFO:tensorflow:global_step/sec: 467.028
INFO:tensorflow:loss = 0.008185251, step = 43001 (0.214 sec)
INFO:tensorflow:global_step/sec: 431.814
INFO:tensorflow:loss = 0.0060589975, step = 43101 (0.231 sec)
INFO:tensorflow:global_step/sec: 454.399
INFO:tensorflow:loss = 0.0138734905, step = 43201 (0.220 sec)
INFO:tensorflow:global_step/sec: 480.215
INFO:tensorflow:loss = 0.017423537, step = 43301 (0.208 sec)
INFO:tensorflow:global_step/sec: 460.834
INFO:tensorflow:loss = 0.013033772, step = 43401 (0.217 sec)
INFO:tensorflow:global_step/sec: 489.313
INFO:tensorflow:loss = 0.018026771, step = 43501 (0.204 sec)
INFO:tensorflow:global_step/sec: 475.597
INFO:tensorflow:loss = 0.008088482, step = 43601 (0.210 sec)
INFO:tensorflow:global_step/sec: 459.796
INFO:tensorflow:loss = 0.00725925, step = 43701 (0.217 sec)
INFO:tensorflow:global_step/sec: 454.434
INFO:tensorflow:loss = 0.010700462, step = 43801 (0.220 sec)
INFO:tensorflow:global_step/sec: 485.871
INFO:tensorflow:loss = 0.006103119, step = 43901 (0.206 sec)
INFO:tensorflow:global_step/sec: 481.494
INFO:tensorflow:loss = 0.010852184, step = 44001 (0.207 sec)
INFO:tensorflow:global_step/sec: 467.93
INFO:tensorflow:loss = 0.010531217, step = 44101 (0.214 sec)
INFO:tensorflow:global_step/sec: 453.365
INFO:tensorflow:loss = 0.0073792683, step = 44201 (0.221 sec)
INFO:tensorflow:global_step/sec: 456.138
INFO:tensorflow:loss = 0.0056517217, step = 44301 (0.220 sec)
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INFO:tensorflow:global_step/sec: 387.669
INFO:tensorflow:loss = 0.008945169, step = 44501 (0.258 sec)
INFO:tensorflow:global_step/sec: 380.011
INFO:tensorflow:loss = 0.010574166, step = 44601 (0.263 sec)
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INFO:tensorflow:loss = 0.013343923, step = 44701 (0.265 sec)
INFO:tensorflow:global_step/sec: 385.77
INFO:tensorflow:loss = 0.009692784, step = 44801 (0.259 sec)
INFO:tensorflow:global_step/sec: 376.301
INFO:tensorflow:loss = 0.010447414, step = 44901 (0.266 sec)
INFO:tensorflow:global_step/sec: 373.331
INFO:tensorflow:loss = 0.006807711, step = 45001 (0.271 sec)
INFO:tensorflow:global_step/sec: 369.024
INFO:tensorflow:loss = 0.0049399105, step = 45101 (0.268 sec)
INFO:tensorflow:global_step/sec: 385.551
INFO:tensorflow:loss = 0.0061659655, step = 45201 (0.260 sec)
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INFO:tensorflow:loss = 0.014129622, step = 45301 (0.270 sec)
INFO:tensorflow:global_step/sec: 380.428
INFO:tensorflow:loss = 0.0068014106, step = 45401 (0.263 sec)
INFO:tensorflow:global_step/sec: 365.342
INFO:tensorflow:loss = 0.004838192, step = 45501 (0.274 sec)
INFO:tensorflow:global_step/sec: 395.007
INFO:tensorflow:loss = 0.015045141, step = 45601 (0.253 sec)
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INFO:tensorflow:loss = 0.009517975, step = 45701 (0.261 sec)
INFO:tensorflow:global_step/sec: 384.175
INFO:tensorflow:loss = 0.008561723, step = 45801 (0.260 sec)
INFO:tensorflow:global_step/sec: 374.008
INFO:tensorflow:loss = 0.0068531577, step = 45901 (0.268 sec)
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INFO:tensorflow:loss = 0.011003407, step = 46001 (0.265 sec)
INFO:tensorflow:global_step/sec: 359.694
INFO:tensorflow:loss = 0.012901819, step = 46101 (0.278 sec)
INFO:tensorflow:global_step/sec: 370.976
INFO:tensorflow:loss = 0.015841253, step = 46201 (0.269 sec)
INFO:tensorflow:global_step/sec: 388.523
INFO:tensorflow:loss = 0.0071182987, step = 46301 (0.257 sec)
INFO:tensorflow:global_step/sec: 381.968
INFO:tensorflow:loss = 0.009662391, step = 46401 (0.262 sec)
INFO:tensorflow:global_step/sec: 379.256
INFO:tensorflow:loss = 0.018189866, step = 46501 (0.264 sec)
INFO:tensorflow:global_step/sec: 482.339
INFO:tensorflow:loss = 0.008831465, step = 46601 (0.207 sec)
INFO:tensorflow:global_step/sec: 455.117
INFO:tensorflow:loss = 0.008682405, step = 46701 (0.220 sec)
INFO:tensorflow:global_step/sec: 453.077
INFO:tensorflow:loss = 0.005749801, step = 46801 (0.221 sec)
INFO:tensorflow:global_step/sec: 471.098
INFO:tensorflow:loss = 0.006978311, step = 46901 (0.212 sec)
INFO:tensorflow:global_step/sec: 454.156
INFO:tensorflow:loss = 0.0061075217, step = 47001 (0.220 sec)
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INFO:tensorflow:loss = 0.0070029953, step = 47101 (0.209 sec)
INFO:tensorflow:global_step/sec: 464.6
INFO:tensorflow:loss = 0.008448597, step = 47201 (0.215 sec)
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INFO:tensorflow:loss = 0.01530963, step = 47301 (0.217 sec)
INFO:tensorflow:global_step/sec: 465.272
INFO:tensorflow:loss = 0.010236602, step = 47401 (0.215 sec)
INFO:tensorflow:global_step/sec: 446.956
INFO:tensorflow:loss = 0.0056663156, step = 47501 (0.224 sec)
INFO:tensorflow:global_step/sec: 456.136
INFO:tensorflow:loss = 0.008336479, step = 47601 (0.219 sec)
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INFO:tensorflow:loss = 0.011462681, step = 47701 (0.210 sec)
INFO:tensorflow:global_step/sec: 459.531
INFO:tensorflow:loss = 0.010124264, step = 47801 (0.218 sec)
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INFO:tensorflow:loss = 0.01634513, step = 48001 (0.215 sec)
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INFO:tensorflow:loss = 0.012271572, step = 48101 (0.210 sec)
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INFO:tensorflow:loss = 0.015318329, step = 48201 (0.212 sec)
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INFO:tensorflow:loss = 0.013058911, step = 48301 (0.216 sec)
INFO:tensorflow:global_step/sec: 457.091
INFO:tensorflow:loss = 0.009103151, step = 48401 (0.219 sec)
INFO:tensorflow:global_step/sec: 445.359
INFO:tensorflow:loss = 0.008441424, step = 48501 (0.224 sec)
INFO:tensorflow:global_step/sec: 475.082
INFO:tensorflow:loss = 0.010041006, step = 48601 (0.211 sec)
INFO:tensorflow:global_step/sec: 465.35
INFO:tensorflow:loss = 0.009227474, step = 48701 (0.215 sec)
INFO:tensorflow:global_step/sec: 462.212
INFO:tensorflow:loss = 0.013637528, step = 48801 (0.216 sec)
INFO:tensorflow:global_step/sec: 475.867
INFO:tensorflow:loss = 0.020799551, step = 48901 (0.210 sec)
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INFO:tensorflow:loss = 0.019677676, step = 49001 (0.217 sec)
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INFO:tensorflow:loss = 0.009188135, step = 49101 (0.210 sec)
INFO:tensorflow:global_step/sec: 456.362
INFO:tensorflow:loss = 0.010784849, step = 49201 (0.219 sec)
INFO:tensorflow:global_step/sec: 451.459
INFO:tensorflow:loss = 0.0055096457, step = 49301 (0.221 sec)
INFO:tensorflow:global_step/sec: 464.106
INFO:tensorflow:loss = 0.00841219, step = 49401 (0.215 sec)
INFO:tensorflow:global_step/sec: 482.989
INFO:tensorflow:loss = 0.009682183, step = 49501 (0.207 sec)
INFO:tensorflow:global_step/sec: 469.153
INFO:tensorflow:loss = 0.00786082, step = 49601 (0.213 sec)
INFO:tensorflow:global_step/sec: 456.198
INFO:tensorflow:loss = 0.005133249, step = 49701 (0.219 sec)
INFO:tensorflow:global_step/sec: 473.281
INFO:tensorflow:loss = 0.009866834, step = 49801 (0.211 sec)
INFO:tensorflow:global_step/sec: 491.405
INFO:tensorflow:loss = 0.01244057, step = 49901 (0.203 sec)
INFO:tensorflow:global_step/sec: 485.581
INFO:tensorflow:loss = 0.012799989, step = 50001 (0.206 sec)
INFO:tensorflow:global_step/sec: 467.768
INFO:tensorflow:loss = 0.005624939, step = 50101 (0.213 sec)
INFO:tensorflow:global_step/sec: 455.351
INFO:tensorflow:loss = 0.011780135, step = 50201 (0.220 sec)
INFO:tensorflow:global_step/sec: 448.105
INFO:tensorflow:loss = 0.009567579, step = 50301 (0.223 sec)
INFO:tensorflow:global_step/sec: 466.978
INFO:tensorflow:loss = 0.008895083, step = 50401 (0.214 sec)
INFO:tensorflow:global_step/sec: 454.39
INFO:tensorflow:loss = 0.0076741823, step = 50501 (0.220 sec)
INFO:tensorflow:global_step/sec: 452.8
INFO:tensorflow:loss = 0.009617523, step = 50601 (0.221 sec)
INFO:tensorflow:global_step/sec: 466.997
INFO:tensorflow:loss = 0.00584445, step = 50701 (0.214 sec)
INFO:tensorflow:global_step/sec: 475.645
INFO:tensorflow:loss = 0.011215346, step = 50801 (0.210 sec)
INFO:tensorflow:global_step/sec: 473.007
INFO:tensorflow:loss = 0.012271669, step = 50901 (0.212 sec)
INFO:tensorflow:global_step/sec: 456.348
INFO:tensorflow:loss = 0.007091139, step = 51001 (0.219 sec)
INFO:tensorflow:global_step/sec: 462.313
INFO:tensorflow:loss = 0.010636905, step = 51101 (0.216 sec)
INFO:tensorflow:global_step/sec: 462.969
INFO:tensorflow:loss = 0.0059023993, step = 51201 (0.216 sec)
INFO:tensorflow:global_step/sec: 472.121
INFO:tensorflow:loss = 0.010111769, step = 51301 (0.212 sec)
INFO:tensorflow:global_step/sec: 457.465
INFO:tensorflow:loss = 0.0042239064, step = 51401 (0.219 sec)
INFO:tensorflow:global_step/sec: 455.431
INFO:tensorflow:loss = 0.016194275, step = 51501 (0.220 sec)
INFO:tensorflow:global_step/sec: 470.491
INFO:tensorflow:loss = 0.0047591464, step = 51601 (0.213 sec)
INFO:tensorflow:global_step/sec: 451.943
INFO:tensorflow:loss = 0.011088285, step = 51701 (0.221 sec)
INFO:tensorflow:global_step/sec: 464.887
INFO:tensorflow:loss = 0.005757529, step = 51801 (0.215 sec)
INFO:tensorflow:global_step/sec: 446.253
INFO:tensorflow:loss = 0.00988936, step = 51901 (0.224 sec)
INFO:tensorflow:global_step/sec: 477.651
INFO:tensorflow:loss = 0.0077861943, step = 52001 (0.210 sec)
INFO:tensorflow:global_step/sec: 458.459
INFO:tensorflow:loss = 0.004958364, step = 52101 (0.218 sec)
INFO:tensorflow:global_step/sec: 462.939
INFO:tensorflow:loss = 0.004744456, step = 52201 (0.216 sec)
INFO:tensorflow:global_step/sec: 458.176
INFO:tensorflow:loss = 0.007713549, step = 52301 (0.218 sec)
INFO:tensorflow:global_step/sec: 461.233
INFO:tensorflow:loss = 0.006713408, step = 52401 (0.217 sec)
INFO:tensorflow:global_step/sec: 487.764
INFO:tensorflow:loss = 0.00816009, step = 52501 (0.209 sec)
INFO:tensorflow:global_step/sec: 473.837
INFO:tensorflow:loss = 0.0061250767, step = 52601 (0.207 sec)
INFO:tensorflow:global_step/sec: 485.171
INFO:tensorflow:loss = 0.012142177, step = 52701 (0.206 sec)
INFO:tensorflow:global_step/sec: 483.444
INFO:tensorflow:loss = 0.012386767, step = 52801 (0.207 sec)
INFO:tensorflow:global_step/sec: 443.19
INFO:tensorflow:loss = 0.0108658485, step = 52901 (0.226 sec)
INFO:tensorflow:global_step/sec: 449.531
INFO:tensorflow:loss = 0.012192146, step = 53001 (0.222 sec)
INFO:tensorflow:global_step/sec: 448.776
INFO:tensorflow:loss = 0.007505279, step = 53101 (0.223 sec)
INFO:tensorflow:global_step/sec: 487.781
INFO:tensorflow:loss = 0.004307149, step = 53201 (0.205 sec)
INFO:tensorflow:global_step/sec: 437.103
INFO:tensorflow:loss = 0.0076565198, step = 53301 (0.229 sec)
INFO:tensorflow:global_step/sec: 477.086
INFO:tensorflow:loss = 0.011304293, step = 53401 (0.209 sec)
INFO:tensorflow:global_step/sec: 447.387
INFO:tensorflow:loss = 0.00830624, step = 53501 (0.224 sec)
INFO:tensorflow:global_step/sec: 485.999
INFO:tensorflow:loss = 0.010162868, step = 53601 (0.206 sec)
INFO:tensorflow:global_step/sec: 465.03
INFO:tensorflow:loss = 0.004446827, step = 53701 (0.215 sec)
INFO:tensorflow:global_step/sec: 450.418
INFO:tensorflow:loss = 0.010949729, step = 53801 (0.222 sec)
INFO:tensorflow:global_step/sec: 460.396
INFO:tensorflow:loss = 0.0057958183, step = 53901 (0.217 sec)
INFO:tensorflow:global_step/sec: 389.695
INFO:tensorflow:loss = 0.0049026543, step = 54001 (0.257 sec)
INFO:tensorflow:global_step/sec: 472.552
INFO:tensorflow:loss = 0.010716478, step = 54101 (0.212 sec)
INFO:tensorflow:global_step/sec: 471.338
INFO:tensorflow:loss = 0.0073531447, step = 54201 (0.212 sec)
INFO:tensorflow:global_step/sec: 490.99
INFO:tensorflow:loss = 0.007303466, step = 54301 (0.204 sec)
INFO:tensorflow:global_step/sec: 485.63
INFO:tensorflow:loss = 0.0046647578, step = 54401 (0.206 sec)
INFO:tensorflow:global_step/sec: 491.174
INFO:tensorflow:loss = 0.00559157, step = 54501 (0.204 sec)
INFO:tensorflow:global_step/sec: 483.821
INFO:tensorflow:loss = 0.010638798, step = 54601 (0.206 sec)
INFO:tensorflow:global_step/sec: 476.122
INFO:tensorflow:loss = 0.0096184425, step = 54701 (0.210 sec)
INFO:tensorflow:global_step/sec: 477.35
INFO:tensorflow:loss = 0.01297844, step = 54801 (0.210 sec)
INFO:tensorflow:global_step/sec: 479.378
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INFO:tensorflow:global_step/sec: 459.948
INFO:tensorflow:loss = 0.015770674, step = 55001 (0.217 sec)
INFO:tensorflow:global_step/sec: 482.795
INFO:tensorflow:loss = 0.010697407, step = 55101 (0.207 sec)
INFO:tensorflow:global_step/sec: 472.72
INFO:tensorflow:loss = 0.009993464, step = 55201 (0.211 sec)
INFO:tensorflow:global_step/sec: 439.491
INFO:tensorflow:loss = 0.011722613, step = 55301 (0.228 sec)
INFO:tensorflow:global_step/sec: 470.181
INFO:tensorflow:loss = 0.0075947065, step = 55401 (0.213 sec)
INFO:tensorflow:global_step/sec: 481.406
INFO:tensorflow:loss = 0.013326233, step = 55501 (0.208 sec)
INFO:tensorflow:global_step/sec: 481.447
INFO:tensorflow:loss = 0.009337759, step = 55601 (0.208 sec)
INFO:tensorflow:global_step/sec: 477.784
INFO:tensorflow:loss = 0.0060269767, step = 55701 (0.209 sec)
INFO:tensorflow:global_step/sec: 472.844
INFO:tensorflow:loss = 0.015555512, step = 55801 (0.212 sec)
INFO:tensorflow:global_step/sec: 478.758
INFO:tensorflow:loss = 0.010265168, step = 55901 (0.209 sec)
INFO:tensorflow:global_step/sec: 486.532
INFO:tensorflow:loss = 0.008330882, step = 56001 (0.205 sec)
INFO:tensorflow:global_step/sec: 475.405
INFO:tensorflow:loss = 0.009440938, step = 56101 (0.211 sec)
INFO:tensorflow:global_step/sec: 461.349
INFO:tensorflow:loss = 0.006797244, step = 56201 (0.217 sec)
INFO:tensorflow:global_step/sec: 479.669
INFO:tensorflow:loss = 0.0061167153, step = 56301 (0.209 sec)
INFO:tensorflow:global_step/sec: 468.132
INFO:tensorflow:loss = 0.008877866, step = 56401 (0.213 sec)
INFO:tensorflow:global_step/sec: 487.572
INFO:tensorflow:loss = 0.00635955, step = 56501 (0.205 sec)
INFO:tensorflow:global_step/sec: 461.533
INFO:tensorflow:loss = 0.00695836, step = 56601 (0.217 sec)
INFO:tensorflow:global_step/sec: 479.64
INFO:tensorflow:loss = 0.007901347, step = 56701 (0.208 sec)
INFO:tensorflow:global_step/sec: 473.42
INFO:tensorflow:loss = 0.0077539934, step = 56801 (0.212 sec)
INFO:tensorflow:global_step/sec: 468.731
INFO:tensorflow:loss = 0.0040710955, step = 56901 (0.213 sec)
INFO:tensorflow:global_step/sec: 460.248
INFO:tensorflow:loss = 0.009691806, step = 57001 (0.217 sec)
INFO:tensorflow:global_step/sec: 465.523
INFO:tensorflow:loss = 0.01473847, step = 57101 (0.215 sec)
INFO:tensorflow:global_step/sec: 451.245
INFO:tensorflow:loss = 0.008093638, step = 57201 (0.222 sec)
INFO:tensorflow:global_step/sec: 465.268
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INFO:tensorflow:global_step/sec: 463.779
INFO:tensorflow:loss = 0.006130575, step = 57401 (0.216 sec)
INFO:tensorflow:global_step/sec: 465.4
INFO:tensorflow:loss = 0.007890692, step = 57501 (0.215 sec)
INFO:tensorflow:global_step/sec: 446.58
INFO:tensorflow:loss = 0.008413052, step = 57601 (0.224 sec)
INFO:tensorflow:global_step/sec: 461.431
INFO:tensorflow:loss = 0.004950462, step = 57701 (0.217 sec)
INFO:tensorflow:global_step/sec: 478.771
INFO:tensorflow:loss = 0.010200614, step = 57801 (0.209 sec)
INFO:tensorflow:global_step/sec: 457.521
INFO:tensorflow:loss = 0.0050936504, step = 57901 (0.219 sec)
INFO:tensorflow:global_step/sec: 483.361
INFO:tensorflow:loss = 0.009040279, step = 58001 (0.207 sec)
INFO:tensorflow:global_step/sec: 453.708
INFO:tensorflow:loss = 0.012236675, step = 58101 (0.220 sec)
INFO:tensorflow:global_step/sec: 459.548
INFO:tensorflow:loss = 0.0077486634, step = 58201 (0.218 sec)
INFO:tensorflow:global_step/sec: 481.926
INFO:tensorflow:loss = 0.013432663, step = 58301 (0.207 sec)
INFO:tensorflow:global_step/sec: 465.162
INFO:tensorflow:loss = 0.009004887, step = 58401 (0.215 sec)
INFO:tensorflow:global_step/sec: 448.35
INFO:tensorflow:loss = 0.007098233, step = 58501 (0.223 sec)
INFO:tensorflow:global_step/sec: 463.394
INFO:tensorflow:loss = 0.007232779, step = 58601 (0.216 sec)
INFO:tensorflow:global_step/sec: 443.679
INFO:tensorflow:loss = 0.004959191, step = 58701 (0.225 sec)
INFO:tensorflow:global_step/sec: 473.83
INFO:tensorflow:loss = 0.006908235, step = 58801 (0.211 sec)
INFO:tensorflow:global_step/sec: 476.467
INFO:tensorflow:loss = 0.0141474735, step = 58901 (0.210 sec)
INFO:tensorflow:global_step/sec: 461.393
INFO:tensorflow:loss = 0.008379664, step = 59001 (0.217 sec)
INFO:tensorflow:global_step/sec: 495.687
INFO:tensorflow:loss = 0.010867199, step = 59101 (0.203 sec)
INFO:tensorflow:global_step/sec: 485.229
INFO:tensorflow:loss = 0.0071855583, step = 59201 (0.205 sec)
INFO:tensorflow:global_step/sec: 482.091
INFO:tensorflow:loss = 0.0051718047, step = 59301 (0.208 sec)
INFO:tensorflow:global_step/sec: 464.531
INFO:tensorflow:loss = 0.009021869, step = 59401 (0.215 sec)
INFO:tensorflow:global_step/sec: 474.012
INFO:tensorflow:loss = 0.005612652, step = 59501 (0.211 sec)
INFO:tensorflow:global_step/sec: 458.11
INFO:tensorflow:loss = 0.0084251575, step = 59601 (0.218 sec)
INFO:tensorflow:global_step/sec: 459.245
INFO:tensorflow:loss = 0.0064932797, step = 59701 (0.218 sec)
INFO:tensorflow:global_step/sec: 482.2
INFO:tensorflow:loss = 0.009190215, step = 59801 (0.208 sec)
INFO:tensorflow:global_step/sec: 502.836
INFO:tensorflow:loss = 0.0087354295, step = 59901 (0.199 sec)
INFO:tensorflow:Saving checkpoints for 60000 into /tmp/tmpDexXZd/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-1.txt: ['0:1_layer_dnn', '1:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:30:45
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpDexXZd/model.ckpt-60000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't1_2_layer_dnn' dict for global step 60000: architecture/adanet/ensembles =
o
>adanet/iteration_1/ensemble_t1_2_layer_dnn/architecture/adanetB# B | 1_layer_dnn | 2_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.03245418, average_loss/adanet/subnetwork = 0.032510567, average_loss/adanet/uniform_average_ensemble = 0.034043197, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.042194255, loss/adanet/subnetwork = 0.042689238, loss/adanet/uniform_average_ensemble = 0.045813102, prediction/mean/adanet/adanet_weighted_ensemble = 3.122271, prediction/mean/adanet/subnetwork = 3.1452672, prediction/mean/adanet/uniform_average_ensemble = 3.151645
INFO:tensorflow:Saving candidate 't2_2_layer_dnn' dict for global step 60000: architecture/adanet/ensembles =
}
>adanet/iteration_2/ensemble_t2_2_layer_dnn/architecture/adanetB1 B+| 1_layer_dnn | 2_layer_dnn | 2_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.029876871, average_loss/adanet/subnetwork = 0.032713592, average_loss/adanet/uniform_average_ensemble = 0.031925786, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.03766784, loss/adanet/subnetwork = 0.043944843, loss/adanet/uniform_average_ensemble = 0.043711387, prediction/mean/adanet/adanet_weighted_ensemble = 3.1007698, prediction/mean/adanet/subnetwork = 3.1556947, prediction/mean/adanet/uniform_average_ensemble = 3.1529949
INFO:tensorflow:Saving candidate 't2_3_layer_dnn' dict for global step 60000: architecture/adanet/ensembles =
}
>adanet/iteration_2/ensemble_t2_3_layer_dnn/architecture/adanetB1 B+| 1_layer_dnn | 2_layer_dnn | 3_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.030102361, average_loss/adanet/subnetwork = 0.032910354, average_loss/adanet/uniform_average_ensemble = 0.03231746, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.038314007, loss/adanet/subnetwork = 0.043785788, loss/adanet/uniform_average_ensemble = 0.04384736, prediction/mean/adanet/adanet_weighted_ensemble = 3.1021514, prediction/mean/adanet/subnetwork = 3.134045, prediction/mean/adanet/uniform_average_ensemble = 3.1457782
INFO:tensorflow:Finished evaluation at 2018-12-13-19:30:49
INFO:tensorflow:Saving dict for global step 60000: average_loss = 0.030102361, average_loss/adanet/adanet_weighted_ensemble = 0.030102361, average_loss/adanet/subnetwork = 0.032910354, average_loss/adanet/uniform_average_ensemble = 0.03231746, global_step = 60000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.038314007, loss/adanet/adanet_weighted_ensemble = 0.038314007, loss/adanet/subnetwork = 0.043785788, loss/adanet/uniform_average_ensemble = 0.04384736, prediction/mean = 3.1021514, prediction/mean/adanet/adanet_weighted_ensemble = 3.1021514, prediction/mean/adanet/subnetwork = 3.134045, prediction/mean/adanet/uniform_average_ensemble = 3.1457782
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 60000: /tmp/tmpDexXZd/model.ckpt-60000
INFO:tensorflow:Loss for final step: 0.0064613554.
INFO:tensorflow:Finished training Adanet iteration 2
INFO:tensorflow:Beginning bookkeeping phase for iteration 2
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-1.txt: ['0:1_layer_dnn', '1:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Starting ensemble evaluation for iteration 2
INFO:tensorflow:Restoring parameters from /tmp/tmpDexXZd/model.ckpt-60000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Encountered end of input after 14 evaluations
INFO:tensorflow:Computed ensemble metrics: adanet_loss/t1_2_layer_dnn = 0.011639, adanet_loss/t2_2_layer_dnn = 0.008030, adanet_loss/t2_3_layer_dnn = 0.007307
INFO:tensorflow:Finished ensemble evaluation for iteration 2
INFO:tensorflow:'t2_3_layer_dnn' at index 2 is moving onto the next iteration
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-2.txt: ['0:1_layer_dnn', '1:2_layer_dnn', '2:3_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Warm-starting from: (u'/tmp/tmpDexXZd/model.ckpt-60000',)
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_3/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_3_layer_dnn/adanet/iteration_2/candidate_t2_3_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_3_layer_dnn/adanet/iteration_2/candidate_t2_3_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: global_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_3_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_2_layer_dnn/adanet/iteration_2/candidate_t1_2_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_3/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_2_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_2_layer_dnn/adanet/iteration_2/candidate_t1_2_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Building iteration 3
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Overwriting checkpoint with new graph for iteration 3 to /tmp/tmpDexXZd/model.ckpt-60000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Finished bookkeeping phase for iteration 2
INFO:tensorflow:Beginning training AdaNet iteration 3
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-2.txt: ['0:1_layer_dnn', '1:2_layer_dnn', '2:3_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Building iteration 3
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpDexXZd/increment.ckpt-3
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 60000 into /tmp/tmpDexXZd/model.ckpt.
INFO:tensorflow:loss = 0.008016532, step = 60001
INFO:tensorflow:global_step/sec: 86.6128
INFO:tensorflow:loss = 0.009209515, step = 60101 (1.155 sec)
INFO:tensorflow:global_step/sec: 471.158
INFO:tensorflow:loss = 0.01075724, step = 60201 (0.212 sec)
INFO:tensorflow:global_step/sec: 437.641
INFO:tensorflow:loss = 0.0059028785, step = 60301 (0.228 sec)
INFO:tensorflow:global_step/sec: 436.302
INFO:tensorflow:loss = 0.011906806, step = 60401 (0.229 sec)
INFO:tensorflow:global_step/sec: 452.837
INFO:tensorflow:loss = 0.0039542457, step = 60501 (0.221 sec)
INFO:tensorflow:global_step/sec: 442.837
INFO:tensorflow:loss = 0.0043944037, step = 60601 (0.226 sec)
INFO:tensorflow:global_step/sec: 454.003
INFO:tensorflow:loss = 0.0061429534, step = 60701 (0.220 sec)
INFO:tensorflow:global_step/sec: 454.473
INFO:tensorflow:loss = 0.0069281845, step = 60801 (0.220 sec)
INFO:tensorflow:global_step/sec: 450.475
INFO:tensorflow:loss = 0.0092753135, step = 60901 (0.222 sec)
INFO:tensorflow:global_step/sec: 467.688
INFO:tensorflow:loss = 0.009403301, step = 61001 (0.214 sec)
INFO:tensorflow:global_step/sec: 450.793
INFO:tensorflow:loss = 0.00983897, step = 61101 (0.221 sec)
INFO:tensorflow:global_step/sec: 449.982
INFO:tensorflow:loss = 0.010074519, step = 61201 (0.223 sec)
INFO:tensorflow:global_step/sec: 459.688
INFO:tensorflow:loss = 0.004837831, step = 61301 (0.217 sec)
INFO:tensorflow:global_step/sec: 473.48
INFO:tensorflow:loss = 0.003884089, step = 61401 (0.211 sec)
INFO:tensorflow:global_step/sec: 438.768
INFO:tensorflow:loss = 0.0039161984, step = 61501 (0.228 sec)
INFO:tensorflow:global_step/sec: 442.4
INFO:tensorflow:loss = 0.004391333, step = 61601 (0.226 sec)
INFO:tensorflow:global_step/sec: 446.632
INFO:tensorflow:loss = 0.011235558, step = 61701 (0.224 sec)
INFO:tensorflow:global_step/sec: 456.198
INFO:tensorflow:loss = 0.007418411, step = 61801 (0.219 sec)
INFO:tensorflow:global_step/sec: 474.331
INFO:tensorflow:loss = 0.0071272356, step = 61901 (0.215 sec)
INFO:tensorflow:global_step/sec: 440.575
INFO:tensorflow:loss = 0.00686712, step = 62001 (0.222 sec)
INFO:tensorflow:global_step/sec: 461.446
INFO:tensorflow:loss = 0.010292519, step = 62101 (0.217 sec)
INFO:tensorflow:global_step/sec: 440.387
INFO:tensorflow:loss = 0.0039625755, step = 62201 (0.227 sec)
INFO:tensorflow:global_step/sec: 452.014
INFO:tensorflow:loss = 0.0054634213, step = 62301 (0.221 sec)
INFO:tensorflow:global_step/sec: 438.016
INFO:tensorflow:loss = 0.0035767714, step = 62401 (0.228 sec)
INFO:tensorflow:global_step/sec: 468.766
INFO:tensorflow:loss = 0.009523136, step = 62501 (0.214 sec)
INFO:tensorflow:global_step/sec: 477.288
INFO:tensorflow:loss = 0.0035087317, step = 62601 (0.209 sec)
INFO:tensorflow:global_step/sec: 452.339
INFO:tensorflow:loss = 0.008077238, step = 62701 (0.221 sec)
INFO:tensorflow:global_step/sec: 467.189
INFO:tensorflow:loss = 0.0061389813, step = 62801 (0.214 sec)
INFO:tensorflow:global_step/sec: 449.372
INFO:tensorflow:loss = 0.006167573, step = 62901 (0.222 sec)
INFO:tensorflow:global_step/sec: 434.509
INFO:tensorflow:loss = 0.0056978366, step = 63001 (0.230 sec)
INFO:tensorflow:global_step/sec: 459.705
INFO:tensorflow:loss = 0.00419854, step = 63101 (0.217 sec)
INFO:tensorflow:global_step/sec: 447.265
INFO:tensorflow:loss = 0.007870656, step = 63201 (0.224 sec)
INFO:tensorflow:global_step/sec: 471.963
INFO:tensorflow:loss = 0.0106264595, step = 63301 (0.212 sec)
INFO:tensorflow:global_step/sec: 474.908
INFO:tensorflow:loss = 0.009301414, step = 63401 (0.211 sec)
INFO:tensorflow:global_step/sec: 464.17
INFO:tensorflow:loss = 0.011040909, step = 63501 (0.215 sec)
INFO:tensorflow:global_step/sec: 457.779
INFO:tensorflow:loss = 0.0058821742, step = 63601 (0.218 sec)
INFO:tensorflow:global_step/sec: 460.431
INFO:tensorflow:loss = 0.006685866, step = 63701 (0.217 sec)
INFO:tensorflow:global_step/sec: 438.225
INFO:tensorflow:loss = 0.007290181, step = 63801 (0.228 sec)
INFO:tensorflow:global_step/sec: 444.638
INFO:tensorflow:loss = 0.0045587993, step = 63901 (0.225 sec)
INFO:tensorflow:global_step/sec: 452.941
INFO:tensorflow:loss = 0.006104091, step = 64001 (0.220 sec)
INFO:tensorflow:global_step/sec: 461.704
INFO:tensorflow:loss = 0.0055608135, step = 64101 (0.217 sec)
INFO:tensorflow:global_step/sec: 434.471
INFO:tensorflow:loss = 0.0045667156, step = 64201 (0.230 sec)
INFO:tensorflow:global_step/sec: 446.854
INFO:tensorflow:loss = 0.0035601123, step = 64301 (0.224 sec)
INFO:tensorflow:global_step/sec: 429.236
INFO:tensorflow:loss = 0.007950223, step = 64401 (0.233 sec)
INFO:tensorflow:global_step/sec: 463.349
INFO:tensorflow:loss = 0.00501996, step = 64501 (0.216 sec)
INFO:tensorflow:global_step/sec: 463.545
INFO:tensorflow:loss = 0.00829908, step = 64601 (0.216 sec)
INFO:tensorflow:global_step/sec: 445.543
INFO:tensorflow:loss = 0.0058983793, step = 64701 (0.224 sec)
INFO:tensorflow:global_step/sec: 440.191
INFO:tensorflow:loss = 0.005716239, step = 64801 (0.227 sec)
INFO:tensorflow:global_step/sec: 444.828
INFO:tensorflow:loss = 0.0063681947, step = 64901 (0.224 sec)
INFO:tensorflow:global_step/sec: 475.69
INFO:tensorflow:loss = 0.005070969, step = 65001 (0.211 sec)
INFO:tensorflow:global_step/sec: 430.246
INFO:tensorflow:loss = 0.0031899558, step = 65101 (0.232 sec)
INFO:tensorflow:global_step/sec: 457.119
INFO:tensorflow:loss = 0.003931294, step = 65201 (0.219 sec)
INFO:tensorflow:global_step/sec: 459.741
INFO:tensorflow:loss = 0.008194013, step = 65301 (0.218 sec)
INFO:tensorflow:global_step/sec: 456.366
INFO:tensorflow:loss = 0.005469339, step = 65401 (0.219 sec)
INFO:tensorflow:global_step/sec: 471.247
INFO:tensorflow:loss = 0.0035420237, step = 65501 (0.216 sec)
INFO:tensorflow:global_step/sec: 450.258
INFO:tensorflow:loss = 0.007944604, step = 65601 (0.219 sec)
INFO:tensorflow:global_step/sec: 475.83
INFO:tensorflow:loss = 0.0055986336, step = 65701 (0.210 sec)
INFO:tensorflow:global_step/sec: 456.033
INFO:tensorflow:loss = 0.005359305, step = 65801 (0.219 sec)
INFO:tensorflow:global_step/sec: 467.284
INFO:tensorflow:loss = 0.005838528, step = 65901 (0.214 sec)
INFO:tensorflow:global_step/sec: 472.835
INFO:tensorflow:loss = 0.008952239, step = 66001 (0.211 sec)
INFO:tensorflow:global_step/sec: 458.14
INFO:tensorflow:loss = 0.008125879, step = 66101 (0.218 sec)
INFO:tensorflow:global_step/sec: 457.767
INFO:tensorflow:loss = 0.010595839, step = 66201 (0.219 sec)
INFO:tensorflow:global_step/sec: 462.086
INFO:tensorflow:loss = 0.004360906, step = 66301 (0.216 sec)
INFO:tensorflow:global_step/sec: 444.026
INFO:tensorflow:loss = 0.005472458, step = 66401 (0.225 sec)
INFO:tensorflow:global_step/sec: 451.127
INFO:tensorflow:loss = 0.009706709, step = 66501 (0.221 sec)
INFO:tensorflow:global_step/sec: 481.362
INFO:tensorflow:loss = 0.0058737276, step = 66601 (0.208 sec)
INFO:tensorflow:global_step/sec: 448.974
INFO:tensorflow:loss = 0.005375354, step = 66701 (0.223 sec)
INFO:tensorflow:global_step/sec: 453.542
INFO:tensorflow:loss = 0.0051962878, step = 66801 (0.220 sec)
INFO:tensorflow:global_step/sec: 455.828
INFO:tensorflow:loss = 0.004631044, step = 66901 (0.219 sec)
INFO:tensorflow:global_step/sec: 467.034
INFO:tensorflow:loss = 0.0036607399, step = 67001 (0.214 sec)
INFO:tensorflow:global_step/sec: 447.937
INFO:tensorflow:loss = 0.0042718584, step = 67101 (0.224 sec)
INFO:tensorflow:global_step/sec: 464.857
INFO:tensorflow:loss = 0.0060365, step = 67201 (0.215 sec)
INFO:tensorflow:global_step/sec: 472.802
INFO:tensorflow:loss = 0.0057770684, step = 67301 (0.211 sec)
INFO:tensorflow:global_step/sec: 464.572
INFO:tensorflow:loss = 0.007338159, step = 67401 (0.215 sec)
INFO:tensorflow:global_step/sec: 450.024
INFO:tensorflow:loss = 0.003209616, step = 67501 (0.222 sec)
INFO:tensorflow:global_step/sec: 402.198
INFO:tensorflow:loss = 0.0061726947, step = 67601 (0.249 sec)
INFO:tensorflow:global_step/sec: 466.459
INFO:tensorflow:loss = 0.010532187, step = 67701 (0.215 sec)
INFO:tensorflow:global_step/sec: 460.095
INFO:tensorflow:loss = 0.0068775183, step = 67801 (0.217 sec)
INFO:tensorflow:global_step/sec: 438.587
INFO:tensorflow:loss = 0.003887407, step = 67901 (0.231 sec)
INFO:tensorflow:global_step/sec: 458.894
INFO:tensorflow:loss = 0.010415395, step = 68001 (0.215 sec)
INFO:tensorflow:global_step/sec: 446.07
INFO:tensorflow:loss = 0.009217195, step = 68101 (0.224 sec)
INFO:tensorflow:global_step/sec: 443.274
INFO:tensorflow:loss = 0.0074221427, step = 68201 (0.225 sec)
INFO:tensorflow:global_step/sec: 456.147
INFO:tensorflow:loss = 0.010360572, step = 68301 (0.219 sec)
INFO:tensorflow:global_step/sec: 442.065
INFO:tensorflow:loss = 0.004218365, step = 68401 (0.226 sec)
INFO:tensorflow:global_step/sec: 446.83
INFO:tensorflow:loss = 0.00443579, step = 68501 (0.224 sec)
INFO:tensorflow:global_step/sec: 464.784
INFO:tensorflow:loss = 0.0053496305, step = 68601 (0.215 sec)
INFO:tensorflow:global_step/sec: 460.521
INFO:tensorflow:loss = 0.0067975875, step = 68701 (0.217 sec)
INFO:tensorflow:global_step/sec: 444.809
INFO:tensorflow:loss = 0.009376096, step = 68801 (0.225 sec)
INFO:tensorflow:global_step/sec: 419.725
INFO:tensorflow:loss = 0.012522965, step = 68901 (0.238 sec)
INFO:tensorflow:global_step/sec: 459.324
INFO:tensorflow:loss = 0.01441093, step = 69001 (0.217 sec)
INFO:tensorflow:global_step/sec: 468.123
INFO:tensorflow:loss = 0.0070652366, step = 69101 (0.214 sec)
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INFO:tensorflow:global_step/sec: 475.367
INFO:tensorflow:loss = 0.003959253, step = 69301 (0.210 sec)
INFO:tensorflow:global_step/sec: 466.879
INFO:tensorflow:loss = 0.006276269, step = 69401 (0.214 sec)
INFO:tensorflow:global_step/sec: 437.938
INFO:tensorflow:loss = 0.0045333365, step = 69501 (0.228 sec)
INFO:tensorflow:global_step/sec: 460.792
INFO:tensorflow:loss = 0.003597036, step = 69601 (0.218 sec)
INFO:tensorflow:global_step/sec: 442.832
INFO:tensorflow:loss = 0.0047999304, step = 69701 (0.226 sec)
INFO:tensorflow:global_step/sec: 445.29
INFO:tensorflow:loss = 0.008004857, step = 69801 (0.225 sec)
INFO:tensorflow:global_step/sec: 460.155
INFO:tensorflow:loss = 0.0075903703, step = 69901 (0.217 sec)
INFO:tensorflow:global_step/sec: 442.654
INFO:tensorflow:loss = 0.008740846, step = 70001 (0.226 sec)
INFO:tensorflow:global_step/sec: 446.397
INFO:tensorflow:loss = 0.005103236, step = 70101 (0.224 sec)
INFO:tensorflow:global_step/sec: 447.263
INFO:tensorflow:loss = 0.0072469544, step = 70201 (0.224 sec)
INFO:tensorflow:global_step/sec: 457.804
INFO:tensorflow:loss = 0.0064546643, step = 70301 (0.218 sec)
INFO:tensorflow:global_step/sec: 460.518
INFO:tensorflow:loss = 0.005093436, step = 70401 (0.217 sec)
INFO:tensorflow:global_step/sec: 456.533
INFO:tensorflow:loss = 0.0053966255, step = 70501 (0.219 sec)
INFO:tensorflow:global_step/sec: 449.873
INFO:tensorflow:loss = 0.0071552694, step = 70601 (0.222 sec)
INFO:tensorflow:global_step/sec: 416.691
INFO:tensorflow:loss = 0.004265731, step = 70701 (0.240 sec)
INFO:tensorflow:global_step/sec: 444.543
INFO:tensorflow:loss = 0.008054085, step = 70801 (0.225 sec)
INFO:tensorflow:global_step/sec: 460.473
INFO:tensorflow:loss = 0.0084706675, step = 70901 (0.217 sec)
INFO:tensorflow:global_step/sec: 438.689
INFO:tensorflow:loss = 0.005538496, step = 71001 (0.228 sec)
INFO:tensorflow:global_step/sec: 453.449
INFO:tensorflow:loss = 0.0076392516, step = 71101 (0.221 sec)
INFO:tensorflow:global_step/sec: 419.511
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INFO:tensorflow:global_step/sec: 447.429
INFO:tensorflow:loss = 0.007264255, step = 71301 (0.223 sec)
INFO:tensorflow:global_step/sec: 440.985
INFO:tensorflow:loss = 0.003469113, step = 71401 (0.227 sec)
INFO:tensorflow:global_step/sec: 437.382
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INFO:tensorflow:global_step/sec: 455.226
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INFO:tensorflow:global_step/sec: 474.978
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INFO:tensorflow:global_step/sec: 465.641
INFO:tensorflow:loss = 0.0049074343, step = 71801 (0.215 sec)
INFO:tensorflow:global_step/sec: 437.264
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INFO:tensorflow:global_step/sec: 460.223
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INFO:tensorflow:global_step/sec: 448.262
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INFO:tensorflow:global_step/sec: 457.884
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INFO:tensorflow:global_step/sec: 477.827
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INFO:tensorflow:global_step/sec: 478.282
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INFO:tensorflow:global_step/sec: 458.951
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INFO:tensorflow:global_step/sec: 422.297
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INFO:tensorflow:global_step/sec: 468.005
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INFO:tensorflow:global_step/sec: 465.378
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INFO:tensorflow:global_step/sec: 461.299
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INFO:tensorflow:global_step/sec: 401.531
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INFO:tensorflow:global_step/sec: 458.901
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INFO:tensorflow:global_step/sec: 461.949
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INFO:tensorflow:global_step/sec: 452.759
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INFO:tensorflow:global_step/sec: 453.655
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INFO:tensorflow:global_step/sec: 442.006
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INFO:tensorflow:global_step/sec: 443.243
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INFO:tensorflow:global_step/sec: 447.971
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INFO:tensorflow:global_step/sec: 430.132
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INFO:tensorflow:global_step/sec: 462.974
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INFO:tensorflow:global_step/sec: 469.074
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INFO:tensorflow:global_step/sec: 444.136
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INFO:tensorflow:global_step/sec: 457.113
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INFO:tensorflow:global_step/sec: 455.869
INFO:tensorflow:loss = 0.0050777653, step = 76001 (0.219 sec)
INFO:tensorflow:global_step/sec: 441.663
INFO:tensorflow:loss = 0.005149127, step = 76101 (0.227 sec)
INFO:tensorflow:global_step/sec: 438.495
INFO:tensorflow:loss = 0.003949683, step = 76201 (0.228 sec)
INFO:tensorflow:global_step/sec: 466.259
INFO:tensorflow:loss = 0.0050260425, step = 76301 (0.214 sec)
INFO:tensorflow:global_step/sec: 472.018
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INFO:tensorflow:global_step/sec: 457.151
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INFO:tensorflow:global_step/sec: 462.717
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INFO:tensorflow:global_step/sec: 463.495
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INFO:tensorflow:global_step/sec: 462.601
INFO:tensorflow:loss = 0.006519336, step = 76801 (0.216 sec)
INFO:tensorflow:global_step/sec: 451.04
INFO:tensorflow:loss = 0.0033873874, step = 76901 (0.222 sec)
INFO:tensorflow:global_step/sec: 461.138
INFO:tensorflow:loss = 0.007071457, step = 77001 (0.217 sec)
INFO:tensorflow:global_step/sec: 450.747
INFO:tensorflow:loss = 0.012458454, step = 77101 (0.222 sec)
INFO:tensorflow:global_step/sec: 447.764
INFO:tensorflow:loss = 0.0050108135, step = 77201 (0.223 sec)
INFO:tensorflow:global_step/sec: 455.951
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INFO:tensorflow:global_step/sec: 449.315
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INFO:tensorflow:global_step/sec: 462.342
INFO:tensorflow:loss = 0.0052490076, step = 77501 (0.216 sec)
INFO:tensorflow:global_step/sec: 440.938
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INFO:tensorflow:global_step/sec: 474.422
INFO:tensorflow:loss = 0.0046195406, step = 77701 (0.210 sec)
INFO:tensorflow:global_step/sec: 451.879
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INFO:tensorflow:global_step/sec: 453.856
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INFO:tensorflow:global_step/sec: 461.973
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INFO:tensorflow:global_step/sec: 455.716
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INFO:tensorflow:global_step/sec: 476.649
INFO:tensorflow:loss = 0.0063690925, step = 78201 (0.210 sec)
INFO:tensorflow:global_step/sec: 438.354
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INFO:tensorflow:global_step/sec: 440.884
INFO:tensorflow:loss = 0.006499151, step = 78401 (0.226 sec)
INFO:tensorflow:global_step/sec: 460.764
INFO:tensorflow:loss = 0.0064125946, step = 78501 (0.217 sec)
INFO:tensorflow:global_step/sec: 438.978
INFO:tensorflow:loss = 0.0055036284, step = 78601 (0.228 sec)
INFO:tensorflow:global_step/sec: 457.536
INFO:tensorflow:loss = 0.004464712, step = 78701 (0.219 sec)
INFO:tensorflow:global_step/sec: 467.041
INFO:tensorflow:loss = 0.006037531, step = 78801 (0.214 sec)
INFO:tensorflow:global_step/sec: 467.284
INFO:tensorflow:loss = 0.009124339, step = 78901 (0.214 sec)
INFO:tensorflow:global_step/sec: 445.984
INFO:tensorflow:loss = 0.0064703375, step = 79001 (0.224 sec)
INFO:tensorflow:global_step/sec: 415.031
INFO:tensorflow:loss = 0.006439813, step = 79101 (0.241 sec)
INFO:tensorflow:global_step/sec: 426.452
INFO:tensorflow:loss = 0.0041991677, step = 79201 (0.235 sec)
INFO:tensorflow:global_step/sec: 447.958
INFO:tensorflow:loss = 0.0046974616, step = 79301 (0.223 sec)
INFO:tensorflow:global_step/sec: 472.65
INFO:tensorflow:loss = 0.0066093374, step = 79401 (0.212 sec)
INFO:tensorflow:global_step/sec: 499.753
INFO:tensorflow:loss = 0.0034619216, step = 79501 (0.200 sec)
INFO:tensorflow:global_step/sec: 477.662
INFO:tensorflow:loss = 0.006463375, step = 79601 (0.209 sec)
INFO:tensorflow:global_step/sec: 474.841
INFO:tensorflow:loss = 0.0044739046, step = 79701 (0.211 sec)
INFO:tensorflow:global_step/sec: 459.107
INFO:tensorflow:loss = 0.0049773594, step = 79801 (0.218 sec)
INFO:tensorflow:global_step/sec: 454.759
INFO:tensorflow:loss = 0.007405264, step = 79901 (0.220 sec)
INFO:tensorflow:Saving checkpoints for 80000 into /tmp/tmpDexXZd/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-2.txt: ['0:1_layer_dnn', '1:2_layer_dnn', '2:3_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Building iteration 3
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:32:02
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpDexXZd/model.ckpt-80000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't2_3_layer_dnn' dict for global step 80000: architecture/adanet/ensembles =
}
>adanet/iteration_2/ensemble_t2_3_layer_dnn/architecture/adanetB1 B+| 1_layer_dnn | 2_layer_dnn | 3_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.030102361, average_loss/adanet/subnetwork = 0.032910354, average_loss/adanet/uniform_average_ensemble = 0.03231746, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.038314007, loss/adanet/subnetwork = 0.043785788, loss/adanet/uniform_average_ensemble = 0.04384736, prediction/mean/adanet/adanet_weighted_ensemble = 3.1021514, prediction/mean/adanet/subnetwork = 3.134045, prediction/mean/adanet/uniform_average_ensemble = 3.1457782
INFO:tensorflow:Saving candidate 't3_3_layer_dnn' dict for global step 80000: architecture/adanet/ensembles =
�
>adanet/iteration_3/ensemble_t3_3_layer_dnn/architecture/adanetB? B9| 1_layer_dnn | 2_layer_dnn | 3_layer_dnn | 3_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.032788813, average_loss/adanet/subnetwork = 0.03740776, average_loss/adanet/uniform_average_ensemble = 0.032510772, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.039163962, loss/adanet/subnetwork = 0.055050053, loss/adanet/uniform_average_ensemble = 0.04530649, prediction/mean/adanet/adanet_weighted_ensemble = 3.0593674, prediction/mean/adanet/subnetwork = 3.1547055, prediction/mean/adanet/uniform_average_ensemble = 3.1480103
INFO:tensorflow:Saving candidate 't3_4_layer_dnn' dict for global step 80000: architecture/adanet/ensembles =
�
>adanet/iteration_3/ensemble_t3_4_layer_dnn/architecture/adanetB? B9| 1_layer_dnn | 2_layer_dnn | 3_layer_dnn | 4_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.03169271, average_loss/adanet/subnetwork = 0.03348904, average_loss/adanet/uniform_average_ensemble = 0.031587753, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.038267143, loss/adanet/subnetwork = 0.041055106, loss/adanet/uniform_average_ensemble = 0.04207115, prediction/mean/adanet/adanet_weighted_ensemble = 3.067704, prediction/mean/adanet/subnetwork = 3.1287208, prediction/mean/adanet/uniform_average_ensemble = 3.1415138
INFO:tensorflow:Finished evaluation at 2018-12-13-19:32:06
INFO:tensorflow:Saving dict for global step 80000: average_loss = 0.03169271, average_loss/adanet/adanet_weighted_ensemble = 0.03169271, average_loss/adanet/subnetwork = 0.03348904, average_loss/adanet/uniform_average_ensemble = 0.031587753, global_step = 80000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.038267143, loss/adanet/adanet_weighted_ensemble = 0.038267143, loss/adanet/subnetwork = 0.041055106, loss/adanet/uniform_average_ensemble = 0.04207115, prediction/mean = 3.067704, prediction/mean/adanet/adanet_weighted_ensemble = 3.067704, prediction/mean/adanet/subnetwork = 3.1287208, prediction/mean/adanet/uniform_average_ensemble = 3.1415138
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 80000: /tmp/tmpDexXZd/model.ckpt-80000
INFO:tensorflow:Loss for final step: 0.0047632405.
INFO:tensorflow:Finished training Adanet iteration 3
INFO:tensorflow:Beginning bookkeeping phase for iteration 3
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-2.txt: ['0:1_layer_dnn', '1:2_layer_dnn', '2:3_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Building iteration 3
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Starting ensemble evaluation for iteration 3
INFO:tensorflow:Restoring parameters from /tmp/tmpDexXZd/model.ckpt-80000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Encountered end of input after 14 evaluations
INFO:tensorflow:Computed ensemble metrics: adanet_loss/t2_3_layer_dnn = 0.007307, adanet_loss/t3_3_layer_dnn = 0.006105, adanet_loss/t3_4_layer_dnn = 0.005626
INFO:tensorflow:Finished ensemble evaluation for iteration 3
INFO:tensorflow:'t3_4_layer_dnn' at index 2 is moving onto the next iteration
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-3.txt: ['0:1_layer_dnn', '1:2_layer_dnn', '2:3_layer_dnn', '3:4_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Rebuilding iteration 3
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Warm-starting from: (u'/tmp/tmpDexXZd/model.ckpt-80000',)
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_3/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_2/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_3/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_3_layer_dnn/adanet/iteration_2/candidate_t2_3_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_3_layer_dnn/adanet/iteration_2/candidate_t2_3_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t3_4_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t3_4_layer_dnn/adanet/iteration_3/candidate_t3_4_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_4/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: global_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t3_4_layer_dnn/adanet/iteration_3/candidate_t3_4_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_3_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_4/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_3/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t2_3_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_1_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_2_layer_dnn/adanet/iteration_1/candidate_t1_2_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_2_layer_dnn/adanet/iteration_2/candidate_t1_2_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_1_layer_dnn/adanet/iteration_1/candidate_t0_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_4_layer_dnn/weighted_subnetwork_3/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_2_layer_dnn/weighted_subnetwork_1/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t2_3_layer_dnn/adanet/iteration_3/candidate_t2_3_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_3/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t2_3_layer_dnn/adanet/iteration_3/candidate_t2_3_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_2_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_2_layer_dnn/adanet/iteration_2/candidate_t1_2_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_3_layer_dnn/weighted_subnetwork_2/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Building iteration 4
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Building subnetwork '5_layer_dnn'
INFO:tensorflow:Overwriting checkpoint with new graph for iteration 4 to /tmp/tmpDexXZd/model.ckpt-80000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Finished bookkeeping phase for iteration 3
INFO:tensorflow:Beginning training AdaNet iteration 4
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-3.txt: ['0:1_layer_dnn', '1:2_layer_dnn', '2:3_layer_dnn', '3:4_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Rebuilding iteration 3
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Building iteration 4
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Building subnetwork '5_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpDexXZd/increment.ckpt-4
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 80000 into /tmp/tmpDexXZd/model.ckpt.
INFO:tensorflow:loss = 0.005383206, step = 80001
INFO:tensorflow:global_step/sec: 78.49
INFO:tensorflow:loss = 0.0064166253, step = 80101 (1.275 sec)
INFO:tensorflow:global_step/sec: 418.887
INFO:tensorflow:loss = 0.008984626, step = 80201 (0.238 sec)
INFO:tensorflow:global_step/sec: 405.903
INFO:tensorflow:loss = 0.004674765, step = 80301 (0.246 sec)
INFO:tensorflow:global_step/sec: 406.285
INFO:tensorflow:loss = 0.00909529, step = 80401 (0.246 sec)
INFO:tensorflow:global_step/sec: 431.88
INFO:tensorflow:loss = 0.002272259, step = 80501 (0.232 sec)
INFO:tensorflow:global_step/sec: 430.737
INFO:tensorflow:loss = 0.0045274086, step = 80601 (0.232 sec)
INFO:tensorflow:global_step/sec: 413.445
INFO:tensorflow:loss = 0.0054803616, step = 80701 (0.242 sec)
INFO:tensorflow:global_step/sec: 416.309
INFO:tensorflow:loss = 0.0052720383, step = 80801 (0.240 sec)
INFO:tensorflow:global_step/sec: 400.421
INFO:tensorflow:loss = 0.008549853, step = 80901 (0.250 sec)
INFO:tensorflow:global_step/sec: 408.361
INFO:tensorflow:loss = 0.008121803, step = 81001 (0.244 sec)
INFO:tensorflow:global_step/sec: 414.845
INFO:tensorflow:loss = 0.0069520036, step = 81101 (0.241 sec)
INFO:tensorflow:global_step/sec: 408.255
INFO:tensorflow:loss = 0.0076366886, step = 81201 (0.245 sec)
INFO:tensorflow:global_step/sec: 421.117
INFO:tensorflow:loss = 0.003278641, step = 81301 (0.237 sec)
INFO:tensorflow:global_step/sec: 429.802
INFO:tensorflow:loss = 0.0028751101, step = 81401 (0.238 sec)
INFO:tensorflow:global_step/sec: 420.822
INFO:tensorflow:loss = 0.0037532323, step = 81501 (0.232 sec)
INFO:tensorflow:global_step/sec: 436.449
INFO:tensorflow:loss = 0.0046784272, step = 81601 (0.229 sec)
INFO:tensorflow:global_step/sec: 413.609
INFO:tensorflow:loss = 0.007498023, step = 81701 (0.242 sec)
INFO:tensorflow:global_step/sec: 436.12
INFO:tensorflow:loss = 0.0050562844, step = 81801 (0.229 sec)
INFO:tensorflow:global_step/sec: 451.214
INFO:tensorflow:loss = 0.0062173824, step = 81901 (0.222 sec)
INFO:tensorflow:global_step/sec: 431.908
INFO:tensorflow:loss = 0.0070055285, step = 82001 (0.232 sec)
INFO:tensorflow:global_step/sec: 446.57
INFO:tensorflow:loss = 0.0067753876, step = 82101 (0.224 sec)
INFO:tensorflow:global_step/sec: 433.507
INFO:tensorflow:loss = 0.0034281143, step = 82201 (0.231 sec)
INFO:tensorflow:global_step/sec: 420.337
INFO:tensorflow:loss = 0.004406165, step = 82301 (0.238 sec)
INFO:tensorflow:global_step/sec: 438.057
INFO:tensorflow:loss = 0.0028600814, step = 82401 (0.228 sec)
INFO:tensorflow:global_step/sec: 417.812
INFO:tensorflow:loss = 0.005227585, step = 82501 (0.239 sec)
INFO:tensorflow:global_step/sec: 423.689
INFO:tensorflow:loss = 0.0023829981, step = 82601 (0.236 sec)
INFO:tensorflow:global_step/sec: 439.427
INFO:tensorflow:loss = 0.0073684314, step = 82701 (0.227 sec)
INFO:tensorflow:global_step/sec: 450.432
INFO:tensorflow:loss = 0.005705492, step = 82801 (0.222 sec)
INFO:tensorflow:global_step/sec: 436.91
INFO:tensorflow:loss = 0.005273375, step = 82901 (0.229 sec)
INFO:tensorflow:global_step/sec: 424.995
INFO:tensorflow:loss = 0.0046031997, step = 83001 (0.235 sec)
INFO:tensorflow:global_step/sec: 429.823
INFO:tensorflow:loss = 0.0045557586, step = 83101 (0.233 sec)
INFO:tensorflow:global_step/sec: 439.452
INFO:tensorflow:loss = 0.005674702, step = 83201 (0.227 sec)
INFO:tensorflow:global_step/sec: 437.522
INFO:tensorflow:loss = 0.0070310174, step = 83301 (0.229 sec)
INFO:tensorflow:global_step/sec: 435.981
INFO:tensorflow:loss = 0.008453, step = 83401 (0.229 sec)
INFO:tensorflow:global_step/sec: 430.563
INFO:tensorflow:loss = 0.0069910833, step = 83501 (0.232 sec)
INFO:tensorflow:global_step/sec: 449.976
INFO:tensorflow:loss = 0.0055006915, step = 83601 (0.222 sec)
INFO:tensorflow:global_step/sec: 432.556
INFO:tensorflow:loss = 0.006325035, step = 83701 (0.231 sec)
INFO:tensorflow:global_step/sec: 427.639
INFO:tensorflow:loss = 0.007590879, step = 83801 (0.234 sec)
INFO:tensorflow:global_step/sec: 410.661
INFO:tensorflow:loss = 0.005499037, step = 83901 (0.244 sec)
INFO:tensorflow:global_step/sec: 436.698
INFO:tensorflow:loss = 0.004665518, step = 84001 (0.229 sec)
INFO:tensorflow:global_step/sec: 442.345
INFO:tensorflow:loss = 0.0043933718, step = 84101 (0.226 sec)
INFO:tensorflow:global_step/sec: 419.928
INFO:tensorflow:loss = 0.0033534751, step = 84201 (0.238 sec)
INFO:tensorflow:global_step/sec: 404.658
INFO:tensorflow:loss = 0.004103374, step = 84301 (0.247 sec)
INFO:tensorflow:global_step/sec: 405.912
INFO:tensorflow:loss = 0.007383922, step = 84401 (0.247 sec)
INFO:tensorflow:global_step/sec: 413.824
INFO:tensorflow:loss = 0.004536817, step = 84501 (0.242 sec)
INFO:tensorflow:global_step/sec: 438.612
INFO:tensorflow:loss = 0.008081801, step = 84601 (0.228 sec)
INFO:tensorflow:global_step/sec: 434.239
INFO:tensorflow:loss = 0.0052011255, step = 84701 (0.230 sec)
INFO:tensorflow:global_step/sec: 410.951
INFO:tensorflow:loss = 0.0058519435, step = 84801 (0.243 sec)
INFO:tensorflow:global_step/sec: 424.944
INFO:tensorflow:loss = 0.0044878363, step = 84901 (0.235 sec)
INFO:tensorflow:global_step/sec: 434.751
INFO:tensorflow:loss = 0.00481012, step = 85001 (0.230 sec)
INFO:tensorflow:global_step/sec: 423.553
INFO:tensorflow:loss = 0.0026486877, step = 85101 (0.236 sec)
INFO:tensorflow:global_step/sec: 421.31
INFO:tensorflow:loss = 0.0034918466, step = 85201 (0.238 sec)
INFO:tensorflow:global_step/sec: 430.698
INFO:tensorflow:loss = 0.00640889, step = 85301 (0.232 sec)
INFO:tensorflow:global_step/sec: 408.754
INFO:tensorflow:loss = 0.0045417673, step = 85401 (0.244 sec)
INFO:tensorflow:global_step/sec: 424.719
INFO:tensorflow:loss = 0.0033178735, step = 85501 (0.236 sec)
INFO:tensorflow:global_step/sec: 425.554
INFO:tensorflow:loss = 0.005359968, step = 85601 (0.235 sec)
INFO:tensorflow:global_step/sec: 414.381
INFO:tensorflow:loss = 0.0055321874, step = 85701 (0.241 sec)
INFO:tensorflow:global_step/sec: 408.661
INFO:tensorflow:loss = 0.004864615, step = 85801 (0.245 sec)
INFO:tensorflow:global_step/sec: 438.687
INFO:tensorflow:loss = 0.005201213, step = 85901 (0.228 sec)
INFO:tensorflow:global_step/sec: 425.644
INFO:tensorflow:loss = 0.006251228, step = 86001 (0.239 sec)
INFO:tensorflow:global_step/sec: 429.677
INFO:tensorflow:loss = 0.006049957, step = 86101 (0.228 sec)
INFO:tensorflow:global_step/sec: 453.875
INFO:tensorflow:loss = 0.008047962, step = 86201 (0.221 sec)
INFO:tensorflow:global_step/sec: 438.164
INFO:tensorflow:loss = 0.0037406448, step = 86301 (0.228 sec)
INFO:tensorflow:global_step/sec: 447.966
INFO:tensorflow:loss = 0.0044812597, step = 86401 (0.223 sec)
INFO:tensorflow:global_step/sec: 432.292
INFO:tensorflow:loss = 0.006492268, step = 86501 (0.231 sec)
INFO:tensorflow:global_step/sec: 414.309
INFO:tensorflow:loss = 0.0048469296, step = 86601 (0.241 sec)
INFO:tensorflow:global_step/sec: 420.226
INFO:tensorflow:loss = 0.004090667, step = 86701 (0.238 sec)
INFO:tensorflow:global_step/sec: 431.527
INFO:tensorflow:loss = 0.004442987, step = 86801 (0.233 sec)
INFO:tensorflow:global_step/sec: 422.69
INFO:tensorflow:loss = 0.0048192637, step = 86901 (0.236 sec)
INFO:tensorflow:global_step/sec: 411.562
INFO:tensorflow:loss = 0.0032640456, step = 87001 (0.243 sec)
INFO:tensorflow:global_step/sec: 430.037
INFO:tensorflow:loss = 0.0036541175, step = 87101 (0.233 sec)
INFO:tensorflow:global_step/sec: 446.024
INFO:tensorflow:loss = 0.0069467165, step = 87201 (0.224 sec)
INFO:tensorflow:global_step/sec: 436.506
INFO:tensorflow:loss = 0.00457615, step = 87301 (0.229 sec)
INFO:tensorflow:global_step/sec: 425.08
INFO:tensorflow:loss = 0.006205321, step = 87401 (0.235 sec)
INFO:tensorflow:global_step/sec: 437.602
INFO:tensorflow:loss = 0.0030969642, step = 87501 (0.228 sec)
INFO:tensorflow:global_step/sec: 416.349
INFO:tensorflow:loss = 0.0064918445, step = 87601 (0.240 sec)
INFO:tensorflow:global_step/sec: 433.996
INFO:tensorflow:loss = 0.011695679, step = 87701 (0.230 sec)
INFO:tensorflow:global_step/sec: 426.047
INFO:tensorflow:loss = 0.006250561, step = 87801 (0.235 sec)
INFO:tensorflow:global_step/sec: 425.378
INFO:tensorflow:loss = 0.00363587, step = 87901 (0.235 sec)
INFO:tensorflow:global_step/sec: 436.102
INFO:tensorflow:loss = 0.0072322786, step = 88001 (0.229 sec)
INFO:tensorflow:global_step/sec: 387.201
INFO:tensorflow:loss = 0.009675448, step = 88101 (0.258 sec)
INFO:tensorflow:global_step/sec: 422.52
INFO:tensorflow:loss = 0.004536283, step = 88201 (0.237 sec)
INFO:tensorflow:global_step/sec: 433.711
INFO:tensorflow:loss = 0.009590596, step = 88301 (0.230 sec)
INFO:tensorflow:global_step/sec: 441.24
INFO:tensorflow:loss = 0.0032862434, step = 88401 (0.227 sec)
INFO:tensorflow:global_step/sec: 440.67
INFO:tensorflow:loss = 0.0051202993, step = 88501 (0.227 sec)
INFO:tensorflow:global_step/sec: 415.745
INFO:tensorflow:loss = 0.0040213135, step = 88601 (0.241 sec)
INFO:tensorflow:global_step/sec: 423.725
INFO:tensorflow:loss = 0.008824434, step = 88701 (0.236 sec)
INFO:tensorflow:global_step/sec: 427.93
INFO:tensorflow:loss = 0.008001704, step = 88801 (0.234 sec)
INFO:tensorflow:global_step/sec: 418.417
INFO:tensorflow:loss = 0.010184696, step = 88901 (0.239 sec)
INFO:tensorflow:global_step/sec: 437.29
INFO:tensorflow:loss = 0.011775006, step = 89001 (0.229 sec)
INFO:tensorflow:global_step/sec: 426.165
INFO:tensorflow:loss = 0.0058216797, step = 89101 (0.234 sec)
INFO:tensorflow:global_step/sec: 422.801
INFO:tensorflow:loss = 0.0043885754, step = 89201 (0.237 sec)
INFO:tensorflow:global_step/sec: 393.295
INFO:tensorflow:loss = 0.0027851185, step = 89301 (0.254 sec)
INFO:tensorflow:global_step/sec: 427.192
INFO:tensorflow:loss = 0.0047693453, step = 89401 (0.234 sec)
INFO:tensorflow:global_step/sec: 412.793
INFO:tensorflow:loss = 0.003990461, step = 89501 (0.242 sec)
INFO:tensorflow:global_step/sec: 453.507
INFO:tensorflow:loss = 0.0026294854, step = 89601 (0.220 sec)
INFO:tensorflow:global_step/sec: 436.153
INFO:tensorflow:loss = 0.0052362364, step = 89701 (0.229 sec)
INFO:tensorflow:global_step/sec: 444.735
INFO:tensorflow:loss = 0.009088694, step = 89801 (0.225 sec)
INFO:tensorflow:global_step/sec: 433.372
INFO:tensorflow:loss = 0.005390249, step = 89901 (0.231 sec)
INFO:tensorflow:global_step/sec: 439.168
INFO:tensorflow:loss = 0.007205799, step = 90001 (0.227 sec)
INFO:tensorflow:global_step/sec: 435.44
INFO:tensorflow:loss = 0.003689984, step = 90101 (0.230 sec)
INFO:tensorflow:global_step/sec: 428.245
INFO:tensorflow:loss = 0.0054083467, step = 90201 (0.234 sec)
INFO:tensorflow:global_step/sec: 409.696
INFO:tensorflow:loss = 0.005978807, step = 90301 (0.244 sec)
INFO:tensorflow:global_step/sec: 431.561
INFO:tensorflow:loss = 0.0036984396, step = 90401 (0.232 sec)
INFO:tensorflow:global_step/sec: 442.607
INFO:tensorflow:loss = 0.0044141123, step = 90501 (0.226 sec)
INFO:tensorflow:global_step/sec: 439.182
INFO:tensorflow:loss = 0.0047680545, step = 90601 (0.228 sec)
INFO:tensorflow:global_step/sec: 450.201
INFO:tensorflow:loss = 0.0034539485, step = 90701 (0.222 sec)
INFO:tensorflow:global_step/sec: 437.723
INFO:tensorflow:loss = 0.008106205, step = 90801 (0.228 sec)
INFO:tensorflow:global_step/sec: 421.153
INFO:tensorflow:loss = 0.006459282, step = 90901 (0.237 sec)
INFO:tensorflow:global_step/sec: 440.875
INFO:tensorflow:loss = 0.0059008165, step = 91001 (0.227 sec)
INFO:tensorflow:global_step/sec: 449.115
INFO:tensorflow:loss = 0.0076343603, step = 91101 (0.223 sec)
INFO:tensorflow:global_step/sec: 417.472
INFO:tensorflow:loss = 0.0036134154, step = 91201 (0.240 sec)
INFO:tensorflow:global_step/sec: 413.1
INFO:tensorflow:loss = 0.008169176, step = 91301 (0.242 sec)
INFO:tensorflow:global_step/sec: 438.721
INFO:tensorflow:loss = 0.0027639198, step = 91401 (0.228 sec)
INFO:tensorflow:global_step/sec: 426.892
INFO:tensorflow:loss = 0.0072495104, step = 91501 (0.234 sec)
INFO:tensorflow:global_step/sec: 431.559
INFO:tensorflow:loss = 0.002784501, step = 91601 (0.232 sec)
INFO:tensorflow:global_step/sec: 424.739
INFO:tensorflow:loss = 0.008173542, step = 91701 (0.235 sec)
INFO:tensorflow:global_step/sec: 430.228
INFO:tensorflow:loss = 0.0045573693, step = 91801 (0.233 sec)
INFO:tensorflow:global_step/sec: 422.408
INFO:tensorflow:loss = 0.0052920775, step = 91901 (0.237 sec)
INFO:tensorflow:global_step/sec: 442.752
INFO:tensorflow:loss = 0.004408728, step = 92001 (0.226 sec)
INFO:tensorflow:global_step/sec: 419.248
INFO:tensorflow:loss = 0.0039077876, step = 92101 (0.239 sec)
INFO:tensorflow:global_step/sec: 428.701
INFO:tensorflow:loss = 0.0029403642, step = 92201 (0.233 sec)
INFO:tensorflow:global_step/sec: 432.794
INFO:tensorflow:loss = 0.004977732, step = 92301 (0.231 sec)
INFO:tensorflow:global_step/sec: 432.111
INFO:tensorflow:loss = 0.005024727, step = 92401 (0.231 sec)
INFO:tensorflow:global_step/sec: 418.994
INFO:tensorflow:loss = 0.005117008, step = 92501 (0.239 sec)
INFO:tensorflow:global_step/sec: 424.268
INFO:tensorflow:loss = 0.002897, step = 92601 (0.236 sec)
INFO:tensorflow:global_step/sec: 426.479
INFO:tensorflow:loss = 0.0075298087, step = 92701 (0.235 sec)
INFO:tensorflow:global_step/sec: 421.681
INFO:tensorflow:loss = 0.007339681, step = 92801 (0.236 sec)
INFO:tensorflow:global_step/sec: 427.679
INFO:tensorflow:loss = 0.008566435, step = 92901 (0.234 sec)
INFO:tensorflow:global_step/sec: 404.852
INFO:tensorflow:loss = 0.009024516, step = 93001 (0.247 sec)
INFO:tensorflow:global_step/sec: 448.017
INFO:tensorflow:loss = 0.002813141, step = 93101 (0.224 sec)
INFO:tensorflow:global_step/sec: 432.223
INFO:tensorflow:loss = 0.0032254832, step = 93201 (0.230 sec)
INFO:tensorflow:global_step/sec: 438.295
INFO:tensorflow:loss = 0.005016174, step = 93301 (0.228 sec)
INFO:tensorflow:global_step/sec: 379.638
INFO:tensorflow:loss = 0.005437582, step = 93401 (0.266 sec)
INFO:tensorflow:global_step/sec: 393.871
INFO:tensorflow:loss = 0.0030894382, step = 93501 (0.251 sec)
INFO:tensorflow:global_step/sec: 415.015
INFO:tensorflow:loss = 0.008313053, step = 93601 (0.241 sec)
INFO:tensorflow:global_step/sec: 411.137
INFO:tensorflow:loss = 0.0036248101, step = 93701 (0.243 sec)
INFO:tensorflow:global_step/sec: 402.031
INFO:tensorflow:loss = 0.00810652, step = 93801 (0.251 sec)
INFO:tensorflow:global_step/sec: 408.493
INFO:tensorflow:loss = 0.004963128, step = 93901 (0.242 sec)
INFO:tensorflow:global_step/sec: 411.645
INFO:tensorflow:loss = 0.0043365946, step = 94001 (0.243 sec)
INFO:tensorflow:global_step/sec: 429.109
INFO:tensorflow:loss = 0.0077144424, step = 94101 (0.233 sec)
INFO:tensorflow:global_step/sec: 427.771
INFO:tensorflow:loss = 0.004766725, step = 94201 (0.234 sec)
INFO:tensorflow:global_step/sec: 414.778
INFO:tensorflow:loss = 0.0045494647, step = 94301 (0.241 sec)
INFO:tensorflow:global_step/sec: 438.844
INFO:tensorflow:loss = 0.003908638, step = 94401 (0.228 sec)
INFO:tensorflow:global_step/sec: 426.882
INFO:tensorflow:loss = 0.0039011678, step = 94501 (0.234 sec)
INFO:tensorflow:global_step/sec: 434.135
INFO:tensorflow:loss = 0.004798631, step = 94601 (0.231 sec)
INFO:tensorflow:global_step/sec: 412.807
INFO:tensorflow:loss = 0.0052594873, step = 94701 (0.242 sec)
INFO:tensorflow:global_step/sec: 438.564
INFO:tensorflow:loss = 0.00830613, step = 94801 (0.231 sec)
INFO:tensorflow:global_step/sec: 450.031
INFO:tensorflow:loss = 0.0026800362, step = 94901 (0.219 sec)
INFO:tensorflow:global_step/sec: 439.932
INFO:tensorflow:loss = 0.010185981, step = 95001 (0.228 sec)
INFO:tensorflow:global_step/sec: 429.138
INFO:tensorflow:loss = 0.0050802436, step = 95101 (0.233 sec)
INFO:tensorflow:global_step/sec: 437.193
INFO:tensorflow:loss = 0.0063868132, step = 95201 (0.228 sec)
INFO:tensorflow:global_step/sec: 410.026
INFO:tensorflow:loss = 0.009593469, step = 95301 (0.244 sec)
INFO:tensorflow:global_step/sec: 410.539
INFO:tensorflow:loss = 0.00674426, step = 95401 (0.243 sec)
INFO:tensorflow:global_step/sec: 417.308
INFO:tensorflow:loss = 0.010876091, step = 95501 (0.240 sec)
INFO:tensorflow:global_step/sec: 427.914
INFO:tensorflow:loss = 0.005005857, step = 95601 (0.234 sec)
INFO:tensorflow:global_step/sec: 419.595
INFO:tensorflow:loss = 0.0024972835, step = 95701 (0.238 sec)
INFO:tensorflow:global_step/sec: 427.427
INFO:tensorflow:loss = 0.008742824, step = 95801 (0.234 sec)
INFO:tensorflow:global_step/sec: 408.218
INFO:tensorflow:loss = 0.0036342437, step = 95901 (0.245 sec)
INFO:tensorflow:global_step/sec: 430.354
INFO:tensorflow:loss = 0.0037122234, step = 96001 (0.232 sec)
INFO:tensorflow:global_step/sec: 432.266
INFO:tensorflow:loss = 0.0037878011, step = 96101 (0.232 sec)
INFO:tensorflow:global_step/sec: 423.361
INFO:tensorflow:loss = 0.0027226722, step = 96201 (0.235 sec)
INFO:tensorflow:global_step/sec: 418.174
INFO:tensorflow:loss = 0.0038939, step = 96301 (0.239 sec)
INFO:tensorflow:global_step/sec: 409.123
INFO:tensorflow:loss = 0.003957896, step = 96401 (0.244 sec)
INFO:tensorflow:global_step/sec: 400.882
INFO:tensorflow:loss = 0.0032314742, step = 96501 (0.249 sec)
INFO:tensorflow:global_step/sec: 406.075
INFO:tensorflow:loss = 0.0067095207, step = 96601 (0.246 sec)
INFO:tensorflow:global_step/sec: 420.992
INFO:tensorflow:loss = 0.005240967, step = 96701 (0.238 sec)
INFO:tensorflow:global_step/sec: 412.539
INFO:tensorflow:loss = 0.0044168094, step = 96801 (0.250 sec)
INFO:tensorflow:global_step/sec: 400.227
INFO:tensorflow:loss = 0.0031404286, step = 96901 (0.243 sec)
INFO:tensorflow:global_step/sec: 388.701
INFO:tensorflow:loss = 0.0059988415, step = 97001 (0.257 sec)
INFO:tensorflow:global_step/sec: 401.666
INFO:tensorflow:loss = 0.008864116, step = 97101 (0.249 sec)
INFO:tensorflow:global_step/sec: 427.55
INFO:tensorflow:loss = 0.0037582796, step = 97201 (0.234 sec)
INFO:tensorflow:global_step/sec: 421.678
INFO:tensorflow:loss = 0.0020435755, step = 97301 (0.237 sec)
INFO:tensorflow:global_step/sec: 429.424
INFO:tensorflow:loss = 0.004087096, step = 97401 (0.233 sec)
INFO:tensorflow:global_step/sec: 422.187
INFO:tensorflow:loss = 0.005750835, step = 97501 (0.237 sec)
INFO:tensorflow:global_step/sec: 404.812
INFO:tensorflow:loss = 0.0053190826, step = 97601 (0.247 sec)
INFO:tensorflow:global_step/sec: 428.256
INFO:tensorflow:loss = 0.00376792, step = 97701 (0.234 sec)
INFO:tensorflow:global_step/sec: 435.916
INFO:tensorflow:loss = 0.006362297, step = 97801 (0.229 sec)
INFO:tensorflow:global_step/sec: 414.76
INFO:tensorflow:loss = 0.0038138563, step = 97901 (0.241 sec)
INFO:tensorflow:global_step/sec: 411.326
INFO:tensorflow:loss = 0.0060359696, step = 98001 (0.243 sec)
INFO:tensorflow:global_step/sec: 430.408
INFO:tensorflow:loss = 0.0051795617, step = 98101 (0.232 sec)
INFO:tensorflow:global_step/sec: 434.779
INFO:tensorflow:loss = 0.006122092, step = 98201 (0.230 sec)
INFO:tensorflow:global_step/sec: 429.927
INFO:tensorflow:loss = 0.007171316, step = 98301 (0.232 sec)
INFO:tensorflow:global_step/sec: 435.352
INFO:tensorflow:loss = 0.0054256674, step = 98401 (0.230 sec)
INFO:tensorflow:global_step/sec: 425.434
INFO:tensorflow:loss = 0.006302964, step = 98501 (0.235 sec)
INFO:tensorflow:global_step/sec: 398.473
INFO:tensorflow:loss = 0.005042514, step = 98601 (0.251 sec)
INFO:tensorflow:global_step/sec: 392.608
INFO:tensorflow:loss = 0.0032336214, step = 98701 (0.255 sec)
INFO:tensorflow:global_step/sec: 395.989
INFO:tensorflow:loss = 0.0043089064, step = 98801 (0.253 sec)
INFO:tensorflow:global_step/sec: 424.751
INFO:tensorflow:loss = 0.0066612316, step = 98901 (0.235 sec)
INFO:tensorflow:global_step/sec: 424.977
INFO:tensorflow:loss = 0.005831009, step = 99001 (0.235 sec)
INFO:tensorflow:global_step/sec: 419.479
INFO:tensorflow:loss = 0.0040449733, step = 99101 (0.242 sec)
INFO:tensorflow:global_step/sec: 415.762
INFO:tensorflow:loss = 0.0032267657, step = 99201 (0.237 sec)
INFO:tensorflow:global_step/sec: 404.335
INFO:tensorflow:loss = 0.003997384, step = 99301 (0.247 sec)
INFO:tensorflow:global_step/sec: 410.295
INFO:tensorflow:loss = 0.008684888, step = 99401 (0.244 sec)
INFO:tensorflow:global_step/sec: 423.051
INFO:tensorflow:loss = 0.0028126503, step = 99501 (0.236 sec)
INFO:tensorflow:global_step/sec: 433.317
INFO:tensorflow:loss = 0.0060156416, step = 99601 (0.232 sec)
INFO:tensorflow:global_step/sec: 406.767
INFO:tensorflow:loss = 0.003310702, step = 99701 (0.245 sec)
INFO:tensorflow:global_step/sec: 390.561
INFO:tensorflow:loss = 0.005235779, step = 99801 (0.256 sec)
INFO:tensorflow:global_step/sec: 409.527
INFO:tensorflow:loss = 0.005432996, step = 99901 (0.244 sec)
INFO:tensorflow:Saving checkpoints for 100000 into /tmp/tmpDexXZd/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpDexXZd/architecture-3.txt: ['0:1_layer_dnn', '1:2_layer_dnn', '2:3_layer_dnn', '3:4_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Rebuilding iteration 3
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Building iteration 4
INFO:tensorflow:Building subnetwork '4_layer_dnn'
INFO:tensorflow:Building subnetwork '5_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:33:29
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpDexXZd/model.ckpt-100000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't3_4_layer_dnn' dict for global step 100000: architecture/adanet/ensembles =
�
>adanet/iteration_3/ensemble_t3_4_layer_dnn/architecture/adanetB? B9| 1_layer_dnn | 2_layer_dnn | 3_layer_dnn | 4_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.03169271, average_loss/adanet/subnetwork = 0.03348904, average_loss/adanet/uniform_average_ensemble = 0.031587753, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.038267143, loss/adanet/subnetwork = 0.041055106, loss/adanet/uniform_average_ensemble = 0.04207115, prediction/mean/adanet/adanet_weighted_ensemble = 3.067704, prediction/mean/adanet/subnetwork = 3.1287208, prediction/mean/adanet/uniform_average_ensemble = 3.1415138
INFO:tensorflow:Saving candidate 't4_4_layer_dnn' dict for global step 100000: architecture/adanet/ensembles =
�
>adanet/iteration_4/ensemble_t4_4_layer_dnn/architecture/adanetBM BG| 1_layer_dnn | 2_layer_dnn | 3_layer_dnn | 4_layer_dnn | 4_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.031012552, average_loss/adanet/subnetwork = 0.036771663, average_loss/adanet/uniform_average_ensemble = 0.031320345, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.036733527, loss/adanet/subnetwork = 0.049315907, loss/adanet/uniform_average_ensemble = 0.04210123, prediction/mean/adanet/adanet_weighted_ensemble = 3.0690398, prediction/mean/adanet/subnetwork = 3.116253, prediction/mean/adanet/uniform_average_ensemble = 3.1364617
INFO:tensorflow:Saving candidate 't4_5_layer_dnn' dict for global step 100000: architecture/adanet/ensembles =
�
>adanet/iteration_4/ensemble_t4_5_layer_dnn/architecture/adanetBM BG| 1_layer_dnn | 2_layer_dnn | 3_layer_dnn | 4_layer_dnn | 5_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.032236967, average_loss/adanet/subnetwork = 0.0495253, average_loss/adanet/uniform_average_ensemble = 0.0326953, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.038305804, loss/adanet/subnetwork = 0.06338713, loss/adanet/uniform_average_ensemble = 0.043896608, prediction/mean/adanet/adanet_weighted_ensemble = 3.0657516, prediction/mean/adanet/subnetwork = 3.0762491, prediction/mean/adanet/uniform_average_ensemble = 3.1284606
INFO:tensorflow:Finished evaluation at 2018-12-13-19:33:35
INFO:tensorflow:Saving dict for global step 100000: average_loss = 0.032236967, average_loss/adanet/adanet_weighted_ensemble = 0.032236967, average_loss/adanet/subnetwork = 0.0495253, average_loss/adanet/uniform_average_ensemble = 0.0326953, global_step = 100000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.038305804, loss/adanet/adanet_weighted_ensemble = 0.038305804, loss/adanet/subnetwork = 0.06338713, loss/adanet/uniform_average_ensemble = 0.043896608, prediction/mean = 3.0657516, prediction/mean/adanet/adanet_weighted_ensemble = 3.0657516, prediction/mean/adanet/subnetwork = 3.0762491, prediction/mean/adanet/uniform_average_ensemble = 3.1284606
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 100000: /tmp/tmpDexXZd/model.ckpt-100000
INFO:tensorflow:Loss for final step: 0.00343033.
INFO:tensorflow:Finished training Adanet iteration 4
Loss: 0.032236967
Uniform average loss: 0.0326953
Architecture: | 1_layer_dnn | 2_layer_dnn | 3_layer_dnn | 4_layer_dnn | 5_layer_dnn |
| Apache-2.0 | adanet/examples/tutorials/adanet_objective.ipynb | sararob/adanet |
Learning the mixture weights produces a model with **0.0449** MSE, a bit worsethan the uniform average model, which the `adanet.Estimator` always compute as abaseline. The mixture weights were learned without regularization, so theylikely overfit to the training set.Observe that AdaNet learned the same ensemble composition as the previous run.Without complexity regularization, AdaNet will favor more complex subnetworks,which may have worse generalization despite improving the empirical error.Finally, let's apply some **complexity regularization** by using $\lambda > 0$.Since this will penalize more complex subnetworks, AdaNet will select thecandidate subnetwork that most improves the objective for its marginalcomplexity: | #@test {"skip": true}
results, _ = train_and_evaluate(learn_mixture_weights=True, adanet_lambda=.015)
print("Loss:", results["average_loss"])
print("Uniform average loss:", results["average_loss/adanet/uniform_average_ensemble"])
print("Architecture:", ensemble_architecture(results)) | WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpU33rCk
INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f2799e49750>, '_model_dir': '/tmp/tmpU33rCk', '_protocol': None, '_save_checkpoints_steps': 50000, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_tf_random_seed': 42, '_save_summary_steps': 50000, '_device_fn': None, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_evaluation_master': '', '_eval_distribute': None, '_train_distribute': None, '_master': ''}
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 50000 or save_checkpoints_secs None.
INFO:tensorflow:Beginning training AdaNet iteration 0
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Building iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpU33rCk/model.ckpt.
INFO:tensorflow:loss = 21.773132, step = 1
INFO:tensorflow:global_step/sec: 159.287
INFO:tensorflow:loss = 0.62784123, step = 101 (0.629 sec)
INFO:tensorflow:global_step/sec: 565.937
INFO:tensorflow:loss = 0.56678694, step = 201 (0.177 sec)
INFO:tensorflow:global_step/sec: 562.364
INFO:tensorflow:loss = 0.0780399, step = 301 (0.178 sec)
INFO:tensorflow:global_step/sec: 553.066
INFO:tensorflow:loss = 0.08678259, step = 401 (0.181 sec)
INFO:tensorflow:global_step/sec: 539.378
INFO:tensorflow:loss = 0.08137446, step = 501 (0.186 sec)
INFO:tensorflow:global_step/sec: 536.173
INFO:tensorflow:loss = 0.05650991, step = 601 (0.186 sec)
INFO:tensorflow:global_step/sec: 548.312
INFO:tensorflow:loss = 0.025883615, step = 701 (0.183 sec)
INFO:tensorflow:global_step/sec: 539.441
INFO:tensorflow:loss = 0.03018033, step = 801 (0.185 sec)
INFO:tensorflow:global_step/sec: 558.404
INFO:tensorflow:loss = 0.037590593, step = 901 (0.179 sec)
INFO:tensorflow:global_step/sec: 527.969
INFO:tensorflow:loss = 0.06694436, step = 1001 (0.190 sec)
INFO:tensorflow:global_step/sec: 523.316
INFO:tensorflow:loss = 0.03847816, step = 1101 (0.191 sec)
INFO:tensorflow:global_step/sec: 539.966
INFO:tensorflow:loss = 0.04998327, step = 1201 (0.185 sec)
INFO:tensorflow:global_step/sec: 562.721
INFO:tensorflow:loss = 0.090066634, step = 1301 (0.178 sec)
INFO:tensorflow:global_step/sec: 548.134
INFO:tensorflow:loss = 0.02687991, step = 1401 (0.182 sec)
INFO:tensorflow:global_step/sec: 551.788
INFO:tensorflow:loss = 0.021093268, step = 1501 (0.181 sec)
INFO:tensorflow:global_step/sec: 545.461
INFO:tensorflow:loss = 0.036077544, step = 1601 (0.183 sec)
INFO:tensorflow:global_step/sec: 568.288
INFO:tensorflow:loss = 0.034161575, step = 1701 (0.176 sec)
INFO:tensorflow:global_step/sec: 545.449
INFO:tensorflow:loss = 0.04626116, step = 1801 (0.183 sec)
INFO:tensorflow:global_step/sec: 543.943
INFO:tensorflow:loss = 0.07378493, step = 1901 (0.184 sec)
INFO:tensorflow:global_step/sec: 512.521
INFO:tensorflow:loss = 0.04918831, step = 2001 (0.195 sec)
INFO:tensorflow:global_step/sec: 572.804
INFO:tensorflow:loss = 0.078179196, step = 2101 (0.176 sec)
INFO:tensorflow:global_step/sec: 530.082
INFO:tensorflow:loss = 0.030299027, step = 2201 (0.187 sec)
INFO:tensorflow:global_step/sec: 563.393
INFO:tensorflow:loss = 0.024719734, step = 2301 (0.178 sec)
INFO:tensorflow:global_step/sec: 561.019
INFO:tensorflow:loss = 0.024992712, step = 2401 (0.178 sec)
INFO:tensorflow:global_step/sec: 544.837
INFO:tensorflow:loss = 0.047092065, step = 2501 (0.184 sec)
INFO:tensorflow:global_step/sec: 546.753
INFO:tensorflow:loss = 0.04721455, step = 2601 (0.183 sec)
INFO:tensorflow:global_step/sec: 563.574
INFO:tensorflow:loss = 0.038211413, step = 2701 (0.178 sec)
INFO:tensorflow:global_step/sec: 557.643
INFO:tensorflow:loss = 0.03274205, step = 2801 (0.179 sec)
INFO:tensorflow:global_step/sec: 534.748
INFO:tensorflow:loss = 0.04549656, step = 2901 (0.187 sec)
INFO:tensorflow:global_step/sec: 534.379
INFO:tensorflow:loss = 0.03548008, step = 3001 (0.187 sec)
INFO:tensorflow:global_step/sec: 548.986
INFO:tensorflow:loss = 0.024679914, step = 3101 (0.182 sec)
INFO:tensorflow:global_step/sec: 513.339
INFO:tensorflow:loss = 0.04125918, step = 3201 (0.194 sec)
INFO:tensorflow:global_step/sec: 507.007
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INFO:tensorflow:global_step/sec: 526.876
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INFO:tensorflow:global_step/sec: 514.06
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INFO:tensorflow:loss = 0.030225167, step = 10101 (0.181 sec)
INFO:tensorflow:global_step/sec: 571.955
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INFO:tensorflow:global_step/sec: 559.973
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INFO:tensorflow:global_step/sec: 543.75
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INFO:tensorflow:global_step/sec: 551.894
INFO:tensorflow:loss = 0.05866126, step = 10501 (0.181 sec)
INFO:tensorflow:global_step/sec: 547.732
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INFO:tensorflow:global_step/sec: 559.879
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INFO:tensorflow:global_step/sec: 569.976
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INFO:tensorflow:global_step/sec: 559.309
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INFO:tensorflow:global_step/sec: 534.828
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INFO:tensorflow:global_step/sec: 528.611
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INFO:tensorflow:global_step/sec: 479.918
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INFO:tensorflow:global_step/sec: 548.751
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INFO:tensorflow:global_step/sec: 540.521
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INFO:tensorflow:global_step/sec: 534.456
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INFO:tensorflow:global_step/sec: 550.41
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INFO:tensorflow:global_step/sec: 556.588
INFO:tensorflow:loss = 0.025518984, step = 11701 (0.180 sec)
INFO:tensorflow:global_step/sec: 568.631
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INFO:tensorflow:global_step/sec: 560.626
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INFO:tensorflow:global_step/sec: 554.717
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INFO:tensorflow:global_step/sec: 538.956
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INFO:tensorflow:global_step/sec: 546.153
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INFO:tensorflow:global_step/sec: 563.4
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INFO:tensorflow:global_step/sec: 539.875
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INFO:tensorflow:global_step/sec: 581.392
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INFO:tensorflow:global_step/sec: 533.595
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INFO:tensorflow:global_step/sec: 563.581
INFO:tensorflow:loss = 0.026882555, step = 12701 (0.178 sec)
INFO:tensorflow:global_step/sec: 548.224
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INFO:tensorflow:global_step/sec: 561.124
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INFO:tensorflow:global_step/sec: 551.326
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INFO:tensorflow:global_step/sec: 545.509
INFO:tensorflow:loss = 0.035332013, step = 13101 (0.183 sec)
INFO:tensorflow:global_step/sec: 523.653
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INFO:tensorflow:global_step/sec: 575.606
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INFO:tensorflow:global_step/sec: 580.339
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INFO:tensorflow:global_step/sec: 538.723
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INFO:tensorflow:global_step/sec: 532.532
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INFO:tensorflow:global_step/sec: 519.954
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INFO:tensorflow:global_step/sec: 549.572
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INFO:tensorflow:global_step/sec: 556.979
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INFO:tensorflow:global_step/sec: 526.937
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INFO:tensorflow:global_step/sec: 555.05
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INFO:tensorflow:global_step/sec: 543.895
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INFO:tensorflow:global_step/sec: 546.263
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INFO:tensorflow:global_step/sec: 564.515
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INFO:tensorflow:global_step/sec: 515.223
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INFO:tensorflow:global_step/sec: 572.229
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INFO:tensorflow:global_step/sec: 559.462
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INFO:tensorflow:global_step/sec: 526.865
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INFO:tensorflow:global_step/sec: 533.405
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INFO:tensorflow:global_step/sec: 525.403
INFO:tensorflow:loss = 0.031934716, step = 18201 (0.190 sec)
INFO:tensorflow:global_step/sec: 485.025
INFO:tensorflow:loss = 0.037095845, step = 18301 (0.206 sec)
INFO:tensorflow:global_step/sec: 524.049
INFO:tensorflow:loss = 0.030218042, step = 18401 (0.191 sec)
INFO:tensorflow:global_step/sec: 514.793
INFO:tensorflow:loss = 0.036680248, step = 18501 (0.194 sec)
INFO:tensorflow:global_step/sec: 540.085
INFO:tensorflow:loss = 0.027322877, step = 18601 (0.185 sec)
INFO:tensorflow:global_step/sec: 542.712
INFO:tensorflow:loss = 0.040832005, step = 18701 (0.184 sec)
INFO:tensorflow:global_step/sec: 575.261
INFO:tensorflow:loss = 0.0099720275, step = 18801 (0.174 sec)
INFO:tensorflow:global_step/sec: 543.889
INFO:tensorflow:loss = 0.044099957, step = 18901 (0.184 sec)
INFO:tensorflow:global_step/sec: 542.106
INFO:tensorflow:loss = 0.014038452, step = 19001 (0.184 sec)
INFO:tensorflow:global_step/sec: 552.868
INFO:tensorflow:loss = 0.030261023, step = 19101 (0.181 sec)
INFO:tensorflow:global_step/sec: 541.468
INFO:tensorflow:loss = 0.024491156, step = 19201 (0.185 sec)
INFO:tensorflow:global_step/sec: 531.57
INFO:tensorflow:loss = 0.019349206, step = 19301 (0.188 sec)
INFO:tensorflow:global_step/sec: 544.33
INFO:tensorflow:loss = 0.029496612, step = 19401 (0.184 sec)
INFO:tensorflow:global_step/sec: 528.042
INFO:tensorflow:loss = 0.02566719, step = 19501 (0.190 sec)
INFO:tensorflow:global_step/sec: 541.261
INFO:tensorflow:loss = 0.011755895, step = 19601 (0.185 sec)
INFO:tensorflow:global_step/sec: 538.773
INFO:tensorflow:loss = 0.011457266, step = 19701 (0.185 sec)
INFO:tensorflow:global_step/sec: 547.462
INFO:tensorflow:loss = 0.02694907, step = 19801 (0.183 sec)
INFO:tensorflow:global_step/sec: 536.015
INFO:tensorflow:loss = 0.039816238, step = 19901 (0.187 sec)
INFO:tensorflow:Saving checkpoints for 20000 into /tmp/tmpU33rCk/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Building iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:34:17
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpU33rCk/model.ckpt-20000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't0_linear' dict for global step 20000: architecture/adanet/ensembles =
W
9adanet/iteration_0/ensemble_t0_linear/architecture/adanetB B
| linear |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.049419947, average_loss/adanet/subnetwork = 0.049421377, average_loss/adanet/uniform_average_ensemble = 0.049421377, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.0625109, loss/adanet/subnetwork = 0.062442042, loss/adanet/uniform_average_ensemble = 0.062442042, prediction/mean/adanet/adanet_weighted_ensemble = 3.1072564, prediction/mean/adanet/subnetwork = 3.105895, prediction/mean/adanet/uniform_average_ensemble = 3.105895
INFO:tensorflow:Saving candidate 't0_1_layer_dnn' dict for global step 20000: architecture/adanet/ensembles =
a
>adanet/iteration_0/ensemble_t0_1_layer_dnn/architecture/adanetB B| 1_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.03990466, average_loss/adanet/subnetwork = 0.03993654, average_loss/adanet/uniform_average_ensemble = 0.03993654, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.053545497, loss/adanet/subnetwork = 0.053605493, loss/adanet/uniform_average_ensemble = 0.053605493, prediction/mean/adanet/adanet_weighted_ensemble = 3.1576996, prediction/mean/adanet/subnetwork = 3.1580222, prediction/mean/adanet/uniform_average_ensemble = 3.1580222
INFO:tensorflow:Finished evaluation at 2018-12-13-19:34:19
INFO:tensorflow:Saving dict for global step 20000: average_loss = 0.049419947, average_loss/adanet/adanet_weighted_ensemble = 0.049419947, average_loss/adanet/subnetwork = 0.049421377, average_loss/adanet/uniform_average_ensemble = 0.049421377, global_step = 20000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.0625109, loss/adanet/adanet_weighted_ensemble = 0.0625109, loss/adanet/subnetwork = 0.062442042, loss/adanet/uniform_average_ensemble = 0.062442042, prediction/mean = 3.1072564, prediction/mean/adanet/adanet_weighted_ensemble = 3.1072564, prediction/mean/adanet/subnetwork = 3.105895, prediction/mean/adanet/uniform_average_ensemble = 3.105895
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20000: /tmp/tmpU33rCk/model.ckpt-20000
INFO:tensorflow:Loss for final step: 0.05016574.
INFO:tensorflow:Finished training Adanet iteration 0
INFO:tensorflow:Beginning bookkeeping phase for iteration 0
INFO:tensorflow:Building iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Starting ensemble evaluation for iteration 0
INFO:tensorflow:Restoring parameters from /tmp/tmpU33rCk/model.ckpt-20000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Encountered end of input after 14 evaluations
INFO:tensorflow:Computed ensemble metrics: adanet_loss/t0_linear = 0.035082, adanet_loss/t0_1_layer_dnn = 0.035763
INFO:tensorflow:Finished ensemble evaluation for iteration 0
INFO:tensorflow:'t0_linear' at index 0 is moving onto the next iteration
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-0.txt: ['0:linear'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Warm-starting from: (u'/tmp/tmpU33rCk/model.ckpt-20000',)
INFO:tensorflow:Warm-starting variable: global_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/weighted_subnetwork_0/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_linear/adanet/iteration_0/candidate_t0_linear/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_linear/adanet/iteration_0/candidate_t0_linear/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/weighted_subnetwork_0/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_linear/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Building iteration 1
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Overwriting checkpoint with new graph for iteration 1 to /tmp/tmpU33rCk/model.ckpt-20000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Finished bookkeeping phase for iteration 0
INFO:tensorflow:Beginning training AdaNet iteration 1
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-0.txt: ['0:linear'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building iteration 1
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpU33rCk/increment.ckpt-1
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 20000 into /tmp/tmpU33rCk/model.ckpt.
INFO:tensorflow:loss = 0.030963298, step = 20001
INFO:tensorflow:global_step/sec: 124.894
INFO:tensorflow:loss = 0.03516689, step = 20101 (0.802 sec)
INFO:tensorflow:global_step/sec: 539.514
INFO:tensorflow:loss = 0.03981951, step = 20201 (0.185 sec)
INFO:tensorflow:global_step/sec: 513.933
INFO:tensorflow:loss = 0.033043213, step = 20301 (0.195 sec)
INFO:tensorflow:global_step/sec: 480.78
INFO:tensorflow:loss = 0.0467762, step = 20401 (0.208 sec)
INFO:tensorflow:global_step/sec: 530.898
INFO:tensorflow:loss = 0.03045134, step = 20501 (0.188 sec)
INFO:tensorflow:global_step/sec: 516.018
INFO:tensorflow:loss = 0.041044854, step = 20601 (0.197 sec)
INFO:tensorflow:global_step/sec: 519.834
INFO:tensorflow:loss = 0.01876518, step = 20701 (0.189 sec)
INFO:tensorflow:global_step/sec: 512.371
INFO:tensorflow:loss = 0.014937608, step = 20801 (0.195 sec)
INFO:tensorflow:global_step/sec: 525.975
INFO:tensorflow:loss = 0.031160582, step = 20901 (0.190 sec)
INFO:tensorflow:global_step/sec: 551.898
INFO:tensorflow:loss = 0.028920764, step = 21001 (0.181 sec)
INFO:tensorflow:global_step/sec: 515.066
INFO:tensorflow:loss = 0.032353245, step = 21101 (0.194 sec)
INFO:tensorflow:global_step/sec: 531.83
INFO:tensorflow:loss = 0.025514642, step = 21201 (0.188 sec)
INFO:tensorflow:global_step/sec: 485.63
INFO:tensorflow:loss = 0.075824335, step = 21301 (0.206 sec)
INFO:tensorflow:global_step/sec: 516.614
INFO:tensorflow:loss = 0.015722096, step = 21401 (0.194 sec)
INFO:tensorflow:global_step/sec: 522.079
INFO:tensorflow:loss = 0.01953032, step = 21501 (0.191 sec)
INFO:tensorflow:global_step/sec: 534.123
INFO:tensorflow:loss = 0.021188064, step = 21601 (0.187 sec)
INFO:tensorflow:global_step/sec: 520.757
INFO:tensorflow:loss = 0.03627994, step = 21701 (0.192 sec)
INFO:tensorflow:global_step/sec: 519.758
INFO:tensorflow:loss = 0.037261367, step = 21801 (0.192 sec)
INFO:tensorflow:global_step/sec: 505.114
INFO:tensorflow:loss = 0.049223125, step = 21901 (0.198 sec)
INFO:tensorflow:global_step/sec: 535.51
INFO:tensorflow:loss = 0.043363348, step = 22001 (0.186 sec)
INFO:tensorflow:global_step/sec: 525.834
INFO:tensorflow:loss = 0.06184654, step = 22101 (0.190 sec)
INFO:tensorflow:global_step/sec: 518.716
INFO:tensorflow:loss = 0.024632609, step = 22201 (0.192 sec)
INFO:tensorflow:global_step/sec: 512.36
INFO:tensorflow:loss = 0.024932098, step = 22301 (0.195 sec)
INFO:tensorflow:global_step/sec: 503.358
INFO:tensorflow:loss = 0.021503564, step = 22401 (0.198 sec)
INFO:tensorflow:global_step/sec: 529.14
INFO:tensorflow:loss = 0.04656288, step = 22501 (0.189 sec)
INFO:tensorflow:global_step/sec: 515.911
INFO:tensorflow:loss = 0.037371665, step = 22601 (0.194 sec)
INFO:tensorflow:global_step/sec: 540.99
INFO:tensorflow:loss = 0.029282678, step = 22701 (0.185 sec)
INFO:tensorflow:global_step/sec: 530.24
INFO:tensorflow:loss = 0.019854745, step = 22801 (0.188 sec)
INFO:tensorflow:global_step/sec: 519.696
INFO:tensorflow:loss = 0.037007652, step = 22901 (0.193 sec)
INFO:tensorflow:global_step/sec: 469.894
INFO:tensorflow:loss = 0.027802797, step = 23001 (0.213 sec)
INFO:tensorflow:global_step/sec: 435.785
INFO:tensorflow:loss = 0.018298797, step = 23101 (0.229 sec)
INFO:tensorflow:global_step/sec: 430.956
INFO:tensorflow:loss = 0.024072375, step = 23201 (0.232 sec)
INFO:tensorflow:global_step/sec: 408.725
INFO:tensorflow:loss = 0.04094688, step = 23301 (0.246 sec)
INFO:tensorflow:global_step/sec: 449.246
INFO:tensorflow:loss = 0.027860032, step = 23401 (0.222 sec)
INFO:tensorflow:global_step/sec: 453.336
INFO:tensorflow:loss = 0.064778514, step = 23501 (0.220 sec)
INFO:tensorflow:global_step/sec: 453.892
INFO:tensorflow:loss = 0.027260652, step = 23601 (0.221 sec)
INFO:tensorflow:global_step/sec: 445.129
INFO:tensorflow:loss = 0.021556232, step = 23701 (0.225 sec)
INFO:tensorflow:global_step/sec: 438.546
INFO:tensorflow:loss = 0.027705526, step = 23801 (0.228 sec)
INFO:tensorflow:global_step/sec: 437.365
INFO:tensorflow:loss = 0.02642343, step = 23901 (0.229 sec)
INFO:tensorflow:global_step/sec: 440.74
INFO:tensorflow:loss = 0.04874111, step = 24001 (0.227 sec)
INFO:tensorflow:global_step/sec: 434.06
INFO:tensorflow:loss = 0.028818486, step = 24101 (0.230 sec)
INFO:tensorflow:global_step/sec: 437.097
INFO:tensorflow:loss = 0.02050123, step = 24201 (0.229 sec)
INFO:tensorflow:global_step/sec: 441.363
INFO:tensorflow:loss = 0.017606108, step = 24301 (0.227 sec)
INFO:tensorflow:global_step/sec: 452.184
INFO:tensorflow:loss = 0.022820558, step = 24401 (0.220 sec)
INFO:tensorflow:global_step/sec: 439.408
INFO:tensorflow:loss = 0.042412907, step = 24501 (0.228 sec)
INFO:tensorflow:global_step/sec: 471.045
INFO:tensorflow:loss = 0.021762788, step = 24601 (0.212 sec)
INFO:tensorflow:global_step/sec: 434.754
INFO:tensorflow:loss = 0.07007265, step = 24701 (0.230 sec)
INFO:tensorflow:global_step/sec: 424.497
INFO:tensorflow:loss = 0.029353648, step = 24801 (0.236 sec)
INFO:tensorflow:global_step/sec: 440.087
INFO:tensorflow:loss = 0.025005665, step = 24901 (0.227 sec)
INFO:tensorflow:global_step/sec: 414.214
INFO:tensorflow:loss = 0.022534322, step = 25001 (0.241 sec)
INFO:tensorflow:global_step/sec: 426.734
INFO:tensorflow:loss = 0.03668832, step = 25101 (0.235 sec)
INFO:tensorflow:global_step/sec: 468.812
INFO:tensorflow:loss = 0.022969142, step = 25201 (0.214 sec)
INFO:tensorflow:global_step/sec: 458.335
INFO:tensorflow:loss = 0.053870354, step = 25301 (0.218 sec)
INFO:tensorflow:global_step/sec: 435.33
INFO:tensorflow:loss = 0.019454079, step = 25401 (0.230 sec)
INFO:tensorflow:global_step/sec: 430.181
INFO:tensorflow:loss = 0.020998772, step = 25501 (0.232 sec)
INFO:tensorflow:global_step/sec: 461.487
INFO:tensorflow:loss = 0.02694126, step = 25601 (0.217 sec)
INFO:tensorflow:global_step/sec: 441.842
INFO:tensorflow:loss = 0.03186059, step = 25701 (0.226 sec)
INFO:tensorflow:global_step/sec: 425.232
INFO:tensorflow:loss = 0.028446794, step = 25801 (0.236 sec)
INFO:tensorflow:global_step/sec: 407.917
INFO:tensorflow:loss = 0.013250216, step = 25901 (0.244 sec)
INFO:tensorflow:global_step/sec: 437.924
INFO:tensorflow:loss = 0.026782522, step = 26001 (0.228 sec)
INFO:tensorflow:global_step/sec: 454.258
INFO:tensorflow:loss = 0.03703142, step = 26101 (0.220 sec)
INFO:tensorflow:global_step/sec: 419.535
INFO:tensorflow:loss = 0.03735739, step = 26201 (0.238 sec)
INFO:tensorflow:global_step/sec: 427.129
INFO:tensorflow:loss = 0.03606581, step = 26301 (0.234 sec)
INFO:tensorflow:global_step/sec: 429.356
INFO:tensorflow:loss = 0.05572056, step = 26401 (0.233 sec)
INFO:tensorflow:global_step/sec: 441.618
INFO:tensorflow:loss = 0.047298286, step = 26501 (0.226 sec)
INFO:tensorflow:global_step/sec: 434.033
INFO:tensorflow:loss = 0.019773448, step = 26601 (0.231 sec)
INFO:tensorflow:global_step/sec: 434.926
INFO:tensorflow:loss = 0.0269448, step = 26701 (0.230 sec)
INFO:tensorflow:global_step/sec: 417.432
INFO:tensorflow:loss = 0.019713217, step = 26801 (0.239 sec)
INFO:tensorflow:global_step/sec: 437.564
INFO:tensorflow:loss = 0.013232482, step = 26901 (0.229 sec)
INFO:tensorflow:global_step/sec: 434.554
INFO:tensorflow:loss = 0.025097178, step = 27001 (0.230 sec)
INFO:tensorflow:global_step/sec: 428.526
INFO:tensorflow:loss = 0.023188664, step = 27101 (0.233 sec)
INFO:tensorflow:global_step/sec: 442.515
INFO:tensorflow:loss = 0.01634363, step = 27201 (0.226 sec)
INFO:tensorflow:global_step/sec: 439.91
INFO:tensorflow:loss = 0.06469944, step = 27301 (0.227 sec)
INFO:tensorflow:global_step/sec: 442.905
INFO:tensorflow:loss = 0.027050652, step = 27401 (0.226 sec)
INFO:tensorflow:global_step/sec: 448.093
INFO:tensorflow:loss = 0.037419617, step = 27501 (0.223 sec)
INFO:tensorflow:global_step/sec: 536.688
INFO:tensorflow:loss = 0.014020189, step = 27601 (0.186 sec)
INFO:tensorflow:global_step/sec: 509.378
INFO:tensorflow:loss = 0.023243275, step = 27701 (0.196 sec)
INFO:tensorflow:global_step/sec: 531.629
INFO:tensorflow:loss = 0.02837298, step = 27801 (0.188 sec)
INFO:tensorflow:global_step/sec: 537.444
INFO:tensorflow:loss = 0.031420454, step = 27901 (0.186 sec)
INFO:tensorflow:global_step/sec: 529.826
INFO:tensorflow:loss = 0.02842214, step = 28001 (0.189 sec)
INFO:tensorflow:global_step/sec: 525.199
INFO:tensorflow:loss = 0.025423417, step = 28101 (0.190 sec)
INFO:tensorflow:global_step/sec: 507.035
INFO:tensorflow:loss = 0.03314421, step = 28201 (0.197 sec)
INFO:tensorflow:global_step/sec: 543.434
INFO:tensorflow:loss = 0.026891911, step = 28301 (0.184 sec)
INFO:tensorflow:global_step/sec: 526.554
INFO:tensorflow:loss = 0.03682626, step = 28401 (0.190 sec)
INFO:tensorflow:global_step/sec: 554.76
INFO:tensorflow:loss = 0.024336819, step = 28501 (0.180 sec)
INFO:tensorflow:global_step/sec: 541.767
INFO:tensorflow:loss = 0.0462117, step = 28601 (0.185 sec)
INFO:tensorflow:global_step/sec: 510.785
INFO:tensorflow:loss = 0.018110778, step = 28701 (0.196 sec)
INFO:tensorflow:global_step/sec: 492.771
INFO:tensorflow:loss = 0.035204474, step = 28801 (0.203 sec)
INFO:tensorflow:global_step/sec: 527.769
INFO:tensorflow:loss = 0.06147111, step = 28901 (0.189 sec)
INFO:tensorflow:global_step/sec: 536.012
INFO:tensorflow:loss = 0.04892836, step = 29001 (0.186 sec)
INFO:tensorflow:global_step/sec: 538.178
INFO:tensorflow:loss = 0.023903372, step = 29101 (0.186 sec)
INFO:tensorflow:global_step/sec: 549.164
INFO:tensorflow:loss = 0.04787178, step = 29201 (0.182 sec)
INFO:tensorflow:global_step/sec: 547.639
INFO:tensorflow:loss = 0.019329004, step = 29301 (0.183 sec)
INFO:tensorflow:global_step/sec: 550.234
INFO:tensorflow:loss = 0.013593976, step = 29401 (0.182 sec)
INFO:tensorflow:global_step/sec: 535.249
INFO:tensorflow:loss = 0.057428516, step = 29501 (0.187 sec)
INFO:tensorflow:global_step/sec: 532.005
INFO:tensorflow:loss = 0.04012476, step = 29601 (0.188 sec)
INFO:tensorflow:global_step/sec: 528.46
INFO:tensorflow:loss = 0.012712158, step = 29701 (0.189 sec)
INFO:tensorflow:global_step/sec: 533.815
INFO:tensorflow:loss = 0.023402063, step = 29801 (0.187 sec)
INFO:tensorflow:global_step/sec: 515.953
INFO:tensorflow:loss = 0.023870125, step = 29901 (0.194 sec)
INFO:tensorflow:global_step/sec: 541.187
INFO:tensorflow:loss = 0.024826566, step = 30001 (0.185 sec)
INFO:tensorflow:global_step/sec: 532.612
INFO:tensorflow:loss = 0.024468493, step = 30101 (0.188 sec)
INFO:tensorflow:global_step/sec: 551.481
INFO:tensorflow:loss = 0.044891607, step = 30201 (0.182 sec)
INFO:tensorflow:global_step/sec: 543.957
INFO:tensorflow:loss = 0.04333415, step = 30301 (0.183 sec)
INFO:tensorflow:global_step/sec: 529.897
INFO:tensorflow:loss = 0.017114978, step = 30401 (0.189 sec)
INFO:tensorflow:global_step/sec: 542.597
INFO:tensorflow:loss = 0.042604566, step = 30501 (0.184 sec)
INFO:tensorflow:global_step/sec: 551.079
INFO:tensorflow:loss = 0.021167897, step = 30601 (0.184 sec)
INFO:tensorflow:global_step/sec: 541.58
INFO:tensorflow:loss = 0.01646223, step = 30701 (0.182 sec)
INFO:tensorflow:global_step/sec: 507.97
INFO:tensorflow:loss = 0.023372637, step = 30801 (0.197 sec)
INFO:tensorflow:global_step/sec: 523.725
INFO:tensorflow:loss = 0.026064549, step = 30901 (0.191 sec)
INFO:tensorflow:global_step/sec: 531.465
INFO:tensorflow:loss = 0.013219604, step = 31001 (0.188 sec)
INFO:tensorflow:global_step/sec: 543.215
INFO:tensorflow:loss = 0.030059274, step = 31101 (0.184 sec)
INFO:tensorflow:global_step/sec: 539.645
INFO:tensorflow:loss = 0.023715459, step = 31201 (0.185 sec)
INFO:tensorflow:global_step/sec: 532.388
INFO:tensorflow:loss = 0.043849677, step = 31301 (0.188 sec)
INFO:tensorflow:global_step/sec: 548.486
INFO:tensorflow:loss = 0.023017507, step = 31401 (0.184 sec)
INFO:tensorflow:global_step/sec: 504.562
INFO:tensorflow:loss = 0.040082093, step = 31501 (0.196 sec)
INFO:tensorflow:global_step/sec: 528.385
INFO:tensorflow:loss = 0.023561921, step = 31601 (0.189 sec)
INFO:tensorflow:global_step/sec: 520.587
INFO:tensorflow:loss = 0.022855878, step = 31701 (0.192 sec)
INFO:tensorflow:global_step/sec: 514.814
INFO:tensorflow:loss = 0.01623566, step = 31801 (0.194 sec)
INFO:tensorflow:global_step/sec: 481.042
INFO:tensorflow:loss = 0.04121801, step = 31901 (0.208 sec)
INFO:tensorflow:global_step/sec: 523.59
INFO:tensorflow:loss = 0.027685797, step = 32001 (0.191 sec)
INFO:tensorflow:global_step/sec: 529.395
INFO:tensorflow:loss = 0.009713226, step = 32101 (0.189 sec)
INFO:tensorflow:global_step/sec: 472.961
INFO:tensorflow:loss = 0.012999925, step = 32201 (0.211 sec)
INFO:tensorflow:global_step/sec: 496.044
INFO:tensorflow:loss = 0.02126931, step = 32301 (0.202 sec)
INFO:tensorflow:global_step/sec: 493.262
INFO:tensorflow:loss = 0.032282937, step = 32401 (0.203 sec)
INFO:tensorflow:global_step/sec: 536.067
INFO:tensorflow:loss = 0.025438417, step = 32501 (0.188 sec)
INFO:tensorflow:global_step/sec: 500.025
INFO:tensorflow:loss = 0.032477073, step = 32601 (0.199 sec)
INFO:tensorflow:global_step/sec: 519.818
INFO:tensorflow:loss = 0.020108623, step = 32701 (0.192 sec)
INFO:tensorflow:global_step/sec: 527.104
INFO:tensorflow:loss = 0.02968867, step = 32801 (0.190 sec)
INFO:tensorflow:global_step/sec: 548.089
INFO:tensorflow:loss = 0.03175928, step = 32901 (0.182 sec)
INFO:tensorflow:global_step/sec: 504.806
INFO:tensorflow:loss = 0.034673132, step = 33001 (0.198 sec)
INFO:tensorflow:global_step/sec: 529.375
INFO:tensorflow:loss = 0.027451565, step = 33101 (0.189 sec)
INFO:tensorflow:global_step/sec: 532.09
INFO:tensorflow:loss = 0.018435553, step = 33201 (0.188 sec)
INFO:tensorflow:global_step/sec: 507.854
INFO:tensorflow:loss = 0.02186241, step = 33301 (0.197 sec)
INFO:tensorflow:global_step/sec: 532.481
INFO:tensorflow:loss = 0.028581627, step = 33401 (0.188 sec)
INFO:tensorflow:global_step/sec: 536.19
INFO:tensorflow:loss = 0.034122914, step = 33501 (0.186 sec)
INFO:tensorflow:global_step/sec: 541.111
INFO:tensorflow:loss = 0.012513552, step = 33601 (0.185 sec)
INFO:tensorflow:global_step/sec: 515.727
INFO:tensorflow:loss = 0.01912247, step = 33701 (0.194 sec)
INFO:tensorflow:global_step/sec: 535.961
INFO:tensorflow:loss = 0.054644194, step = 33801 (0.187 sec)
INFO:tensorflow:global_step/sec: 463.751
INFO:tensorflow:loss = 0.015859207, step = 33901 (0.215 sec)
INFO:tensorflow:global_step/sec: 519.512
INFO:tensorflow:loss = 0.018914541, step = 34001 (0.192 sec)
INFO:tensorflow:global_step/sec: 525.71
INFO:tensorflow:loss = 0.025463093, step = 34101 (0.190 sec)
INFO:tensorflow:global_step/sec: 530.929
INFO:tensorflow:loss = 0.026399424, step = 34201 (0.188 sec)
INFO:tensorflow:global_step/sec: 547.223
INFO:tensorflow:loss = 0.036557145, step = 34301 (0.183 sec)
INFO:tensorflow:global_step/sec: 531.649
INFO:tensorflow:loss = 0.016763993, step = 34401 (0.188 sec)
INFO:tensorflow:global_step/sec: 520.27
INFO:tensorflow:loss = 0.025890842, step = 34501 (0.192 sec)
INFO:tensorflow:global_step/sec: 523.376
INFO:tensorflow:loss = 0.033754352, step = 34601 (0.191 sec)
INFO:tensorflow:global_step/sec: 522.111
INFO:tensorflow:loss = 0.028789936, step = 34701 (0.191 sec)
INFO:tensorflow:global_step/sec: 502.536
INFO:tensorflow:loss = 0.036325447, step = 34801 (0.199 sec)
INFO:tensorflow:global_step/sec: 522.865
INFO:tensorflow:loss = 0.05625145, step = 34901 (0.191 sec)
INFO:tensorflow:global_step/sec: 500.97
INFO:tensorflow:loss = 0.034521013, step = 35001 (0.201 sec)
INFO:tensorflow:global_step/sec: 520.735
INFO:tensorflow:loss = 0.0150056705, step = 35101 (0.190 sec)
INFO:tensorflow:global_step/sec: 534.722
INFO:tensorflow:loss = 0.020731103, step = 35201 (0.187 sec)
INFO:tensorflow:global_step/sec: 529.112
INFO:tensorflow:loss = 0.026185703, step = 35301 (0.189 sec)
INFO:tensorflow:global_step/sec: 529.947
INFO:tensorflow:loss = 0.014758859, step = 35401 (0.189 sec)
INFO:tensorflow:global_step/sec: 514.263
INFO:tensorflow:loss = 0.037995845, step = 35501 (0.194 sec)
INFO:tensorflow:global_step/sec: 516.201
INFO:tensorflow:loss = 0.041229367, step = 35601 (0.194 sec)
INFO:tensorflow:global_step/sec: 528.712
INFO:tensorflow:loss = 0.050575472, step = 35701 (0.189 sec)
INFO:tensorflow:global_step/sec: 521.548
INFO:tensorflow:loss = 0.041070037, step = 35801 (0.192 sec)
INFO:tensorflow:global_step/sec: 461.603
INFO:tensorflow:loss = 0.031438783, step = 35901 (0.216 sec)
INFO:tensorflow:global_step/sec: 514.287
INFO:tensorflow:loss = 0.03783085, step = 36001 (0.195 sec)
INFO:tensorflow:global_step/sec: 461.018
INFO:tensorflow:loss = 0.030223355, step = 36101 (0.217 sec)
INFO:tensorflow:global_step/sec: 491.196
INFO:tensorflow:loss = 0.013579338, step = 36201 (0.204 sec)
INFO:tensorflow:global_step/sec: 534.359
INFO:tensorflow:loss = 0.025656413, step = 36301 (0.187 sec)
INFO:tensorflow:global_step/sec: 527.051
INFO:tensorflow:loss = 0.0447367, step = 36401 (0.190 sec)
INFO:tensorflow:global_step/sec: 525.867
INFO:tensorflow:loss = 0.03292373, step = 36501 (0.190 sec)
INFO:tensorflow:global_step/sec: 528.422
INFO:tensorflow:loss = 0.022560276, step = 36601 (0.190 sec)
INFO:tensorflow:global_step/sec: 501.263
INFO:tensorflow:loss = 0.029914442, step = 36701 (0.201 sec)
INFO:tensorflow:global_step/sec: 534.25
INFO:tensorflow:loss = 0.025595265, step = 36801 (0.186 sec)
INFO:tensorflow:global_step/sec: 512.091
INFO:tensorflow:loss = 0.019214138, step = 36901 (0.195 sec)
INFO:tensorflow:global_step/sec: 519.424
INFO:tensorflow:loss = 0.029903362, step = 37001 (0.192 sec)
INFO:tensorflow:global_step/sec: 505.132
INFO:tensorflow:loss = 0.03370553, step = 37101 (0.198 sec)
INFO:tensorflow:global_step/sec: 490.146
INFO:tensorflow:loss = 0.012709979, step = 37201 (0.204 sec)
INFO:tensorflow:global_step/sec: 526.05
INFO:tensorflow:loss = 0.03051424, step = 37301 (0.190 sec)
INFO:tensorflow:global_step/sec: 531.841
INFO:tensorflow:loss = 0.026626717, step = 37401 (0.188 sec)
INFO:tensorflow:global_step/sec: 519.588
INFO:tensorflow:loss = 0.032916978, step = 37501 (0.192 sec)
INFO:tensorflow:global_step/sec: 517.663
INFO:tensorflow:loss = 0.035041273, step = 37601 (0.193 sec)
INFO:tensorflow:global_step/sec: 505.776
INFO:tensorflow:loss = 0.028301798, step = 37701 (0.198 sec)
INFO:tensorflow:global_step/sec: 533.214
INFO:tensorflow:loss = 0.025922457, step = 37801 (0.188 sec)
INFO:tensorflow:global_step/sec: 508.577
INFO:tensorflow:loss = 0.020458834, step = 37901 (0.196 sec)
INFO:tensorflow:global_step/sec: 531.459
INFO:tensorflow:loss = 0.039606288, step = 38001 (0.188 sec)
INFO:tensorflow:global_step/sec: 506.014
INFO:tensorflow:loss = 0.021974199, step = 38101 (0.198 sec)
INFO:tensorflow:global_step/sec: 533.624
INFO:tensorflow:loss = 0.025154984, step = 38201 (0.187 sec)
INFO:tensorflow:global_step/sec: 492.373
INFO:tensorflow:loss = 0.027581938, step = 38301 (0.203 sec)
INFO:tensorflow:global_step/sec: 533.955
INFO:tensorflow:loss = 0.04229299, step = 38401 (0.188 sec)
INFO:tensorflow:global_step/sec: 526.862
INFO:tensorflow:loss = 0.029815745, step = 38501 (0.189 sec)
INFO:tensorflow:global_step/sec: 537.21
INFO:tensorflow:loss = 0.02210619, step = 38601 (0.186 sec)
INFO:tensorflow:global_step/sec: 534.931
INFO:tensorflow:loss = 0.031400964, step = 38701 (0.187 sec)
INFO:tensorflow:global_step/sec: 520.161
INFO:tensorflow:loss = 0.021041807, step = 38801 (0.192 sec)
INFO:tensorflow:global_step/sec: 504.541
INFO:tensorflow:loss = 0.036891684, step = 38901 (0.198 sec)
INFO:tensorflow:global_step/sec: 524.874
INFO:tensorflow:loss = 0.017958721, step = 39001 (0.191 sec)
INFO:tensorflow:global_step/sec: 520.248
INFO:tensorflow:loss = 0.039710287, step = 39101 (0.192 sec)
INFO:tensorflow:global_step/sec: 504.559
INFO:tensorflow:loss = 0.03919609, step = 39201 (0.198 sec)
INFO:tensorflow:global_step/sec: 518.409
INFO:tensorflow:loss = 0.015263615, step = 39301 (0.193 sec)
INFO:tensorflow:global_step/sec: 513.087
INFO:tensorflow:loss = 0.021182708, step = 39401 (0.195 sec)
INFO:tensorflow:global_step/sec: 529.391
INFO:tensorflow:loss = 0.018177632, step = 39501 (0.189 sec)
INFO:tensorflow:global_step/sec: 529.892
INFO:tensorflow:loss = 0.01564832, step = 39601 (0.189 sec)
INFO:tensorflow:global_step/sec: 511.341
INFO:tensorflow:loss = 0.019479267, step = 39701 (0.196 sec)
INFO:tensorflow:global_step/sec: 535.86
INFO:tensorflow:loss = 0.020116728, step = 39801 (0.187 sec)
INFO:tensorflow:global_step/sec: 498.708
INFO:tensorflow:loss = 0.027168905, step = 39901 (0.201 sec)
INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmpU33rCk/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-0.txt: ['0:linear'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building iteration 1
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:35:16
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpU33rCk/model.ckpt-40000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't0_linear' dict for global step 40000: architecture/adanet/ensembles =
W
9adanet/iteration_0/ensemble_t0_linear/architecture/adanetB B
| linear |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.049419947, average_loss/adanet/subnetwork = 0.049421377, average_loss/adanet/uniform_average_ensemble = 0.049421377, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.0625109, loss/adanet/subnetwork = 0.062442042, loss/adanet/uniform_average_ensemble = 0.062442042, prediction/mean/adanet/adanet_weighted_ensemble = 3.1072564, prediction/mean/adanet/subnetwork = 3.105895, prediction/mean/adanet/uniform_average_ensemble = 3.105895
INFO:tensorflow:Saving candidate 't1_linear' dict for global step 40000: architecture/adanet/ensembles =
`
9adanet/iteration_1/ensemble_t1_linear/architecture/adanetB B| linear | linear |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.04959257, average_loss/adanet/subnetwork = 0.051883172, average_loss/adanet/uniform_average_ensemble = 0.05056643, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.062364295, loss/adanet/subnetwork = 0.06333193, loss/adanet/uniform_average_ensemble = 0.0627961, prediction/mean/adanet/adanet_weighted_ensemble = 3.103125, prediction/mean/adanet/subnetwork = 3.103565, prediction/mean/adanet/uniform_average_ensemble = 3.1047301
INFO:tensorflow:Saving candidate 't1_1_layer_dnn' dict for global step 40000: architecture/adanet/ensembles =
j
>adanet/iteration_1/ensemble_t1_1_layer_dnn/architecture/adanetB B| linear | 1_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.04422355, average_loss/adanet/subnetwork = 0.044653624, average_loss/adanet/uniform_average_ensemble = 0.043328855, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.060774922, loss/adanet/subnetwork = 0.06800773, loss/adanet/uniform_average_ensemble = 0.061006997, prediction/mean/adanet/adanet_weighted_ensemble = 3.1278303, prediction/mean/adanet/subnetwork = 3.1593368, prediction/mean/adanet/uniform_average_ensemble = 3.1326156
INFO:tensorflow:Finished evaluation at 2018-12-13-19:35:19
INFO:tensorflow:Saving dict for global step 40000: average_loss = 0.04422355, average_loss/adanet/adanet_weighted_ensemble = 0.04422355, average_loss/adanet/subnetwork = 0.044653624, average_loss/adanet/uniform_average_ensemble = 0.043328855, global_step = 40000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.060774922, loss/adanet/adanet_weighted_ensemble = 0.060774922, loss/adanet/subnetwork = 0.06800773, loss/adanet/uniform_average_ensemble = 0.061006997, prediction/mean = 3.1278303, prediction/mean/adanet/adanet_weighted_ensemble = 3.1278303, prediction/mean/adanet/subnetwork = 3.1593368, prediction/mean/adanet/uniform_average_ensemble = 3.1326156
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 40000: /tmp/tmpU33rCk/model.ckpt-40000
INFO:tensorflow:Loss for final step: 0.039504506.
INFO:tensorflow:Finished training Adanet iteration 1
INFO:tensorflow:Beginning bookkeeping phase for iteration 1
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-0.txt: ['0:linear'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building iteration 1
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Starting ensemble evaluation for iteration 1
INFO:tensorflow:Restoring parameters from /tmp/tmpU33rCk/model.ckpt-40000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Encountered end of input after 14 evaluations
INFO:tensorflow:Computed ensemble metrics: adanet_loss/t0_linear = 0.035082, adanet_loss/t1_linear = 0.035048, adanet_loss/t1_1_layer_dnn = 0.031544
INFO:tensorflow:Finished ensemble evaluation for iteration 1
INFO:tensorflow:'t1_1_layer_dnn' at index 2 is moving onto the next iteration
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-1.txt: ['0:linear', '1:1_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Warm-starting from: (u'/tmp/tmpU33rCk/model.ckpt-40000',)
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_linear/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_linear/adanet/iteration_1/candidate_t0_linear/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/weighted_subnetwork_0/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_linear/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: global_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_1_layer_dnn/adanet/iteration_1/candidate_t1_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/weighted_subnetwork_0/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_linear/adanet/iteration_1/candidate_t0_linear/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_linear/adanet/iteration_0/candidate_t0_linear/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_linear/adanet/iteration_0/candidate_t0_linear/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_1_layer_dnn/adanet/iteration_1/candidate_t1_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Building iteration 2
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Overwriting checkpoint with new graph for iteration 2 to /tmp/tmpU33rCk/model.ckpt-40000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Finished bookkeeping phase for iteration 1
INFO:tensorflow:Beginning training AdaNet iteration 2
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-1.txt: ['0:linear', '1:1_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building iteration 2
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpU33rCk/increment.ckpt-2
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmpU33rCk/model.ckpt.
INFO:tensorflow:loss = 0.026201203, step = 40001
INFO:tensorflow:global_step/sec: 94.3463
INFO:tensorflow:loss = 0.026093123, step = 40101 (1.061 sec)
INFO:tensorflow:global_step/sec: 517.928
INFO:tensorflow:loss = 0.028024156, step = 40201 (0.193 sec)
INFO:tensorflow:global_step/sec: 486.175
INFO:tensorflow:loss = 0.025919124, step = 40301 (0.206 sec)
INFO:tensorflow:global_step/sec: 514.963
INFO:tensorflow:loss = 0.03824448, step = 40401 (0.194 sec)
INFO:tensorflow:global_step/sec: 497.851
INFO:tensorflow:loss = 0.021180643, step = 40501 (0.201 sec)
INFO:tensorflow:global_step/sec: 505.048
INFO:tensorflow:loss = 0.02561801, step = 40601 (0.198 sec)
INFO:tensorflow:global_step/sec: 493.109
INFO:tensorflow:loss = 0.013173097, step = 40701 (0.203 sec)
INFO:tensorflow:global_step/sec: 494.829
INFO:tensorflow:loss = 0.013205946, step = 40801 (0.202 sec)
INFO:tensorflow:global_step/sec: 474.102
INFO:tensorflow:loss = 0.021362148, step = 40901 (0.211 sec)
INFO:tensorflow:global_step/sec: 493.635
INFO:tensorflow:loss = 0.025423564, step = 41001 (0.203 sec)
INFO:tensorflow:global_step/sec: 494.193
INFO:tensorflow:loss = 0.0243335, step = 41101 (0.203 sec)
INFO:tensorflow:global_step/sec: 459.692
INFO:tensorflow:loss = 0.022402506, step = 41201 (0.217 sec)
INFO:tensorflow:global_step/sec: 502.384
INFO:tensorflow:loss = 0.056009114, step = 41301 (0.199 sec)
INFO:tensorflow:global_step/sec: 502.717
INFO:tensorflow:loss = 0.011265494, step = 41401 (0.199 sec)
INFO:tensorflow:global_step/sec: 493.873
INFO:tensorflow:loss = 0.013360662, step = 41501 (0.203 sec)
INFO:tensorflow:global_step/sec: 495.582
INFO:tensorflow:loss = 0.018083222, step = 41601 (0.202 sec)
INFO:tensorflow:global_step/sec: 494.103
INFO:tensorflow:loss = 0.027759094, step = 41701 (0.203 sec)
INFO:tensorflow:global_step/sec: 476.501
INFO:tensorflow:loss = 0.021066925, step = 41801 (0.210 sec)
INFO:tensorflow:global_step/sec: 489.445
INFO:tensorflow:loss = 0.03532522, step = 41901 (0.205 sec)
INFO:tensorflow:global_step/sec: 474.552
INFO:tensorflow:loss = 0.024856593, step = 42001 (0.210 sec)
INFO:tensorflow:global_step/sec: 474.226
INFO:tensorflow:loss = 0.039143093, step = 42101 (0.211 sec)
INFO:tensorflow:global_step/sec: 473.887
INFO:tensorflow:loss = 0.020432828, step = 42201 (0.211 sec)
INFO:tensorflow:global_step/sec: 504.587
INFO:tensorflow:loss = 0.016078953, step = 42301 (0.198 sec)
INFO:tensorflow:global_step/sec: 543.227
INFO:tensorflow:loss = 0.01749191, step = 42401 (0.184 sec)
INFO:tensorflow:global_step/sec: 513.405
INFO:tensorflow:loss = 0.030648103, step = 42501 (0.195 sec)
INFO:tensorflow:global_step/sec: 497.01
INFO:tensorflow:loss = 0.023955716, step = 42601 (0.201 sec)
INFO:tensorflow:global_step/sec: 491.169
INFO:tensorflow:loss = 0.02257087, step = 42701 (0.204 sec)
INFO:tensorflow:global_step/sec: 505.155
INFO:tensorflow:loss = 0.012919056, step = 42801 (0.198 sec)
INFO:tensorflow:global_step/sec: 507.949
INFO:tensorflow:loss = 0.02518668, step = 42901 (0.197 sec)
INFO:tensorflow:global_step/sec: 512.492
INFO:tensorflow:loss = 0.016773593, step = 43001 (0.195 sec)
INFO:tensorflow:global_step/sec: 480.144
INFO:tensorflow:loss = 0.010921106, step = 43101 (0.208 sec)
INFO:tensorflow:global_step/sec: 459.276
INFO:tensorflow:loss = 0.020361234, step = 43201 (0.218 sec)
INFO:tensorflow:global_step/sec: 509.892
INFO:tensorflow:loss = 0.02932311, step = 43301 (0.196 sec)
INFO:tensorflow:global_step/sec: 532.62
INFO:tensorflow:loss = 0.018495196, step = 43401 (0.188 sec)
INFO:tensorflow:global_step/sec: 497.785
INFO:tensorflow:loss = 0.046665255, step = 43501 (0.201 sec)
INFO:tensorflow:global_step/sec: 466.057
INFO:tensorflow:loss = 0.01727711, step = 43601 (0.214 sec)
INFO:tensorflow:global_step/sec: 489.098
INFO:tensorflow:loss = 0.015068072, step = 43701 (0.205 sec)
INFO:tensorflow:global_step/sec: 502.207
INFO:tensorflow:loss = 0.016884852, step = 43801 (0.199 sec)
INFO:tensorflow:global_step/sec: 480.13
INFO:tensorflow:loss = 0.016389422, step = 43901 (0.208 sec)
INFO:tensorflow:global_step/sec: 511.187
INFO:tensorflow:loss = 0.031890914, step = 44001 (0.195 sec)
INFO:tensorflow:global_step/sec: 495.717
INFO:tensorflow:loss = 0.018032018, step = 44101 (0.202 sec)
INFO:tensorflow:global_step/sec: 503.54
INFO:tensorflow:loss = 0.012677894, step = 44201 (0.199 sec)
INFO:tensorflow:global_step/sec: 472.958
INFO:tensorflow:loss = 0.0093172705, step = 44301 (0.211 sec)
INFO:tensorflow:global_step/sec: 498.149
INFO:tensorflow:loss = 0.013411153, step = 44401 (0.201 sec)
INFO:tensorflow:global_step/sec: 501.806
INFO:tensorflow:loss = 0.024332076, step = 44501 (0.200 sec)
INFO:tensorflow:global_step/sec: 496.596
INFO:tensorflow:loss = 0.017249683, step = 44601 (0.201 sec)
INFO:tensorflow:global_step/sec: 509.783
INFO:tensorflow:loss = 0.04461584, step = 44701 (0.196 sec)
INFO:tensorflow:global_step/sec: 516.438
INFO:tensorflow:loss = 0.017492075, step = 44801 (0.194 sec)
INFO:tensorflow:global_step/sec: 502.894
INFO:tensorflow:loss = 0.01984074, step = 44901 (0.199 sec)
INFO:tensorflow:global_step/sec: 505.094
INFO:tensorflow:loss = 0.013942476, step = 45001 (0.198 sec)
INFO:tensorflow:global_step/sec: 460.284
INFO:tensorflow:loss = 0.014163842, step = 45101 (0.217 sec)
INFO:tensorflow:global_step/sec: 492.434
INFO:tensorflow:loss = 0.013226744, step = 45201 (0.203 sec)
INFO:tensorflow:global_step/sec: 511.428
INFO:tensorflow:loss = 0.04130336, step = 45301 (0.197 sec)
INFO:tensorflow:global_step/sec: 512.321
INFO:tensorflow:loss = 0.012158102, step = 45401 (0.194 sec)
INFO:tensorflow:global_step/sec: 516.734
INFO:tensorflow:loss = 0.013963826, step = 45501 (0.193 sec)
INFO:tensorflow:global_step/sec: 491.527
INFO:tensorflow:loss = 0.02029209, step = 45601 (0.204 sec)
INFO:tensorflow:global_step/sec: 492.858
INFO:tensorflow:loss = 0.019698065, step = 45701 (0.203 sec)
INFO:tensorflow:global_step/sec: 496.645
INFO:tensorflow:loss = 0.01769293, step = 45801 (0.202 sec)
INFO:tensorflow:global_step/sec: 517.042
INFO:tensorflow:loss = 0.007403482, step = 45901 (0.196 sec)
INFO:tensorflow:global_step/sec: 500.964
INFO:tensorflow:loss = 0.019337641, step = 46001 (0.197 sec)
INFO:tensorflow:global_step/sec: 483.474
INFO:tensorflow:loss = 0.02821711, step = 46101 (0.211 sec)
INFO:tensorflow:global_step/sec: 469.759
INFO:tensorflow:loss = 0.024295965, step = 46201 (0.208 sec)
INFO:tensorflow:global_step/sec: 494.364
INFO:tensorflow:loss = 0.02877579, step = 46301 (0.202 sec)
INFO:tensorflow:global_step/sec: 499.813
INFO:tensorflow:loss = 0.039782353, step = 46401 (0.201 sec)
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INFO:tensorflow:global_step/sec: 483.985
INFO:tensorflow:loss = 0.033054538, step = 50201 (0.207 sec)
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INFO:tensorflow:global_step/sec: 490.479
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INFO:tensorflow:global_step/sec: 507.174
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INFO:tensorflow:global_step/sec: 528.388
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INFO:tensorflow:global_step/sec: 436.382
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INFO:tensorflow:global_step/sec: 501.562
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INFO:tensorflow:global_step/sec: 503.393
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INFO:tensorflow:global_step/sec: 495.847
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INFO:tensorflow:global_step/sec: 501.645
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INFO:tensorflow:global_step/sec: 477.282
INFO:tensorflow:loss = 0.011738155, step = 53201 (0.209 sec)
INFO:tensorflow:global_step/sec: 482.635
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INFO:tensorflow:global_step/sec: 475.606
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INFO:tensorflow:global_step/sec: 518.551
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INFO:tensorflow:global_step/sec: 509.053
INFO:tensorflow:loss = 0.012389019, step = 54001 (0.196 sec)
INFO:tensorflow:global_step/sec: 480.34
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INFO:tensorflow:global_step/sec: 489.512
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INFO:tensorflow:global_step/sec: 488.031
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INFO:tensorflow:global_step/sec: 487.318
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INFO:tensorflow:global_step/sec: 493.966
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INFO:tensorflow:global_step/sec: 490.831
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INFO:tensorflow:global_step/sec: 478.098
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INFO:tensorflow:global_step/sec: 478.781
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INFO:tensorflow:global_step/sec: 513.745
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INFO:tensorflow:global_step/sec: 515.188
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INFO:tensorflow:global_step/sec: 518.355
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INFO:tensorflow:global_step/sec: 486.289
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INFO:tensorflow:global_step/sec: 483.545
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INFO:tensorflow:global_step/sec: 475.885
INFO:tensorflow:loss = 0.009764336, step = 55401 (0.210 sec)
INFO:tensorflow:global_step/sec: 496.645
INFO:tensorflow:loss = 0.023222383, step = 55501 (0.201 sec)
INFO:tensorflow:global_step/sec: 486.084
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INFO:tensorflow:global_step/sec: 472.016
INFO:tensorflow:loss = 0.03277654, step = 55701 (0.212 sec)
INFO:tensorflow:global_step/sec: 495.236
INFO:tensorflow:loss = 0.027625782, step = 55801 (0.202 sec)
INFO:tensorflow:global_step/sec: 491.458
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INFO:tensorflow:global_step/sec: 509.349
INFO:tensorflow:loss = 0.023520954, step = 56001 (0.196 sec)
INFO:tensorflow:global_step/sec: 510.329
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INFO:tensorflow:global_step/sec: 486.133
INFO:tensorflow:loss = 0.01003485, step = 56201 (0.205 sec)
INFO:tensorflow:global_step/sec: 510.092
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INFO:tensorflow:global_step/sec: 512.403
INFO:tensorflow:loss = 0.026615448, step = 56401 (0.195 sec)
INFO:tensorflow:global_step/sec: 503.733
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INFO:tensorflow:global_step/sec: 486.393
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INFO:tensorflow:global_step/sec: 468.049
INFO:tensorflow:loss = 0.017364534, step = 56701 (0.214 sec)
INFO:tensorflow:global_step/sec: 496.162
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INFO:tensorflow:global_step/sec: 499.616
INFO:tensorflow:loss = 0.012617761, step = 56901 (0.201 sec)
INFO:tensorflow:global_step/sec: 509.315
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INFO:tensorflow:global_step/sec: 512.602
INFO:tensorflow:loss = 0.021613391, step = 57101 (0.195 sec)
INFO:tensorflow:global_step/sec: 467.97
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INFO:tensorflow:global_step/sec: 482.016
INFO:tensorflow:loss = 0.018750113, step = 57301 (0.208 sec)
INFO:tensorflow:global_step/sec: 490.028
INFO:tensorflow:loss = 0.017790722, step = 57401 (0.204 sec)
INFO:tensorflow:global_step/sec: 497.622
INFO:tensorflow:loss = 0.020575833, step = 57501 (0.204 sec)
INFO:tensorflow:global_step/sec: 472.974
INFO:tensorflow:loss = 0.019994969, step = 57601 (0.208 sec)
INFO:tensorflow:global_step/sec: 497.001
INFO:tensorflow:loss = 0.021272149, step = 57701 (0.202 sec)
INFO:tensorflow:global_step/sec: 501.628
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INFO:tensorflow:global_step/sec: 502.993
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INFO:tensorflow:global_step/sec: 503.758
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INFO:tensorflow:global_step/sec: 456.351
INFO:tensorflow:loss = 0.018541595, step = 58201 (0.219 sec)
INFO:tensorflow:global_step/sec: 493.559
INFO:tensorflow:loss = 0.019901603, step = 58301 (0.203 sec)
INFO:tensorflow:global_step/sec: 485.557
INFO:tensorflow:loss = 0.029426899, step = 58401 (0.206 sec)
INFO:tensorflow:global_step/sec: 487.159
INFO:tensorflow:loss = 0.019241655, step = 58501 (0.205 sec)
INFO:tensorflow:global_step/sec: 494.266
INFO:tensorflow:loss = 0.016469326, step = 58601 (0.202 sec)
INFO:tensorflow:global_step/sec: 519.923
INFO:tensorflow:loss = 0.021836523, step = 58701 (0.192 sec)
INFO:tensorflow:global_step/sec: 506.186
INFO:tensorflow:loss = 0.014409851, step = 58801 (0.198 sec)
INFO:tensorflow:global_step/sec: 500.854
INFO:tensorflow:loss = 0.023873296, step = 58901 (0.203 sec)
INFO:tensorflow:global_step/sec: 474.735
INFO:tensorflow:loss = 0.011066675, step = 59001 (0.207 sec)
INFO:tensorflow:global_step/sec: 462.548
INFO:tensorflow:loss = 0.025976984, step = 59101 (0.216 sec)
INFO:tensorflow:global_step/sec: 495.194
INFO:tensorflow:loss = 0.022162579, step = 59201 (0.202 sec)
INFO:tensorflow:global_step/sec: 503.867
INFO:tensorflow:loss = 0.011563149, step = 59301 (0.199 sec)
INFO:tensorflow:global_step/sec: 518.912
INFO:tensorflow:loss = 0.015920684, step = 59401 (0.192 sec)
INFO:tensorflow:global_step/sec: 509.084
INFO:tensorflow:loss = 0.0122279115, step = 59501 (0.197 sec)
INFO:tensorflow:global_step/sec: 486.934
INFO:tensorflow:loss = 0.01201019, step = 59601 (0.206 sec)
INFO:tensorflow:global_step/sec: 492.735
INFO:tensorflow:loss = 0.012843441, step = 59701 (0.207 sec)
INFO:tensorflow:global_step/sec: 476.856
INFO:tensorflow:loss = 0.014685018, step = 59801 (0.206 sec)
INFO:tensorflow:global_step/sec: 493.486
INFO:tensorflow:loss = 0.02178935, step = 59901 (0.202 sec)
INFO:tensorflow:Saving checkpoints for 60000 into /tmp/tmpU33rCk/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-1.txt: ['0:linear', '1:1_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building iteration 2
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:36:21
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpU33rCk/model.ckpt-60000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't1_1_layer_dnn' dict for global step 60000: architecture/adanet/ensembles =
j
>adanet/iteration_1/ensemble_t1_1_layer_dnn/architecture/adanetB B| linear | 1_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.04422355, average_loss/adanet/subnetwork = 0.044653624, average_loss/adanet/uniform_average_ensemble = 0.043328855, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.060774922, loss/adanet/subnetwork = 0.06800773, loss/adanet/uniform_average_ensemble = 0.061006997, prediction/mean/adanet/adanet_weighted_ensemble = 3.1278303, prediction/mean/adanet/subnetwork = 3.1593368, prediction/mean/adanet/uniform_average_ensemble = 3.1326156
INFO:tensorflow:Saving candidate 't2_1_layer_dnn' dict for global step 60000: architecture/adanet/ensembles =
x
>adanet/iteration_2/ensemble_t2_1_layer_dnn/architecture/adanetB, B&| linear | 1_layer_dnn | 1_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.04198682, average_loss/adanet/subnetwork = 0.0445389, average_loss/adanet/uniform_average_ensemble = 0.042342477, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.0576995, loss/adanet/subnetwork = 0.06806376, loss/adanet/uniform_average_ensemble = 0.061814114, prediction/mean/adanet/adanet_weighted_ensemble = 3.0984364, prediction/mean/adanet/subnetwork = 3.1642232, prediction/mean/adanet/uniform_average_ensemble = 3.143152
INFO:tensorflow:Saving candidate 't2_2_layer_dnn' dict for global step 60000: architecture/adanet/ensembles =
x
>adanet/iteration_2/ensemble_t2_2_layer_dnn/architecture/adanetB, B&| linear | 1_layer_dnn | 2_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.03654939, average_loss/adanet/subnetwork = 0.032713592, average_loss/adanet/uniform_average_ensemble = 0.036697652, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.05076569, loss/adanet/subnetwork = 0.043944843, loss/adanet/uniform_average_ensemble = 0.052397445, prediction/mean/adanet/adanet_weighted_ensemble = 3.1145082, prediction/mean/adanet/subnetwork = 3.1556947, prediction/mean/adanet/uniform_average_ensemble = 3.140309
INFO:tensorflow:Finished evaluation at 2018-12-13-19:36:24
INFO:tensorflow:Saving dict for global step 60000: average_loss = 0.03654939, average_loss/adanet/adanet_weighted_ensemble = 0.03654939, average_loss/adanet/subnetwork = 0.032713592, average_loss/adanet/uniform_average_ensemble = 0.036697652, global_step = 60000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.05076569, loss/adanet/adanet_weighted_ensemble = 0.05076569, loss/adanet/subnetwork = 0.043944843, loss/adanet/uniform_average_ensemble = 0.052397445, prediction/mean = 3.1145082, prediction/mean/adanet/adanet_weighted_ensemble = 3.1145082, prediction/mean/adanet/subnetwork = 3.1556947, prediction/mean/adanet/uniform_average_ensemble = 3.140309
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 60000: /tmp/tmpU33rCk/model.ckpt-60000
INFO:tensorflow:Loss for final step: 0.023291564.
INFO:tensorflow:Finished training Adanet iteration 2
INFO:tensorflow:Beginning bookkeeping phase for iteration 2
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-1.txt: ['0:linear', '1:1_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building iteration 2
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Starting ensemble evaluation for iteration 2
INFO:tensorflow:Restoring parameters from /tmp/tmpU33rCk/model.ckpt-60000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Encountered end of input after 14 evaluations
INFO:tensorflow:Computed ensemble metrics: adanet_loss/t1_1_layer_dnn = 0.031544, adanet_loss/t2_1_layer_dnn = 0.029996, adanet_loss/t2_2_layer_dnn = 0.027457
INFO:tensorflow:Finished ensemble evaluation for iteration 2
INFO:tensorflow:'t2_2_layer_dnn' at index 2 is moving onto the next iteration
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-2.txt: ['0:linear', '1:1_layer_dnn', '2:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Warm-starting from: (u'/tmp/tmpU33rCk/model.ckpt-60000',)
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_linear/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_linear/adanet/iteration_1/candidate_t0_linear/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_2/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/weighted_subnetwork_0/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_2/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_2/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_linear/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_1_layer_dnn/adanet/iteration_2/candidate_t1_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: global_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_2/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_1_layer_dnn/adanet/iteration_1/candidate_t1_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_2/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/weighted_subnetwork_0/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_1_layer_dnn/adanet/iteration_2/candidate_t1_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_2_layer_dnn/adanet/iteration_2/candidate_t2_2_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_2_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_2/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_2/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_2_layer_dnn/adanet/iteration_2/candidate_t2_2_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_linear/adanet/iteration_1/candidate_t0_linear/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_linear/adanet/iteration_0/candidate_t0_linear/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_linear/adanet/iteration_0/candidate_t0_linear/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_1_layer_dnn/adanet/iteration_1/candidate_t1_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Building iteration 3
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Overwriting checkpoint with new graph for iteration 3 to /tmp/tmpU33rCk/model.ckpt-60000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Finished bookkeeping phase for iteration 2
INFO:tensorflow:Beginning training AdaNet iteration 3
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-2.txt: ['0:linear', '1:1_layer_dnn', '2:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building iteration 3
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpU33rCk/increment.ckpt-3
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 60000 into /tmp/tmpU33rCk/model.ckpt.
INFO:tensorflow:loss = 0.01849521, step = 60001
INFO:tensorflow:global_step/sec: 78.0646
INFO:tensorflow:loss = 0.018221313, step = 60101 (1.282 sec)
INFO:tensorflow:global_step/sec: 434.75
INFO:tensorflow:loss = 0.01849538, step = 60201 (0.230 sec)
INFO:tensorflow:global_step/sec: 446.038
INFO:tensorflow:loss = 0.016187005, step = 60301 (0.224 sec)
INFO:tensorflow:global_step/sec: 436.706
INFO:tensorflow:loss = 0.02324728, step = 60401 (0.229 sec)
INFO:tensorflow:global_step/sec: 428.09
INFO:tensorflow:loss = 0.012834492, step = 60501 (0.233 sec)
INFO:tensorflow:global_step/sec: 433.341
INFO:tensorflow:loss = 0.015347466, step = 60601 (0.236 sec)
INFO:tensorflow:global_step/sec: 399.925
INFO:tensorflow:loss = 0.009656646, step = 60701 (0.245 sec)
INFO:tensorflow:global_step/sec: 424.715
INFO:tensorflow:loss = 0.0098030735, step = 60801 (0.235 sec)
INFO:tensorflow:global_step/sec: 423.418
INFO:tensorflow:loss = 0.015358811, step = 60901 (0.236 sec)
INFO:tensorflow:global_step/sec: 427.33
INFO:tensorflow:loss = 0.017170426, step = 61001 (0.234 sec)
INFO:tensorflow:global_step/sec: 440.126
INFO:tensorflow:loss = 0.01597068, step = 61101 (0.227 sec)
INFO:tensorflow:global_step/sec: 429.895
INFO:tensorflow:loss = 0.015691243, step = 61201 (0.233 sec)
INFO:tensorflow:global_step/sec: 446.663
INFO:tensorflow:loss = 0.032044083, step = 61301 (0.224 sec)
INFO:tensorflow:global_step/sec: 458.96
INFO:tensorflow:loss = 0.0077379774, step = 61401 (0.218 sec)
INFO:tensorflow:global_step/sec: 418.322
INFO:tensorflow:loss = 0.008269019, step = 61501 (0.239 sec)
INFO:tensorflow:global_step/sec: 438.034
INFO:tensorflow:loss = 0.0090968115, step = 61601 (0.228 sec)
INFO:tensorflow:global_step/sec: 450.816
INFO:tensorflow:loss = 0.017395135, step = 61701 (0.222 sec)
INFO:tensorflow:global_step/sec: 426.758
INFO:tensorflow:loss = 0.0142538585, step = 61801 (0.234 sec)
INFO:tensorflow:global_step/sec: 433.994
INFO:tensorflow:loss = 0.021575892, step = 61901 (0.230 sec)
INFO:tensorflow:global_step/sec: 421.07
INFO:tensorflow:loss = 0.014129536, step = 62001 (0.237 sec)
INFO:tensorflow:global_step/sec: 437.777
INFO:tensorflow:loss = 0.026330313, step = 62101 (0.229 sec)
INFO:tensorflow:global_step/sec: 419.702
INFO:tensorflow:loss = 0.0139617715, step = 62201 (0.238 sec)
INFO:tensorflow:global_step/sec: 432.636
INFO:tensorflow:loss = 0.009793356, step = 62301 (0.231 sec)
INFO:tensorflow:global_step/sec: 431.287
INFO:tensorflow:loss = 0.010544407, step = 62401 (0.232 sec)
INFO:tensorflow:global_step/sec: 457.463
INFO:tensorflow:loss = 0.019151235, step = 62501 (0.219 sec)
INFO:tensorflow:global_step/sec: 459.025
INFO:tensorflow:loss = 0.01287616, step = 62601 (0.218 sec)
INFO:tensorflow:global_step/sec: 449.174
INFO:tensorflow:loss = 0.014950998, step = 62701 (0.223 sec)
INFO:tensorflow:global_step/sec: 433.467
INFO:tensorflow:loss = 0.007641917, step = 62801 (0.231 sec)
INFO:tensorflow:global_step/sec: 443.762
INFO:tensorflow:loss = 0.013454292, step = 62901 (0.225 sec)
INFO:tensorflow:global_step/sec: 441.784
INFO:tensorflow:loss = 0.01109894, step = 63001 (0.227 sec)
INFO:tensorflow:global_step/sec: 420.142
INFO:tensorflow:loss = 0.008401312, step = 63101 (0.238 sec)
INFO:tensorflow:global_step/sec: 455.998
INFO:tensorflow:loss = 0.015624371, step = 63201 (0.219 sec)
INFO:tensorflow:global_step/sec: 438.82
INFO:tensorflow:loss = 0.019629583, step = 63301 (0.228 sec)
INFO:tensorflow:global_step/sec: 435.159
INFO:tensorflow:loss = 0.012380507, step = 63401 (0.230 sec)
INFO:tensorflow:global_step/sec: 423.594
INFO:tensorflow:loss = 0.027570924, step = 63501 (0.236 sec)
INFO:tensorflow:global_step/sec: 442.672
INFO:tensorflow:loss = 0.010853866, step = 63601 (0.226 sec)
INFO:tensorflow:global_step/sec: 405.876
INFO:tensorflow:loss = 0.011129044, step = 63701 (0.247 sec)
INFO:tensorflow:global_step/sec: 436.927
INFO:tensorflow:loss = 0.009922019, step = 63801 (0.229 sec)
INFO:tensorflow:global_step/sec: 447.684
INFO:tensorflow:loss = 0.00835341, step = 63901 (0.224 sec)
INFO:tensorflow:global_step/sec: 423.123
INFO:tensorflow:loss = 0.017287806, step = 64001 (0.241 sec)
INFO:tensorflow:global_step/sec: 433.067
INFO:tensorflow:loss = 0.010719417, step = 64101 (0.226 sec)
INFO:tensorflow:global_step/sec: 445.943
INFO:tensorflow:loss = 0.0088530835, step = 64201 (0.224 sec)
INFO:tensorflow:global_step/sec: 441.675
INFO:tensorflow:loss = 0.007045265, step = 64301 (0.226 sec)
INFO:tensorflow:global_step/sec: 435.802
INFO:tensorflow:loss = 0.010601383, step = 64401 (0.229 sec)
INFO:tensorflow:global_step/sec: 442.292
INFO:tensorflow:loss = 0.014272485, step = 64501 (0.226 sec)
INFO:tensorflow:global_step/sec: 446.148
INFO:tensorflow:loss = 0.011902935, step = 64601 (0.224 sec)
INFO:tensorflow:global_step/sec: 386.881
INFO:tensorflow:loss = 0.021894043, step = 64701 (0.258 sec)
INFO:tensorflow:global_step/sec: 434.687
INFO:tensorflow:loss = 0.011304423, step = 64801 (0.230 sec)
INFO:tensorflow:global_step/sec: 447.015
INFO:tensorflow:loss = 0.011946198, step = 64901 (0.224 sec)
INFO:tensorflow:global_step/sec: 430.878
INFO:tensorflow:loss = 0.0061903694, step = 65001 (0.232 sec)
INFO:tensorflow:global_step/sec: 443.07
INFO:tensorflow:loss = 0.009150965, step = 65101 (0.226 sec)
INFO:tensorflow:global_step/sec: 443.768
INFO:tensorflow:loss = 0.009455716, step = 65201 (0.225 sec)
INFO:tensorflow:global_step/sec: 438.064
INFO:tensorflow:loss = 0.022442203, step = 65301 (0.228 sec)
INFO:tensorflow:global_step/sec: 453.221
INFO:tensorflow:loss = 0.0081521, step = 65401 (0.221 sec)
INFO:tensorflow:global_step/sec: 438.333
INFO:tensorflow:loss = 0.009311147, step = 65501 (0.228 sec)
INFO:tensorflow:global_step/sec: 449.745
INFO:tensorflow:loss = 0.013529962, step = 65601 (0.222 sec)
INFO:tensorflow:global_step/sec: 449.759
INFO:tensorflow:loss = 0.010945886, step = 65701 (0.222 sec)
INFO:tensorflow:global_step/sec: 467.687
INFO:tensorflow:loss = 0.012356184, step = 65801 (0.214 sec)
INFO:tensorflow:global_step/sec: 439.224
INFO:tensorflow:loss = 0.00745131, step = 65901 (0.227 sec)
INFO:tensorflow:global_step/sec: 450.264
INFO:tensorflow:loss = 0.013327674, step = 66001 (0.222 sec)
INFO:tensorflow:global_step/sec: 391.416
INFO:tensorflow:loss = 0.016667463, step = 66101 (0.256 sec)
INFO:tensorflow:global_step/sec: 452.796
INFO:tensorflow:loss = 0.016279226, step = 66201 (0.221 sec)
INFO:tensorflow:global_step/sec: 435.819
INFO:tensorflow:loss = 0.013686002, step = 66301 (0.230 sec)
INFO:tensorflow:global_step/sec: 464.829
INFO:tensorflow:loss = 0.019556196, step = 66401 (0.215 sec)
INFO:tensorflow:global_step/sec: 456.261
INFO:tensorflow:loss = 0.019318718, step = 66501 (0.219 sec)
INFO:tensorflow:global_step/sec: 455.689
INFO:tensorflow:loss = 0.009969889, step = 66601 (0.220 sec)
INFO:tensorflow:global_step/sec: 469.437
INFO:tensorflow:loss = 0.015530272, step = 66701 (0.212 sec)
INFO:tensorflow:global_step/sec: 448.356
INFO:tensorflow:loss = 0.009502322, step = 66801 (0.223 sec)
INFO:tensorflow:global_step/sec: 473.653
INFO:tensorflow:loss = 0.005345877, step = 66901 (0.212 sec)
INFO:tensorflow:global_step/sec: 458.18
INFO:tensorflow:loss = 0.011250553, step = 67001 (0.218 sec)
INFO:tensorflow:global_step/sec: 459.257
INFO:tensorflow:loss = 0.00852987, step = 67101 (0.218 sec)
INFO:tensorflow:global_step/sec: 436.399
INFO:tensorflow:loss = 0.010775156, step = 67201 (0.229 sec)
INFO:tensorflow:global_step/sec: 436.717
INFO:tensorflow:loss = 0.025048286, step = 67301 (0.229 sec)
INFO:tensorflow:global_step/sec: 462.479
INFO:tensorflow:loss = 0.013347473, step = 67401 (0.216 sec)
INFO:tensorflow:global_step/sec: 430.719
INFO:tensorflow:loss = 0.013946894, step = 67501 (0.232 sec)
INFO:tensorflow:global_step/sec: 447.986
INFO:tensorflow:loss = 0.008865064, step = 67601 (0.223 sec)
INFO:tensorflow:global_step/sec: 438.656
INFO:tensorflow:loss = 0.014698386, step = 67701 (0.228 sec)
INFO:tensorflow:global_step/sec: 424.978
INFO:tensorflow:loss = 0.0154539235, step = 67801 (0.235 sec)
INFO:tensorflow:global_step/sec: 430.234
INFO:tensorflow:loss = 0.011255688, step = 67901 (0.233 sec)
INFO:tensorflow:global_step/sec: 430.048
INFO:tensorflow:loss = 0.015850207, step = 68001 (0.232 sec)
INFO:tensorflow:global_step/sec: 415.735
INFO:tensorflow:loss = 0.013518164, step = 68101 (0.241 sec)
INFO:tensorflow:global_step/sec: 422.744
INFO:tensorflow:loss = 0.014690641, step = 68201 (0.236 sec)
INFO:tensorflow:global_step/sec: 442.805
INFO:tensorflow:loss = 0.016613279, step = 68301 (0.226 sec)
INFO:tensorflow:global_step/sec: 440.044
INFO:tensorflow:loss = 0.014170462, step = 68401 (0.227 sec)
INFO:tensorflow:global_step/sec: 472.751
INFO:tensorflow:loss = 0.013066348, step = 68501 (0.211 sec)
INFO:tensorflow:global_step/sec: 461.455
INFO:tensorflow:loss = 0.016186215, step = 68601 (0.217 sec)
INFO:tensorflow:global_step/sec: 469.914
INFO:tensorflow:loss = 0.012064418, step = 68701 (0.213 sec)
INFO:tensorflow:global_step/sec: 488.141
INFO:tensorflow:loss = 0.019498233, step = 68801 (0.206 sec)
INFO:tensorflow:global_step/sec: 455.88
INFO:tensorflow:loss = 0.029157348, step = 68901 (0.221 sec)
INFO:tensorflow:global_step/sec: 410.979
INFO:tensorflow:loss = 0.025464673, step = 69001 (0.241 sec)
INFO:tensorflow:global_step/sec: 447.237
INFO:tensorflow:loss = 0.012853953, step = 69101 (0.224 sec)
INFO:tensorflow:global_step/sec: 445.46
INFO:tensorflow:loss = 0.01971712, step = 69201 (0.224 sec)
INFO:tensorflow:global_step/sec: 455.365
INFO:tensorflow:loss = 0.00933605, step = 69301 (0.219 sec)
INFO:tensorflow:global_step/sec: 449.188
INFO:tensorflow:loss = 0.008620865, step = 69401 (0.223 sec)
INFO:tensorflow:global_step/sec: 432.865
INFO:tensorflow:loss = 0.02142889, step = 69501 (0.231 sec)
INFO:tensorflow:global_step/sec: 422.422
INFO:tensorflow:loss = 0.013078446, step = 69601 (0.237 sec)
INFO:tensorflow:global_step/sec: 415.236
INFO:tensorflow:loss = 0.007206355, step = 69701 (0.241 sec)
INFO:tensorflow:global_step/sec: 410.206
INFO:tensorflow:loss = 0.011162484, step = 69801 (0.244 sec)
INFO:tensorflow:global_step/sec: 424.856
INFO:tensorflow:loss = 0.014292128, step = 69901 (0.235 sec)
INFO:tensorflow:global_step/sec: 451.229
INFO:tensorflow:loss = 0.0128045315, step = 70001 (0.221 sec)
INFO:tensorflow:global_step/sec: 441.995
INFO:tensorflow:loss = 0.009628586, step = 70101 (0.226 sec)
INFO:tensorflow:global_step/sec: 459.095
INFO:tensorflow:loss = 0.017084569, step = 70201 (0.218 sec)
INFO:tensorflow:global_step/sec: 449.281
INFO:tensorflow:loss = 0.020728739, step = 70301 (0.223 sec)
INFO:tensorflow:global_step/sec: 458.651
INFO:tensorflow:loss = 0.008801332, step = 70401 (0.218 sec)
INFO:tensorflow:global_step/sec: 460.891
INFO:tensorflow:loss = 0.017882807, step = 70501 (0.217 sec)
INFO:tensorflow:global_step/sec: 436.792
INFO:tensorflow:loss = 0.01087595, step = 70601 (0.229 sec)
INFO:tensorflow:global_step/sec: 418.973
INFO:tensorflow:loss = 0.008092202, step = 70701 (0.239 sec)
INFO:tensorflow:global_step/sec: 432.683
INFO:tensorflow:loss = 0.014348139, step = 70801 (0.231 sec)
INFO:tensorflow:global_step/sec: 438.743
INFO:tensorflow:loss = 0.015363082, step = 70901 (0.228 sec)
INFO:tensorflow:global_step/sec: 425.628
INFO:tensorflow:loss = 0.0074509815, step = 71001 (0.235 sec)
INFO:tensorflow:global_step/sec: 445.878
INFO:tensorflow:loss = 0.016008766, step = 71101 (0.224 sec)
INFO:tensorflow:global_step/sec: 427.319
INFO:tensorflow:loss = 0.008940533, step = 71201 (0.234 sec)
INFO:tensorflow:global_step/sec: 446.644
INFO:tensorflow:loss = 0.018691873, step = 71301 (0.224 sec)
INFO:tensorflow:global_step/sec: 433.29
INFO:tensorflow:loss = 0.009328838, step = 71401 (0.231 sec)
INFO:tensorflow:global_step/sec: 414.273
INFO:tensorflow:loss = 0.020122949, step = 71501 (0.242 sec)
INFO:tensorflow:global_step/sec: 416.212
INFO:tensorflow:loss = 0.0081863925, step = 71601 (0.240 sec)
INFO:tensorflow:global_step/sec: 440.94
INFO:tensorflow:loss = 0.015287314, step = 71701 (0.227 sec)
INFO:tensorflow:global_step/sec: 443.737
INFO:tensorflow:loss = 0.008990615, step = 71801 (0.225 sec)
INFO:tensorflow:global_step/sec: 441.201
INFO:tensorflow:loss = 0.014130508, step = 71901 (0.227 sec)
INFO:tensorflow:global_step/sec: 444.709
INFO:tensorflow:loss = 0.012323266, step = 72001 (0.225 sec)
INFO:tensorflow:global_step/sec: 429.538
INFO:tensorflow:loss = 0.0058762175, step = 72101 (0.232 sec)
INFO:tensorflow:global_step/sec: 441.439
INFO:tensorflow:loss = 0.0059047127, step = 72201 (0.230 sec)
INFO:tensorflow:global_step/sec: 430.715
INFO:tensorflow:loss = 0.011101288, step = 72301 (0.229 sec)
INFO:tensorflow:global_step/sec: 460.779
INFO:tensorflow:loss = 0.012443201, step = 72401 (0.217 sec)
INFO:tensorflow:global_step/sec: 435.449
INFO:tensorflow:loss = 0.013553011, step = 72501 (0.230 sec)
INFO:tensorflow:global_step/sec: 464.572
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INFO:tensorflow:global_step/sec: 443.478
INFO:tensorflow:loss = 0.015503101, step = 72701 (0.226 sec)
INFO:tensorflow:global_step/sec: 450.28
INFO:tensorflow:loss = 0.015577295, step = 72801 (0.222 sec)
INFO:tensorflow:global_step/sec: 441.036
INFO:tensorflow:loss = 0.013324114, step = 72901 (0.226 sec)
INFO:tensorflow:global_step/sec: 415.557
INFO:tensorflow:loss = 0.018878024, step = 73001 (0.241 sec)
INFO:tensorflow:global_step/sec: 432.534
INFO:tensorflow:loss = 0.012767976, step = 73101 (0.231 sec)
INFO:tensorflow:global_step/sec: 422.767
INFO:tensorflow:loss = 0.008253518, step = 73201 (0.237 sec)
INFO:tensorflow:global_step/sec: 420.863
INFO:tensorflow:loss = 0.009813534, step = 73301 (0.237 sec)
INFO:tensorflow:global_step/sec: 425.775
INFO:tensorflow:loss = 0.014758039, step = 73401 (0.235 sec)
INFO:tensorflow:global_step/sec: 451.745
INFO:tensorflow:loss = 0.013327519, step = 73501 (0.221 sec)
INFO:tensorflow:global_step/sec: 453.684
INFO:tensorflow:loss = 0.008495433, step = 73601 (0.221 sec)
INFO:tensorflow:global_step/sec: 458.686
INFO:tensorflow:loss = 0.008404325, step = 73701 (0.218 sec)
INFO:tensorflow:global_step/sec: 456.944
INFO:tensorflow:loss = 0.020715604, step = 73801 (0.219 sec)
INFO:tensorflow:global_step/sec: 443.062
INFO:tensorflow:loss = 0.008773352, step = 73901 (0.226 sec)
INFO:tensorflow:global_step/sec: 445.772
INFO:tensorflow:loss = 0.006708255, step = 74001 (0.224 sec)
INFO:tensorflow:global_step/sec: 436.992
INFO:tensorflow:loss = 0.014570928, step = 74101 (0.229 sec)
INFO:tensorflow:global_step/sec: 427.314
INFO:tensorflow:loss = 0.013555666, step = 74201 (0.234 sec)
INFO:tensorflow:global_step/sec: 426.323
INFO:tensorflow:loss = 0.015543242, step = 74301 (0.235 sec)
INFO:tensorflow:global_step/sec: 430.522
INFO:tensorflow:loss = 0.009950759, step = 74401 (0.232 sec)
INFO:tensorflow:global_step/sec: 458.121
INFO:tensorflow:loss = 0.011727323, step = 74501 (0.218 sec)
INFO:tensorflow:global_step/sec: 448.499
INFO:tensorflow:loss = 0.013463419, step = 74601 (0.223 sec)
INFO:tensorflow:global_step/sec: 449.675
INFO:tensorflow:loss = 0.014221058, step = 74701 (0.222 sec)
INFO:tensorflow:global_step/sec: 456.559
INFO:tensorflow:loss = 0.021520961, step = 74801 (0.219 sec)
INFO:tensorflow:global_step/sec: 442.399
INFO:tensorflow:loss = 0.019705988, step = 74901 (0.226 sec)
INFO:tensorflow:global_step/sec: 452.213
INFO:tensorflow:loss = 0.02249371, step = 75001 (0.221 sec)
INFO:tensorflow:global_step/sec: 429.673
INFO:tensorflow:loss = 0.0074728457, step = 75101 (0.232 sec)
INFO:tensorflow:global_step/sec: 424.708
INFO:tensorflow:loss = 0.0101506235, step = 75201 (0.235 sec)
INFO:tensorflow:global_step/sec: 423.449
INFO:tensorflow:loss = 0.017612845, step = 75301 (0.236 sec)
INFO:tensorflow:global_step/sec: 446.245
INFO:tensorflow:loss = 0.008105265, step = 75401 (0.224 sec)
INFO:tensorflow:global_step/sec: 476.808
INFO:tensorflow:loss = 0.018446082, step = 75501 (0.210 sec)
INFO:tensorflow:global_step/sec: 480.908
INFO:tensorflow:loss = 0.017977942, step = 75601 (0.208 sec)
INFO:tensorflow:global_step/sec: 426.707
INFO:tensorflow:loss = 0.015822789, step = 75701 (0.235 sec)
INFO:tensorflow:global_step/sec: 456.861
INFO:tensorflow:loss = 0.022029698, step = 75801 (0.219 sec)
INFO:tensorflow:global_step/sec: 454.081
INFO:tensorflow:loss = 0.013807894, step = 75901 (0.221 sec)
INFO:tensorflow:global_step/sec: 440.413
INFO:tensorflow:loss = 0.016518306, step = 76001 (0.227 sec)
INFO:tensorflow:global_step/sec: 425.114
INFO:tensorflow:loss = 0.012706269, step = 76101 (0.235 sec)
INFO:tensorflow:global_step/sec: 450.359
INFO:tensorflow:loss = 0.007131013, step = 76201 (0.222 sec)
INFO:tensorflow:global_step/sec: 453.875
INFO:tensorflow:loss = 0.0075605772, step = 76301 (0.220 sec)
INFO:tensorflow:global_step/sec: 448.744
INFO:tensorflow:loss = 0.015425386, step = 76401 (0.223 sec)
INFO:tensorflow:global_step/sec: 423.08
INFO:tensorflow:loss = 0.011027876, step = 76501 (0.236 sec)
INFO:tensorflow:global_step/sec: 440.678
INFO:tensorflow:loss = 0.010813015, step = 76601 (0.227 sec)
INFO:tensorflow:global_step/sec: 425.418
INFO:tensorflow:loss = 0.01431744, step = 76701 (0.235 sec)
INFO:tensorflow:global_step/sec: 439.504
INFO:tensorflow:loss = 0.015267668, step = 76801 (0.228 sec)
INFO:tensorflow:global_step/sec: 459.278
INFO:tensorflow:loss = 0.006063135, step = 76901 (0.218 sec)
INFO:tensorflow:global_step/sec: 443.638
INFO:tensorflow:loss = 0.015281367, step = 77001 (0.226 sec)
INFO:tensorflow:global_step/sec: 471.045
INFO:tensorflow:loss = 0.019066438, step = 77101 (0.212 sec)
INFO:tensorflow:global_step/sec: 448.344
INFO:tensorflow:loss = 0.007818027, step = 77201 (0.223 sec)
INFO:tensorflow:global_step/sec: 422.428
INFO:tensorflow:loss = 0.015884731, step = 77301 (0.237 sec)
INFO:tensorflow:global_step/sec: 437.945
INFO:tensorflow:loss = 0.010117618, step = 77401 (0.228 sec)
INFO:tensorflow:global_step/sec: 462.714
INFO:tensorflow:loss = 0.01640339, step = 77501 (0.216 sec)
INFO:tensorflow:global_step/sec: 441.64
INFO:tensorflow:loss = 0.015515845, step = 77601 (0.226 sec)
INFO:tensorflow:global_step/sec: 452.919
INFO:tensorflow:loss = 0.0094986195, step = 77701 (0.221 sec)
INFO:tensorflow:global_step/sec: 437.642
INFO:tensorflow:loss = 0.01584887, step = 77801 (0.228 sec)
INFO:tensorflow:global_step/sec: 439.325
INFO:tensorflow:loss = 0.011430892, step = 77901 (0.228 sec)
INFO:tensorflow:global_step/sec: 444.182
INFO:tensorflow:loss = 0.0141262, step = 78001 (0.226 sec)
INFO:tensorflow:global_step/sec: 448.982
INFO:tensorflow:loss = 0.012604534, step = 78101 (0.221 sec)
INFO:tensorflow:global_step/sec: 459.633
INFO:tensorflow:loss = 0.013469087, step = 78201 (0.218 sec)
INFO:tensorflow:global_step/sec: 444.693
INFO:tensorflow:loss = 0.018378606, step = 78301 (0.225 sec)
INFO:tensorflow:global_step/sec: 456.617
INFO:tensorflow:loss = 0.02119773, step = 78401 (0.219 sec)
INFO:tensorflow:global_step/sec: 443.707
INFO:tensorflow:loss = 0.010857796, step = 78501 (0.225 sec)
INFO:tensorflow:global_step/sec: 436.517
INFO:tensorflow:loss = 0.012341502, step = 78601 (0.229 sec)
INFO:tensorflow:global_step/sec: 451.939
INFO:tensorflow:loss = 0.013480957, step = 78701 (0.225 sec)
INFO:tensorflow:global_step/sec: 450.995
INFO:tensorflow:loss = 0.014637279, step = 78801 (0.217 sec)
INFO:tensorflow:global_step/sec: 393.986
INFO:tensorflow:loss = 0.022157174, step = 78901 (0.254 sec)
INFO:tensorflow:global_step/sec: 442.333
INFO:tensorflow:loss = 0.009894935, step = 79001 (0.226 sec)
INFO:tensorflow:global_step/sec: 452.319
INFO:tensorflow:loss = 0.01841922, step = 79101 (0.221 sec)
INFO:tensorflow:global_step/sec: 461.667
INFO:tensorflow:loss = 0.016005103, step = 79201 (0.216 sec)
INFO:tensorflow:global_step/sec: 451.48
INFO:tensorflow:loss = 0.008755811, step = 79301 (0.222 sec)
INFO:tensorflow:global_step/sec: 458.436
INFO:tensorflow:loss = 0.011758109, step = 79401 (0.218 sec)
INFO:tensorflow:global_step/sec: 435.212
INFO:tensorflow:loss = 0.0098045, step = 79501 (0.230 sec)
INFO:tensorflow:global_step/sec: 425.878
INFO:tensorflow:loss = 0.007837824, step = 79601 (0.235 sec)
INFO:tensorflow:global_step/sec: 439.844
INFO:tensorflow:loss = 0.007720313, step = 79701 (0.227 sec)
INFO:tensorflow:global_step/sec: 449.251
INFO:tensorflow:loss = 0.011834578, step = 79801 (0.223 sec)
INFO:tensorflow:global_step/sec: 433.121
INFO:tensorflow:loss = 0.018032994, step = 79901 (0.231 sec)
INFO:tensorflow:Saving checkpoints for 80000 into /tmp/tmpU33rCk/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-2.txt: ['0:linear', '1:1_layer_dnn', '2:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building iteration 3
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:37:39
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpU33rCk/model.ckpt-80000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't2_2_layer_dnn' dict for global step 80000: architecture/adanet/ensembles =
x
>adanet/iteration_2/ensemble_t2_2_layer_dnn/architecture/adanetB, B&| linear | 1_layer_dnn | 2_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.03654939, average_loss/adanet/subnetwork = 0.032713592, average_loss/adanet/uniform_average_ensemble = 0.036697656, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.05076569, loss/adanet/subnetwork = 0.043944843, loss/adanet/uniform_average_ensemble = 0.052397452, prediction/mean/adanet/adanet_weighted_ensemble = 3.1145082, prediction/mean/adanet/subnetwork = 3.1556947, prediction/mean/adanet/uniform_average_ensemble = 3.140309
INFO:tensorflow:Saving candidate 't3_2_layer_dnn' dict for global step 80000: architecture/adanet/ensembles =
�
>adanet/iteration_3/ensemble_t3_2_layer_dnn/architecture/adanetB: B4| linear | 1_layer_dnn | 2_layer_dnn | 2_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.032998268, average_loss/adanet/subnetwork = 0.04255607, average_loss/adanet/uniform_average_ensemble = 0.036970153, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.042651616, loss/adanet/subnetwork = 0.059904583, loss/adanet/uniform_average_ensemble = 0.05318597, prediction/mean/adanet/adanet_weighted_ensemble = 3.0920377, prediction/mean/adanet/subnetwork = 3.1531146, prediction/mean/adanet/uniform_average_ensemble = 3.1435103
INFO:tensorflow:Saving candidate 't3_3_layer_dnn' dict for global step 80000: architecture/adanet/ensembles =
�
>adanet/iteration_3/ensemble_t3_3_layer_dnn/architecture/adanetB: B4| linear | 1_layer_dnn | 2_layer_dnn | 3_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.03303379, average_loss/adanet/subnetwork = 0.03740776, average_loss/adanet/uniform_average_ensemble = 0.035802316, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.042533625, loss/adanet/subnetwork = 0.055050053, loss/adanet/uniform_average_ensemble = 0.051954698, prediction/mean/adanet/adanet_weighted_ensemble = 3.0902042, prediction/mean/adanet/subnetwork = 3.1547055, prediction/mean/adanet/uniform_average_ensemble = 3.143908
INFO:tensorflow:Finished evaluation at 2018-12-13-19:37:43
INFO:tensorflow:Saving dict for global step 80000: average_loss = 0.032998268, average_loss/adanet/adanet_weighted_ensemble = 0.032998268, average_loss/adanet/subnetwork = 0.04255607, average_loss/adanet/uniform_average_ensemble = 0.036970153, global_step = 80000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.042651616, loss/adanet/adanet_weighted_ensemble = 0.042651616, loss/adanet/subnetwork = 0.059904583, loss/adanet/uniform_average_ensemble = 0.05318597, prediction/mean = 3.0920377, prediction/mean/adanet/adanet_weighted_ensemble = 3.0920377, prediction/mean/adanet/subnetwork = 3.1531146, prediction/mean/adanet/uniform_average_ensemble = 3.1435103
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 80000: /tmp/tmpU33rCk/model.ckpt-80000
INFO:tensorflow:Loss for final step: 0.0128020905.
INFO:tensorflow:Finished training Adanet iteration 3
INFO:tensorflow:Beginning bookkeeping phase for iteration 3
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-2.txt: ['0:linear', '1:1_layer_dnn', '2:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building iteration 3
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Starting ensemble evaluation for iteration 3
INFO:tensorflow:Restoring parameters from /tmp/tmpU33rCk/model.ckpt-80000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Encountered end of input after 14 evaluations
INFO:tensorflow:Computed ensemble metrics: adanet_loss/t2_2_layer_dnn = 0.027457, adanet_loss/t3_2_layer_dnn = 0.025281, adanet_loss/t3_3_layer_dnn = 0.025353
INFO:tensorflow:Finished ensemble evaluation for iteration 3
INFO:tensorflow:'t3_2_layer_dnn' at index 1 is moving onto the next iteration
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-3.txt: ['0:linear', '1:1_layer_dnn', '2:2_layer_dnn', '3:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 3
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Warm-starting from: (u'/tmp/tmpU33rCk/model.ckpt-80000',)
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t2_2_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_linear/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_linear/adanet/iteration_1/candidate_t0_linear/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_2_layer_dnn/weighted_subnetwork_3/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_2/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/weighted_subnetwork_0/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/subnetwork/dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_2/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_2/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_2_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_linear/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_1_layer_dnn/adanet/iteration_2/candidate_t1_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t3_2_layer_dnn/adanet/iteration_3/candidate_t3_2_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: global_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_2/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_1_layer_dnn/adanet/iteration_1/candidate_t1_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t2_2_layer_dnn/adanet/iteration_3/candidate_t2_2_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t2_2_layer_dnn/adanet/iteration_3/candidate_t2_2_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_2/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/weighted_subnetwork_0/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_2_layer_dnn/weighted_subnetwork_3/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_2_layer_dnn/weighted_subnetwork_2/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t1_1_layer_dnn/adanet/iteration_2/candidate_t1_1_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_2_layer_dnn/adanet/iteration_2/candidate_t2_2_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_2_layer_dnn/weighted_subnetwork_3/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_2_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_1_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_2/subnetwork/dense_2/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/ensemble_t2_2_layer_dnn/weighted_subnetwork_2/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_2_layer_dnn/weighted_subnetwork_3/subnetwork/dense_1/kernel; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/ensemble_t0_linear/weighted_subnetwork_0/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/candidate_t2_2_layer_dnn/adanet/iteration_2/candidate_t2_2_layer_dnn/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/ensemble_t1_1_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_2_layer_dnn/weighted_subnetwork_3/subnetwork/dense/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t0_linear/adanet/iteration_1/candidate_t0_linear/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_2_layer_dnn/weighted_subnetwork_3/subnetwork/dense_2/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t3_2_layer_dnn/adanet_loss; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_2/step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_linear/adanet/iteration_0/candidate_t0_linear/adanet_loss/local_step; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_2_layer_dnn/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_2_layer_dnn/weighted_subnetwork_1/logits/mixture_weight; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/candidate_t3_2_layer_dnn/adanet/iteration_3/candidate_t3_2_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/candidate_t0_linear/adanet/iteration_0/candidate_t0_linear/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_1/candidate_t1_1_layer_dnn/adanet/iteration_1/candidate_t1_1_layer_dnn/adanet_loss/biased; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_0/train_op/is_over/is_over_var_fn/is_over_var; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: adanet/iteration_3/ensemble_t3_2_layer_dnn/weighted_subnetwork_3/subnetwork/dense/kernel; prev_var_name: Unchanged
INFO:tensorflow:Building iteration 4
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Overwriting checkpoint with new graph for iteration 4 to /tmp/tmpU33rCk/model.ckpt-80000
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
INFO:tensorflow:Finished bookkeeping phase for iteration 3
INFO:tensorflow:Beginning training AdaNet iteration 4
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-3.txt: ['0:linear', '1:1_layer_dnn', '2:2_layer_dnn', '3:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 3
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building iteration 4
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpU33rCk/increment.ckpt-4
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 80000 into /tmp/tmpU33rCk/model.ckpt.
INFO:tensorflow:loss = 0.014136677, step = 80001
INFO:tensorflow:global_step/sec: 77.9609
INFO:tensorflow:loss = 0.015122209, step = 80101 (1.284 sec)
INFO:tensorflow:global_step/sec: 451.808
INFO:tensorflow:loss = 0.015705626, step = 80201 (0.221 sec)
INFO:tensorflow:global_step/sec: 434.951
INFO:tensorflow:loss = 0.010678144, step = 80301 (0.230 sec)
INFO:tensorflow:global_step/sec: 461.659
INFO:tensorflow:loss = 0.015691724, step = 80401 (0.217 sec)
INFO:tensorflow:global_step/sec: 462.839
INFO:tensorflow:loss = 0.010645296, step = 80501 (0.216 sec)
INFO:tensorflow:global_step/sec: 441.256
INFO:tensorflow:loss = 0.013091616, step = 80601 (0.227 sec)
INFO:tensorflow:global_step/sec: 453.622
INFO:tensorflow:loss = 0.009302431, step = 80701 (0.221 sec)
INFO:tensorflow:global_step/sec: 469.065
INFO:tensorflow:loss = 0.008277564, step = 80801 (0.213 sec)
INFO:tensorflow:global_step/sec: 462.342
INFO:tensorflow:loss = 0.014173703, step = 80901 (0.216 sec)
INFO:tensorflow:global_step/sec: 420.955
INFO:tensorflow:loss = 0.013234565, step = 81001 (0.238 sec)
INFO:tensorflow:global_step/sec: 456.046
INFO:tensorflow:loss = 0.012928063, step = 81101 (0.219 sec)
INFO:tensorflow:global_step/sec: 468.575
INFO:tensorflow:loss = 0.012574772, step = 81201 (0.213 sec)
INFO:tensorflow:global_step/sec: 473.682
INFO:tensorflow:loss = 0.021084582, step = 81301 (0.211 sec)
INFO:tensorflow:global_step/sec: 459.173
INFO:tensorflow:loss = 0.005883986, step = 81401 (0.221 sec)
INFO:tensorflow:global_step/sec: 425.724
INFO:tensorflow:loss = 0.007256987, step = 81501 (0.232 sec)
INFO:tensorflow:global_step/sec: 447.471
INFO:tensorflow:loss = 0.0073771467, step = 81601 (0.224 sec)
INFO:tensorflow:global_step/sec: 457.456
INFO:tensorflow:loss = 0.015889518, step = 81701 (0.218 sec)
INFO:tensorflow:global_step/sec: 462.518
INFO:tensorflow:loss = 0.013416372, step = 81801 (0.216 sec)
INFO:tensorflow:global_step/sec: 449.76
INFO:tensorflow:loss = 0.017629668, step = 81901 (0.223 sec)
INFO:tensorflow:global_step/sec: 481.047
INFO:tensorflow:loss = 0.011713915, step = 82001 (0.208 sec)
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INFO:tensorflow:loss = 0.025065506, step = 82101 (0.228 sec)
INFO:tensorflow:global_step/sec: 440.24
INFO:tensorflow:loss = 0.012257604, step = 82201 (0.227 sec)
INFO:tensorflow:global_step/sec: 441.714
INFO:tensorflow:loss = 0.010574514, step = 82301 (0.226 sec)
INFO:tensorflow:global_step/sec: 423.429
INFO:tensorflow:loss = 0.0075078337, step = 82401 (0.236 sec)
INFO:tensorflow:global_step/sec: 455.264
INFO:tensorflow:loss = 0.017724512, step = 82501 (0.220 sec)
INFO:tensorflow:global_step/sec: 450.382
INFO:tensorflow:loss = 0.010300313, step = 82601 (0.223 sec)
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INFO:tensorflow:loss = 0.017516607, step = 83301 (0.227 sec)
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INFO:tensorflow:loss = 0.011353311, step = 83401 (0.233 sec)
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INFO:tensorflow:loss = 0.022538329, step = 83501 (0.232 sec)
INFO:tensorflow:global_step/sec: 420.897
INFO:tensorflow:loss = 0.01015048, step = 83601 (0.239 sec)
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INFO:tensorflow:loss = 0.011027567, step = 83701 (0.238 sec)
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INFO:tensorflow:loss = 0.014897767, step = 84001 (0.233 sec)
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INFO:tensorflow:loss = 0.008032159, step = 84201 (0.225 sec)
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INFO:tensorflow:loss = 0.010676222, step = 84601 (0.229 sec)
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INFO:tensorflow:loss = 0.006030474, step = 85001 (0.231 sec)
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INFO:tensorflow:loss = 0.008406863, step = 85901 (0.221 sec)
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INFO:tensorflow:loss = 0.0119697945, step = 86001 (0.223 sec)
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INFO:tensorflow:loss = 0.013377575, step = 95901 (0.216 sec)
INFO:tensorflow:global_step/sec: 444.692
INFO:tensorflow:loss = 0.014647892, step = 96001 (0.225 sec)
INFO:tensorflow:global_step/sec: 432.998
INFO:tensorflow:loss = 0.011901893, step = 96101 (0.231 sec)
INFO:tensorflow:global_step/sec: 436.192
INFO:tensorflow:loss = 0.0070857587, step = 96201 (0.229 sec)
INFO:tensorflow:global_step/sec: 454.613
INFO:tensorflow:loss = 0.0070481785, step = 96301 (0.220 sec)
INFO:tensorflow:global_step/sec: 462.447
INFO:tensorflow:loss = 0.014068865, step = 96401 (0.216 sec)
INFO:tensorflow:global_step/sec: 463.401
INFO:tensorflow:loss = 0.009489993, step = 96501 (0.216 sec)
INFO:tensorflow:global_step/sec: 442.954
INFO:tensorflow:loss = 0.009569755, step = 96601 (0.226 sec)
INFO:tensorflow:global_step/sec: 460.514
INFO:tensorflow:loss = 0.011614005, step = 96701 (0.217 sec)
INFO:tensorflow:global_step/sec: 451.355
INFO:tensorflow:loss = 0.013578262, step = 96801 (0.222 sec)
INFO:tensorflow:global_step/sec: 455.554
INFO:tensorflow:loss = 0.0053700837, step = 96901 (0.220 sec)
INFO:tensorflow:global_step/sec: 441.355
INFO:tensorflow:loss = 0.01334461, step = 97001 (0.226 sec)
INFO:tensorflow:global_step/sec: 452.378
INFO:tensorflow:loss = 0.0177409, step = 97101 (0.221 sec)
INFO:tensorflow:global_step/sec: 447.299
INFO:tensorflow:loss = 0.006775462, step = 97201 (0.224 sec)
INFO:tensorflow:global_step/sec: 459.251
INFO:tensorflow:loss = 0.013195847, step = 97301 (0.218 sec)
INFO:tensorflow:global_step/sec: 457.995
INFO:tensorflow:loss = 0.009728897, step = 97401 (0.218 sec)
INFO:tensorflow:global_step/sec: 455.546
INFO:tensorflow:loss = 0.014908279, step = 97501 (0.220 sec)
INFO:tensorflow:global_step/sec: 455.737
INFO:tensorflow:loss = 0.01381776, step = 97601 (0.219 sec)
INFO:tensorflow:global_step/sec: 450.554
INFO:tensorflow:loss = 0.009535696, step = 97701 (0.222 sec)
INFO:tensorflow:global_step/sec: 458.461
INFO:tensorflow:loss = 0.015514374, step = 97801 (0.218 sec)
INFO:tensorflow:global_step/sec: 423.27
INFO:tensorflow:loss = 0.011712936, step = 97901 (0.236 sec)
INFO:tensorflow:global_step/sec: 420.42
INFO:tensorflow:loss = 0.013672416, step = 98001 (0.238 sec)
INFO:tensorflow:global_step/sec: 435.023
INFO:tensorflow:loss = 0.012360635, step = 98101 (0.230 sec)
INFO:tensorflow:global_step/sec: 441.184
INFO:tensorflow:loss = 0.012664949, step = 98201 (0.227 sec)
INFO:tensorflow:global_step/sec: 441.965
INFO:tensorflow:loss = 0.015917204, step = 98301 (0.226 sec)
INFO:tensorflow:global_step/sec: 436.022
INFO:tensorflow:loss = 0.020906836, step = 98401 (0.234 sec)
INFO:tensorflow:global_step/sec: 413.842
INFO:tensorflow:loss = 0.010173284, step = 98501 (0.237 sec)
INFO:tensorflow:global_step/sec: 452.2
INFO:tensorflow:loss = 0.012074901, step = 98601 (0.221 sec)
INFO:tensorflow:global_step/sec: 454.932
INFO:tensorflow:loss = 0.011731269, step = 98701 (0.220 sec)
INFO:tensorflow:global_step/sec: 446.592
INFO:tensorflow:loss = 0.013173582, step = 98801 (0.224 sec)
INFO:tensorflow:global_step/sec: 447.219
INFO:tensorflow:loss = 0.020451186, step = 98901 (0.223 sec)
INFO:tensorflow:global_step/sec: 457.725
INFO:tensorflow:loss = 0.009836784, step = 99001 (0.219 sec)
INFO:tensorflow:global_step/sec: 447.492
INFO:tensorflow:loss = 0.018442167, step = 99101 (0.224 sec)
INFO:tensorflow:global_step/sec: 456.811
INFO:tensorflow:loss = 0.014100221, step = 99201 (0.219 sec)
INFO:tensorflow:global_step/sec: 440.711
INFO:tensorflow:loss = 0.0076775113, step = 99301 (0.227 sec)
INFO:tensorflow:global_step/sec: 462.599
INFO:tensorflow:loss = 0.011209414, step = 99401 (0.217 sec)
INFO:tensorflow:global_step/sec: 438.489
INFO:tensorflow:loss = 0.008704329, step = 99501 (0.228 sec)
INFO:tensorflow:global_step/sec: 436.411
INFO:tensorflow:loss = 0.0077988785, step = 99601 (0.229 sec)
INFO:tensorflow:global_step/sec: 411.029
INFO:tensorflow:loss = 0.0077135414, step = 99701 (0.243 sec)
INFO:tensorflow:global_step/sec: 427.106
INFO:tensorflow:loss = 0.011483322, step = 99801 (0.234 sec)
INFO:tensorflow:global_step/sec: 439.47
INFO:tensorflow:loss = 0.016574938, step = 99901 (0.228 sec)
INFO:tensorflow:Saving checkpoints for 100000 into /tmp/tmpU33rCk/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Importing architecture from /tmp/tmpU33rCk/architecture-3.txt: ['0:linear', '1:1_layer_dnn', '2:2_layer_dnn', '3:2_layer_dnn'].
INFO:tensorflow:Rebuilding iteration 0
INFO:tensorflow:Building subnetwork 'linear'
INFO:tensorflow:Rebuilding iteration 1
INFO:tensorflow:Building subnetwork '1_layer_dnn'
INFO:tensorflow:Rebuilding iteration 2
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Rebuilding iteration 3
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building iteration 4
INFO:tensorflow:Building subnetwork '2_layer_dnn'
INFO:tensorflow:Building subnetwork '3_layer_dnn'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-12-13-19:39:03
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpU33rCk/model.ckpt-100000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving candidate 't3_2_layer_dnn' dict for global step 100000: architecture/adanet/ensembles =
�
>adanet/iteration_3/ensemble_t3_2_layer_dnn/architecture/adanetB: B4| linear | 1_layer_dnn | 2_layer_dnn | 2_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.032998268, average_loss/adanet/subnetwork = 0.04255607, average_loss/adanet/uniform_average_ensemble = 0.036970153, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.042651616, loss/adanet/subnetwork = 0.059904583, loss/adanet/uniform_average_ensemble = 0.05318597, prediction/mean/adanet/adanet_weighted_ensemble = 3.0920377, prediction/mean/adanet/subnetwork = 3.1531146, prediction/mean/adanet/uniform_average_ensemble = 3.1435103
INFO:tensorflow:Saving candidate 't4_2_layer_dnn' dict for global step 100000: architecture/adanet/ensembles =
�
>adanet/iteration_4/ensemble_t4_2_layer_dnn/architecture/adanetBH BB| linear | 1_layer_dnn | 2_layer_dnn | 2_layer_dnn | 2_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.03505087, average_loss/adanet/subnetwork = 0.03415539, average_loss/adanet/uniform_average_ensemble = 0.03567381, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.04599317, loss/adanet/subnetwork = 0.0469665, loss/adanet/uniform_average_ensemble = 0.05124563, prediction/mean/adanet/adanet_weighted_ensemble = 3.093939, prediction/mean/adanet/subnetwork = 3.1460986, prediction/mean/adanet/uniform_average_ensemble = 3.144028
INFO:tensorflow:Saving candidate 't4_3_layer_dnn' dict for global step 100000: architecture/adanet/ensembles =
�
>adanet/iteration_4/ensemble_t4_3_layer_dnn/architecture/adanetBH BB| linear | 1_layer_dnn | 2_layer_dnn | 2_layer_dnn | 3_layer_dnn |J
text, average_loss/adanet/adanet_weighted_ensemble = 0.035222616, average_loss/adanet/subnetwork = 0.038082376, average_loss/adanet/uniform_average_ensemble = 0.036011517, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss/adanet/adanet_weighted_ensemble = 0.04607369, loss/adanet/subnetwork = 0.054222643, loss/adanet/uniform_average_ensemble = 0.051993247, prediction/mean/adanet/adanet_weighted_ensemble = 3.0921233, prediction/mean/adanet/subnetwork = 3.171357, prediction/mean/adanet/uniform_average_ensemble = 3.1490798
INFO:tensorflow:Finished evaluation at 2018-12-13-19:39:09
INFO:tensorflow:Saving dict for global step 100000: average_loss = 0.035222616, average_loss/adanet/adanet_weighted_ensemble = 0.035222616, average_loss/adanet/subnetwork = 0.038082376, average_loss/adanet/uniform_average_ensemble = 0.036011517, global_step = 100000, label/mean = 3.1049454, label/mean/adanet/adanet_weighted_ensemble = 3.1049454, label/mean/adanet/subnetwork = 3.1049454, label/mean/adanet/uniform_average_ensemble = 3.1049454, loss = 0.04607369, loss/adanet/adanet_weighted_ensemble = 0.04607369, loss/adanet/subnetwork = 0.054222643, loss/adanet/uniform_average_ensemble = 0.051993247, prediction/mean = 3.0921233, prediction/mean/adanet/adanet_weighted_ensemble = 3.0921233, prediction/mean/adanet/subnetwork = 3.171357, prediction/mean/adanet/uniform_average_ensemble = 3.1490798
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 100000: /tmp/tmpU33rCk/model.ckpt-100000
INFO:tensorflow:Loss for final step: 0.011420857.
INFO:tensorflow:Finished training Adanet iteration 4
Loss: 0.035222616
Uniform average loss: 0.036011517
Architecture: | linear | 1_layer_dnn | 2_layer_dnn | 2_layer_dnn | 3_layer_dnn |
| Apache-2.0 | adanet/examples/tutorials/adanet_objective.ipynb | sararob/adanet |
Remote control Jetbot using Virtual gamepad====== | # This cell should only run once.
import os
# set the current working directory. This is required by isaac.
os.chdir("../..")
os.getcwd()
simulation =True
from packages.pyalice import Application, Message, Codelet
# Creates an empty Isaac application
app = Application(name="jetbot_application") | _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
The Robot Engine Bridge enables communication between Omniverse and the Isaac SDK. When a REB application is created, a simulation-side Isaac SDK application is started, allowing messages to be sent to, or published from, the REB Components. The simulation Subgraph is loaded into our Isaac application, allowing the exchange of messages between our application and the REB Components present in the Omniverse model over TCP. Thus, by loading in the simulation subgraph and using the Camera and Differential Base components, our Isaac application can receive the image stream from Omniverse’s Viewport and can transmit commands to be effectuated in simulation. | if simulation:
# Loads the simulation_tcp subgraph into the Isaac application, adding all nodes, components,
# edges, and configurations
app.load(filename="apps/jetbot/simulation_tcp.subgraph.json", prefix="simulation")
# Gets a reference to the interface node of the subgraph having a prefix of "simulation"
simulation_node = app.nodes["simulation.interface"]
| _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
The Robot Remote Control component can send commands to the Differential Base to control the Jetbot model. Therefore, we add a node to which a component of type RobotRemoteControl is added. Nodes can be thought of as a container to group related components in an Isaac application. As the RobotRemoteControl component will be used to generate commands in the form of the desired state of a Segway, an edge is added between the “Segway_cmd” channel of the RobotRemoteControl component and the “base_command” channel of the simulation subgraph. This allows the REB Differential Base in Omniverse to receive the desired Segway states and move the Jetbot model in accordance with the received command. | if simulation:
# Creating a new node in the Isaac application named "robot_remote"
robot_remote_node = app.add("robot_remote")
# Loads the navigation module, allowing components requiring this module to be added to the application
app.load_module("navigation")
# Add the RobotRemoteControl and FailsafeHeartbeat components to the robot_remote node
robot_remote_control_component = robot_remote_node.add(name="RobotRemoteControl",
ctype=app.registry.isaac.navigation.RobotRemoteControl)
failsafe_component = robot_remote_node.add(name="FailsafeHeartbeat",
ctype=app.registry.isaac.alice.FailsafeHeartbeat)
# Set component configuration parameters
robot_remote_control_component.config["tick_period"] = "10ms"
failsafe_component.config["heartbeat_name"] = "deadman_switch"
failsafe_component.config["failsafe_name"] = "robot_failsafe"
failsafe_component.config["interval"] = 0.25
# Makes dataflow connection between "segway_cmd" channel of RobotRemoteControl component, and the
# "base_command" channel of the REB Differental Base in simulation
app.connect(robot_remote_control_component, "segway_cmd", simulation_node["input"], "base_command")
| _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
To generate a corresponding command, the Robot Remote Control component must receive either JoystickStateProto messages from its "js_state" channel, or messages consisting of a linear and angular velocity over its "ctrl" channel. Here, we add the virtual gamepad subgraph which can be used to generate "JoystickStateProto" messages required by the Robot Remote Control component. Therefore, we establish the necessary connection. | if simulation:
# Loads the virtual_gamepad subgraph into the application
app.load(filename="apps/jetbot/virtual_gamepad.subgraph.json", prefix="virtual_gamepad")
# Finds a reference to the component named "interface", located in the subgraph node of the virtual gamepad
# subgraph. The component named "interface" is of type Subgraph, meaning all messages coming to or from the
# virtual gamepad subgraph will pass through the channels of the subgraph component.
virtual_gamepad_interface = app.nodes["virtual_gamepad.subgraph"]["interface"]
# Pass messages generated by virtual gamepad to RobotRemoteControl component.
app.connect(virtual_gamepad_interface, "joystick", robot_remote_control_component, "js_state")
| _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
The virtual gamepad widget in Sight allows us to use the WASD keys of the keyboard to steer the Jetbot in simulation, thus making the connection between Isaac and Omniverse more concrete. Prior to starting the Isaac application, the REB application is created by opening the jetbot.usd file in Omniverse, navigating to the Robot Engine Bridge extension and clicking "create application", followed by pressing the "Play" button. The Isaac application can now be started by executing the following piece of code. | app.start() | _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
Open Sight by going to (Your-IP-Address):3000 in your browser (or localhost:3000 if the Isaac SDK is running on your local machine), and control the Jetbot in simulation with the Virtual Gamepad as shown below. | app.stop() | _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
Running Inference in Simulation ======Please note the following section requires a training environment built in simulation and a model trained.With the simulation environment and a trained model (the .etlt file generated by following the Object Detection with DetectNetv2 pipeline), we can run inference using data streamed from simulation using the detectnet subgraph. The subgraph receives ImageViewer proto messages from its "image" channel, performs inference on the received images, and transmits a Detections2Proto message containing the bounding box position, label, and confidence for each of the detections. Upon loading the subgraph, configuration parameters are adjusted according to how training was conducted using the object detection pipeline. | app.load(filename="packages/detect_net/apps/detect_net_inference.subgraph.json", prefix="detect_net")
# Setting configuration parameters of components used in the detect-net subgraph to allow the trained
# model to be used, and training parameters specified.
inference_component = app.nodes["detect_net.tensor_r_t_inference"]["isaac.ml.TensorRTInference"]
inference_component.config.model_file_path = "external/jetbot_ball_detection_resnet_model/jetbot_ball_detection_resnet18.etlt"
inference_component.config.etlt_password = "nvidia"
decoder_component = app.nodes["detect_net.detection_decoder"]["isaac.detect_net.DetectNetDecoder"]
decoder_component.config.labels = ["sphere"]
# Changing Detectnet Subgraph to accommodate Omniverse viewport (720 x 1280) and
# dimensions used to train model
if simulation:
inference_component.config["input_tensor_info"] = [
{
"operation_name": "input_1",
"channel": "image",
"dims": [3, 368, 640],
"uff_input_order": "channels_last"
}
]
decoder_component.config["output_scale"] = [720, 1280]
encoder_component = app.nodes["detect_net.tensor_encoder"]["isaac.ml.ColorCameraEncoderCuda"]
encoder_component.config["rows"] = 368
| _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
With the subgraph loaded and configuration parameters set, we can relay Omniverse's viewport stream, captured by the REB Camera, to the detectnet subgraph, allowing inference to be performed on simulation data. | detect_net_interface = app.nodes["detect_net.subgraph"]["interface"]
if simulation:
# Allows image stream from Omniverse to flow to detect-net
app.connect(simulation_node["output"], "color", detect_net_interface, "image")
app.start() | _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
Upon opening the jetbot_inference.usd file in Omniverse, creating the Robot Engine Bridge application, and starting both the simulation and he Isaac application, the performance of the detection model can be verified. | app.stop() | _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
Jetbot Autonomously Following Objects in Simulation======Now that objects are being correctly detected in simulation, we need to implement the control logic to move the Jetbot model such that it keeps the desired object both just in front of it and horizontally centered. To accomplish this, we first define a couple helper functions to parse detections2proto messages, determine the pixel coordinates of the center of a bounding box, determine the area (in pixels) of a bounding box, and find the detection of a specified label whose bounding box center is closest to the target location. Bounding box area will later be used to estimate how close or far a detected object is from the Jetbot. | import numpy as np
import json
def get_parsed_detections(detections_msg):
"""Parses and reformats Detections2Proto messages"""
detections_msg_json = detections_msg.json
zipped = zip(detections_msg_json['predictions'], detections_msg_json['boundingBoxes'])
zipped_list = list(zipped)
detections = {}
for k, sublist in enumerate(zipped_list):
for sub_sublist in sublist:
# If key already present; append to the existing dict.
if (k in detections):
detections[k] = {**detections[k],**sub_sublist}
# If key not present insert the first attribute of the detected object.
else:
detections[k] = {**sub_sublist}
return detections
def norm(vec, target):
"""Computes the length of the 2D vector"""
return np.sqrt((vec[0]-target[0])**2 + (vec[1]-target[1])**2)
def get_detection_center(detection):
"""Computes the center x, y coordinates of the object x = rows of image; y = cols of image """
center_x = (detection['min']['x'] + detection['max']['x']) / 2.0 - 0.5
center_y = (detection['min']['y'] + detection['max']['y']) / 2.0 - 0.5
return (center_x, center_y)
def get_detection_area(detection):
"""Computes the area (in pixels) of a detection"""
detection_width = detection['max']['x'] - detection['min']['x']
detection_height = detection['max']['y'] - detection['min']['y']
detection_area = detection_width * detection_height
return detection_area
def find_closest_matching_detection(detections_dict, target, label):
"""Finds the closest detection to target pixel location in detections_dict, having the specified label"""
closest_matching_detection = None
closest_matching_detection_dist = np.inf
for detection in detections_dict.values():
if detection["label"] == label:
detection_center = get_detection_center(detection)
detection_dist = norm(detection_center, target)
if detection_dist < closest_matching_detection_dist:
closest_matching_detection = detection
closest_matching_detection_dist = detection_dist
return closest_matching_detection
| _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
With the helper functions in place and detections being made in simulation, we can develop a Python Codelet to control the Jetbot, which will use detections messages to compute the desired motor commands of the Jetbot, and then publish messages containing these commands. Codelets are functionally equivalent to the built-in COMPONENTS provided by the Isaac SDK in the sense that they both send and receive messages, however, Codelets provide a way for us to create a custom component so we can execute user-defined code within our Isaac application. You can learn more about creating Codelets HERE. As the real Jetbot’s motors are controlled with Pulse Width Modulator (PWM) duty cycle commands, the created Codelet will generate messages containing commands of this type. Linear speed of the Jetbot is determined based on the area of the bounding box of a detected object; If the area of the detected object is too small, the Jetbot will move closer to the object, whereas if the area is too large, the Jetbot will back away. Similarly, angular speed is set based on the horizontal offset of the detection from the center of the Jetbot’s view. Finally, motor commands are calculated based on the linear and angular speed, and messages containing the commands are published. | # Generates PWM commands to follow desired object
class JetbotControl(Codelet):
def start(self):
self.rx = self.isaac_proto_rx("Detections2Proto", "detections")
self.tx = self.isaac_proto_tx("StateProto", "motor_command")
# Ticks when new detections message is received
self.tick_on_message(self.rx)
def tick(self):
# Receives a Detections2Proto message
rx_message = self.rx.message
# Reads configuration parameters set outside of Codelet
label = self.config.label
image_width = self.config.image_width
image_height = self.config.image_height
min_pwm = self.config.min_pwm # Smallest motor command required to move real Jetbot
angular_gain = self.config.angular_gain
target_coverage = self.config.target_coverage
parsed_detections = get_parsed_detections(rx_message)
image_horizontal_center = image_width / 2.0
image_vertical_center = image_height / 2.0
image_center = [image_vertical_center, image_horizontal_center]
detection = find_closest_matching_detection(parsed_detections, image_center, label)
if detection is None:
# Do not move if there isn't a detection with matching label
left_motor_command = 0.0
right_motor_command = 0.0
else:
# Generate PWM commands to move towards detection by keeping the detection horizontally centered,
# and the fraction of the image the bounding box covers equal to target_coverage
# Compute areas
image_area = image_width * image_height
target_area = target_coverage * image_area
detection_area = get_detection_area(detection)
# Use areas to determine linear speed
# min_pwm is used here to eliminate dead zones
if detection_area < target_area:
linear_speed = min_pwm + (1 - min_pwm)*(target_area - detection_area) / target_area
else:
linear_speed = -min_pwm + (1 - min_pwm)*(target_area - detection_area) / (image_area - target_area)
# Use horizontal offset of detection from image center to determine angular speed
detection_center = get_detection_center(detection)
angular_speed = (image_horizontal_center - detection_center[1]) / image_horizontal_center
# Computes motor commands based on desired linear and angular speeds, ensuring PWM commands are in Jetbot's
# acceptable range of [-1, 1]
min_motor_command = -1
max_motor_command = 1
left_motor_command = float(np.clip(linear_speed - angular_gain * angular_speed, min_motor_command, max_motor_command))
right_motor_command = float(np.clip(linear_speed + angular_gain * angular_speed, min_motor_command, max_motor_command))
# Initializes, populates, and transmits a StateProto message containing motor commands
tx_message = self.tx.init()
data = tx_message.proto.init('data', 2)
data[0] = left_motor_command
data[1] = right_motor_command
self.tx.publish()
| _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
The created Codelet must be added to the Isaac application, just like a normal component. The configuration parameters are then set and an edge added between the Codelet and the detect-net subgraph so that the detections messages can be used to generate control commands. | # Create a new node, and add the JetbotControl Codelet to the node.
controller_node = app.add("controller")
jetbot_control_component = controller_node.add(JetbotControl)
# Set the configuration parameters of the JetbotControl Codelet
if simulation:
jetbot_control_component.config.image_width = 1280
jetbot_control_component.config.image_height = 720
else:
jetbot_control_component.config.image_width = 640
jetbot_control_component.config.image_height = 360
jetbot_control_component.config.label = "sphere"
jetbot_control_component.config.target_coverage = 0.05
jetbot_control_component.config.angular_gain = 0.057
jetbot_control_component.config.min_pwm = 0.25
# Pass detections to JetbotControl Codelet
app.connect(detect_net_interface, "detections", jetbot_control_component, "detections")
| _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
While the added Codelet can generate motor commands compatible with the real Jetbot, in simulation the Jetbot is controlled by sending Segway commands to the REB Differential Base. Segway commands can be generated by providing linear and angular velocities to the “ctrl” channel of the previously created RobotRemoteControl component. To convert PWM commands generated by our controller into the linear and angular commands needed, a relationship between motor commands sent to the Jetbot and the speed at which the real Jetbot travels must be established. The mapping between motor command and velocity was found by experimentally measuring the time taken for the real Jetbot to travel 3 meters. | def pwm_to_velocity(pwm_command, min_pwm_command):
"""Computes velocity (in [m/s]) of real Jetbot when both motors are set to "pwm_command" based on experimental data"""
command_abs = np.abs(pwm_command)
# min_pwm_command represents the interval of commands sent to the jetbot which do not cause movement:
# [-min_pwm_command, min_pwm_command]
if command_abs < min_pwm_command:
velocity = 0
else:
velocity = float(np.sign(pwm_command) * (2.0328 * command_abs - 0.0948))
return velocity
| _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
A second Codelet can now be created to adapt the PWM commands so that they are able to be used in simulation. With the help of our recently defined “pwm_to_velocity” function, the velocity of each wheel can be calculated. Then, using the dynamics equations of a differential base, linear and angular velocity can be calculated from the wheel velocities. | # Converts PWM commands into linear and angular velocities
class SimulationAdapter(Codelet):
def start(self):
self.rx = self.isaac_proto_rx("StateProto", "motor_command")
self.tx = self.isaac_proto_tx("StateProto", "velocity_command")
self.tick_on_message(self.rx)
def tick(self):
rx_message = self.rx.message
min_pwm_command = self.config.min_pwm_command
simulation_linear_gain = self.config.simulation_linear_gain
simulation_angular_gain = self.config.simulation_angular_gain
data = rx_message.json['data']
left_motor_command = data[0]
right_motor_command = data[1]
left_wheel_velocity = pwm_to_velocity(left_motor_command, min_pwm_command)
right_wheel_velocity = pwm_to_velocity(right_motor_command, min_pwm_command)
# Distance between wheels of Jetbot [m]
length = 0.1143
# Linear and angular velocity resulting from PWM command
linear_velocity = (left_wheel_velocity + right_wheel_velocity) / 2.0
angular_velocity = (left_wheel_velocity - right_wheel_velocity) / length
# Gains were found using a REB RigidBodySink and measuring velocity traveled in simulation,
# versus linear and angular command sent to simulation.
simulation_linear_command = simulation_linear_gain * linear_velocity
simulation_angular_command = simulation_angular_gain * angular_velocity
# Initializes, populates, and publishes commands containing linear and angular velocities
tx_message = self.tx.init()
data = tx_message.proto.init('data', 2)
data[0] = simulation_linear_command
data[1] = simulation_angular_command
self.tx.publish()
| _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
With the Simulation Adapter Codelet defined, we may now add it to our Isaac application. An edge is added from the Jetbot Control Codelet to the Simulation Adapter, allowing the Adapter to receive PWM commands from the controller. Once the commands are converted into linear and angular velocities, they must be sent to the RobotRemoteControl component as previously discussed, so we need to add the corresponding edge. | if simulation:
# Create a new node, and add the SimulationAdapter Codelet to the node.
adapter_node = app.add("adapter")
simulation_adapter_component = adapter_node.add(SimulationAdapter)
# Set the configuration parameters of the SimulationAdapter Codelet
simulation_adapter_component.config.min_pwm_command = 0.2
simulation_adapter_component.config.simulation_linear_gain = 0.27
simulation_adapter_component.config.simulation_angular_gain = -0.16
# Pass motor commands calculated by the JetbotControl Codelet to the SimulationAdapter Codelet
app.connect(jetbot_control_component, "motor_command", simulation_adapter_component, "motor_command")
# Pass linear and angular velocity commands from the SimulationAdapter Codelet to the RobotRemoteControl component.
app.connect(simulation_adapter_component, "velocity_command", robot_remote_control_component, "ctrl")
| _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
Now we’re ready to autonomously follow a ball in simulation. Upon opening the jetbot_follow.usd file in Omniverse, create the Robot Engine Bridge application, and start the Isaac application by running the next cell. | app.start() | _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
You'll notice that despite the Jetbot detecting objects, it isn't moving. The reason is that the Robot Remote Control component will only send commands while the deadman switch is pressed for safety reasons. But there aren't any safety concerns in simulation! Lets go ahead and disable that. | if simulation:
robot_remote_control_component.config["disable_deadman_switch"] = True | _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
You should now see your Jetbot following balls as they appear before it in simulation. Cool! Tweak the config parameters of the Wheel Velocity Control Generator Codelet to your likening, and let's finish bridging the gap between simulation and reality! | app.stop() | _____no_output_____ | FSFAP | sdk/apps/jetbot/jetbot_notebook.ipynb | antonspivak/isaac_gps |
PyTorch: 사용자 정의 nn Module-------------------------------하나의 은닉층(hidden layer)과 편향(bias)이 없는 완전히 연결된 ReLU 신경망을,유클리드 거리(Euclidean distance) 제곱을 최소화하는 식으로 x로부터 y를 예측하도록학습하겠습니다.이번에는 사용자 정의 Module의 서브클래스로 모델을 정의합니다. 기존 Module의 간단한구성보다 더 복잡한 모델을 원한다면, 이 방법으로 모델을 정의하면 됩니다. | import torch
class TwoLayerNet(torch.nn.Module):
def __init__(self, D_in, H, D_out):
"""
생성자에서 2개의 nn.Linear 모듈을 생성하고, 멤버 변수로 지정합니다.
"""
super(TwoLayerNet, self).__init__()
self.linear1 = torch.nn.Linear(D_in, H)
self.linear2 = torch.nn.Linear(H, D_out)
def forward(self, x):
"""
순전파 함수에서는 입력 데이터의 Tensor를 받고 출력 데이터의 Tensor를
반환해야 합니다. Tensor 상의 임의의 연산자뿐만 아니라 생성자에서 정의한
Module도 사용할 수 있습니다.
"""
h_relu = self.linear1(x).clamp(min=0)
y_pred = self.linear2(h_relu)
return y_pred
# N은 배치 크기이며, D_in은 입력의 차원입니다;
# H는 은닉층의 차원이며, D_out은 출력 차원입니다.
N, D_in, H, D_out = 64, 1000, 100, 10
# 입력과 출력을 저장하기 위해 무작위 값을 갖는 Tensor를 생성합니다.
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)
# 앞에서 정의한 클래스를 생성하여 모델을 구성합니다.
model = TwoLayerNet(D_in, H, D_out)
# 손실 함수와 Optimizer를 만듭니다. SGD 생성자에 model.parameters()를 호출하면
# 모델의 멤버인 2개의 nn.Linear 모듈의 학습 가능한 매개변수들이 포함됩니다.
criterion = torch.nn.MSELoss(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)
for t in range(500):
# 순전파 단계: 모델에 x를 전달하여 예상되는 y 값을 계산합니다.
y_pred = model(x)
# 손실을 계산하고 출력합니다.
loss = criterion(y_pred, y)
print(t, loss.item())
# 변화도를 0으로 만들고, 역전파 단계를 수행하고, 가중치를 갱신합니다.
optimizer.zero_grad()
loss.backward()
optimizer.step() | _____no_output_____ | BSD-3-Clause | docs/_downloads/ee69127c1eacbde4ff2e2aca2e46e8f0/two_layer_net_module.ipynb | jessemin/PyTorch-tutorials-kr |
Read sample data | x_train = pd.read_csv('sample_training_data.csv')
ft_predict = 'cur_pH' # specify which column to predict
all_fts = x_train.columns
model_fts = all_fts
model_fts.drop(ft_predict) | _____no_output_____ | MIT | example/runPairwiseRegression_example.ipynb | philips-labs/pairwise-regression |
Run pairwise regression model | # train model
z_dynamic_range = np.linspace(6.8,7.5,15) # specify the range and binning of key-covariate
model = KeyCovariatePairwiseLR(alpha_blend=20, cov_steps=20, coeff_smooth_z=6, func_smooth_z='sigmoid')
print(ft_predict)
model.fit(x_train[all_fts], 'prev_pH', ft_predict, cov_range_z=z_dynamic_range, include_z_in_x=True)
ypred_train = model.predict(x_train[all_fts])
model.plot_pairwise_interactions(n_plot_cols=3, scaleup=1.6)
plt.tight_layout() | cur_pH
prev_pH
| MIT | example/runPairwiseRegression_example.ipynb | philips-labs/pairwise-regression |
d-sandbox Connecting to JDBCApache Spark™ and Databricks® allow you to connect to a number of data stores using JDBC. In this lesson you:* Read data from a JDBC connection * Parallelize your read operation to leverage distributed computation Audience* Primary Audience: Data Engineers* Additional Audiences: Data Scientists and Data Pipeline Engineers Prerequisites* Web browser: Please use a supported browser.* Concept (optional): DataFrames course from Databricks Academy <iframe src="//fast.wistia.net/embed/iframe/i07uvaoqgh?videoFoam=true"style="border:1px solid 1cb1c2;"allowtransparency="true" scrolling="no" class="wistia_embed"name="wistia_embed" allowfullscreen mozallowfullscreen webkitallowfullscreenoallowfullscreen msallowfullscreen width="640" height="360" > Watch full-screen. -sandbox Java Database ConnectivityJava Database Connectivity (JDBC) is an application programming interface (API) that defines database connections in Java environments. Spark is written in Scala, which runs on the Java Virtual Machine (JVM). This makes JDBC the preferred method for connecting to data whenever possible. Hadoop, Hive, and MySQL all run on Java and easily interface with Spark clusters.Databases are advanced technologies that benefit from decades of research and development. To leverage the inherent efficiencies of database engines, Spark uses an optimization called predicate pushdown. **Predicate pushdown uses the database itself to handle certain parts of a query (the predicates).** In mathematics and functional programming, a predicate is anything that returns a Boolean. In SQL terms, this often refers to the `WHERE` clause. Since the database is filtering data before it arrives on the Spark cluster, there's less data transfer across the network and fewer records for Spark to process. Spark's Catalyst Optimizer includes predicate pushdown communicated through the JDBC API, making JDBC an ideal data source for Spark workloads.In the road map for ETL, this is the **Extract and Validate** step: Recalling the Design PatternRecall the design pattern for connecting to data from the previous lesson: 1. Define the connection point.2. Define connection parameters such as access credentials.3. Add necessary options. After adhering to this, read data using `spark.read.options(, ).()`. The JDBC connection uses this same formula with added complexity over what was covered in the lesson. <iframe src="//fast.wistia.net/embed/iframe/2clbjyxese?videoFoam=true"style="border:1px solid 1cb1c2;"allowtransparency="true" scrolling="no" class="wistia_embed"name="wistia_embed" allowfullscreen mozallowfullscreen webkitallowfullscreenoallowfullscreen msallowfullscreen width="640" height="360" > Watch full-screen.  Classroom-Setup & Classroom-CleanupFor each lesson to execute correctly, please make sure to run the **`Classroom-Setup`** cell at the start of each lesson (see the next cell) and the **`Classroom-Cleanup`** cell at the end of each lesson. | %run "./Includes/Classroom-Setup" | _____no_output_____ | MIT | ETL-I/ETL1 04 - Connecting to JDBC.ipynb | vshiv667/Data-Engineering |
-sandboxRun the cell below to confirm you are using the right driver. Each notebook has a default language that appears in upper corner of the screen next to the notebook name, and you can easily switch between languages in a notebook. To change languages, start your cell with `%python`, `%scala`, `%sql`, or `%r`. | %scala
// run this regardless of language type
Class.forName("org.postgresql.Driver") | _____no_output_____ | MIT | ETL-I/ETL1 04 - Connecting to JDBC.ipynb | vshiv667/Data-Engineering |
Define your database connection criteria. In this case, you need the hostname, port, and database name. Access the database `training` via port `5432` of a Postgres server sitting at the endpoint `server1.databricks.training`.Combine the connection criteria into a URL. | jdbcHostname = "server1.databricks.training"
jdbcPort = 5432
jdbcDatabase = "training"
jdbcUrl = f"jdbc:postgresql://{jdbcHostname}:{jdbcPort}/{jdbcDatabase}" | _____no_output_____ | MIT | ETL-I/ETL1 04 - Connecting to JDBC.ipynb | vshiv667/Data-Engineering |
Create a connection properties object with the username and password for the database. | connectionProps = {
"user": "readonly",
"password": "readonly"
} | _____no_output_____ | MIT | ETL-I/ETL1 04 - Connecting to JDBC.ipynb | vshiv667/Data-Engineering |
Read from the database by passing the URL, table name, and connection properties into `spark.read.jdbc()`. | tableName = "training.people_1m"
peopleDF = spark.read.jdbc(url=jdbcUrl, table=tableName, properties=connectionProps)
display(peopleDF) | _____no_output_____ | MIT | ETL-I/ETL1 04 - Connecting to JDBC.ipynb | vshiv667/Data-Engineering |
Exercise 1: Parallelizing JDBC ConnectionsThe command above was executed as a serial read through a single connection to the database. This works well for small data sets; at scale, parallel reads are necessary for optimal performance.See the [Managing Parallelism](https://docs.databricks.com/spark/latest/data-sources/sql-databases.htmlmanaging-parallelism) section of the Databricks documentation. -sandbox Step 1: Find the Range of Values in the DataParallel JDBC reads entail assigning a range of values for a given partition to read from. The first step of this divide-and-conquer approach is to find bounds of the data.Calculate the range of values in the `id` column of `peopleDF`. Save the minimum to `dfMin` and the maximum to `dfMax`. **This should be the number itself rather than a DataFrame that contains the number.** Use `.first()` to get a Scala or Python object. **Hint:** See the `min()` and `max()` functions in Python `pyspark.sql.functions` or Scala `org.apache.spark.sql.functions`. | dfMin=peopleDF.select("id").rdd.min()[0]
dfMax=peopleDF.select("id").rdd.max()[0]
# TEST - Run this cell to test your solution
dbTest("ET1-P-04-01-01", 1, dfMin)
dbTest("ET1-P-04-01-02", 1000000, dfMax)
print("Tests passed!") | _____no_output_____ | MIT | ETL-I/ETL1 04 - Connecting to JDBC.ipynb | vshiv667/Data-Engineering |
-sandbox Step 2: Define the Connection Parameters.Referencing the documentation, define the connection parameters for this read.Use 8 partitions.Assign the results to `peopleDFParallel`. Setting the column for your parallel read introduces unexpected behavior due to a bug in Spark. To make sure Spark uses the capitalization of your column, use `'"id"'` for your column. Monitor the issue here. | peopleDFParallel = spark.read.jdbc(url=jdbcUrl, table="training.people_1m", column='"id"', lowerBound=1, upperBound=100000, numPartitions=8,properties=connectionProps)
display(peopleDFParallel)
# TEST - Run this cell to test your solution
dbTest("ET1-P-04-02-01", 8, peopleDFParallel.rdd.getNumPartitions())
print("Tests passed!") | _____no_output_____ | MIT | ETL-I/ETL1 04 - Connecting to JDBC.ipynb | vshiv667/Data-Engineering |
Step 3: Compare the Serial and Parallel ReadsCompare the two reads with the `%timeit` function. Display the number of partitions in each DataFrame by running the following: | print("Partitions:", peopleDF.rdd.getNumPartitions())
print("Partitions:", peopleDFParallel.rdd.getNumPartitions()) | _____no_output_____ | MIT | ETL-I/ETL1 04 - Connecting to JDBC.ipynb | vshiv667/Data-Engineering |
Invoke `%timeit` followed by calling a `.describe()`, which computes summary statistics, on both `peopleDF` and `peopleDFParallel`. | %timeit peopleDF.describe()
%timeit peopleDFParallel.describe() | _____no_output_____ | MIT | ETL-I/ETL1 04 - Connecting to JDBC.ipynb | vshiv667/Data-Engineering |
What is the difference between serial and parallel reads? Note that your results vary drastically depending on the cluster and number of partitions you use | #Parallel reads are faster by 3.5 secs on average over 7 runs | _____no_output_____ | MIT | ETL-I/ETL1 04 - Connecting to JDBC.ipynb | vshiv667/Data-Engineering |
Review**Question:** What is JDBC? **Answer:** JDBC stands for Java Database Connectivity, and is a Java API for connecting to databases such as MySQL, Hive, and other data stores.**Question:** How does Spark read from a JDBC connection by default? **Answer:** With a serial read. With additional specifications, Spark conducts a faster, parallel read. Parallel reads take full advantage of Spark's distributed architecture.**Question:** What is the general design pattern for connecting to your data? **Answer:** The general design patter is as follows:0. Define the connection point0. Define connection parameters such as access credentials0. Add necessary options such as for headers or parallelization  Classroom-CleanupRun the **`Classroom-Cleanup`** cell below to remove any artifacts created by this lesson. | %run "./Includes/Classroom-Cleanup" | _____no_output_____ | MIT | ETL-I/ETL1 04 - Connecting to JDBC.ipynb | vshiv667/Data-Engineering |
AI for Earth System Science Hackathon 2020 HOLODEC Machine Learning Challenge ProblemMatt Hayman, Aaron Bansemer, David John Gagne, Gabrielle Gantos, Gunther Wallach, Natasha Flyer IntroductionThe properties of the water and ice particles in clouds are critical to many aspects of weather and climate. The size, shape, and concentration of ice particles control the radiative properties of cirrus clouds. The spatial distribution of water droplets in warm clouds may influence the formation of drizzle and rain. The interactions among droplets, ice particles, and aerosols impact precipitation, lightning, atmospheric chemistry, and more. Measurements of natural cloud particles are often taken aboard research aircraft with instruments mounted on the wings. One of the newer technologies used for these instruments is inline holographic imaging, which has the important advantage of being able to instantaneously record all of the particles inside a small volume of air. Using this technology, the Holographic Detector for Clouds (HOLODEC) has been developed by the university community and NCAR to improve our cloud measurement capabilities.A hologram captures electro-magnatic field amplitude and phase (or wavefront) incident on a detector. In contrast, standard imaging captures only the amplitude of the electric field. Unlike a standard image, holograms can be computationally refocused on any object within the capture volume using standard wave propagation calculations. The figure below shows an example of an inline hologram (large image) with five out of focus particles. The five smaller images show the reconstruction from each particle by computationally propagating the electro-magnetic field back to the depth position of each particle. HOLODEC is an airborne holographic cloud imager capable of capturing particle size distributions in a single shot, so a measured particle size distribution is localized to a specific part of the cloud (not accumulated over a long path length). By capturing a hologram, each particle can be imaged irrespective of its location in the sample volume, and its size and position can be accurately captured.While holographic imaging provides unparalleled information about cloud particles, processing the raw holograms is also computationally expensive. Lacking prior knowledge of the particle position in depth, a typical HOLODEC hologram is reconstructed at 1000 planes (or depths) using standard diffraction calculations. At each plane, a particle’s image sharpness is evaluated and the particle size and position is determined only at a plane where it is in focus. In addition to the computational cost, the processing requires human intervention to recognize when a “particle” is really just artifacts of interfering scattered fields.The objective of this project is to develop a machine learning solution to process HOLODEC data that is more computationally efficient than the first-principles based processor. An important factor in processing hologram data is that the scattered field from a particle spreads out as it propagates. The image below shows the scattered field from a 50 µm particle at distances in increments of 0.1 mm from the particle (0 to 0.7 mm). As the scattered field expands, it’s power is also distributed over a larger area.For simplicity, this project deals with simulated holographic data where particle shapes are limited to spheres. Two datasets are provided. The first dataset contains only one particle per hologram. If you are successful in processing the first dataset, or you wish to immediately focus on a more challenging case, you can work on the second dataset that contains three particles per hologram. Software RequirementsThis notebook requires Python >= 3.7. The following libraries are required:* numpy* scipy* matplotlib* xarray* pandas* scikit-learn* tensorflow >= 2.1* netcdf4* h5netcdf* tqdm* s3fs* zarr | !pip install numpy scipy matplotlib xarray pandas scikit-learn tensorflow netcdf4 h5netcdf tqdm s3fs zarr
# if working on google colab, uncomment and enable save to google drive
# ! pip install -U -q PyDrive
# from google.colab import drive
# drive.mount('/content/gdrive') | _____no_output_____ | MIT | notebooks/holodec.ipynb | carlosenciso/ai4ess-hackathon-2020 |
DataThe datasets consist of synthetically-generated holograms of cloud droplets. Each dataset is in zarr format, and contains a series of hologram images as well as the properties of each particle in the image. The zarr variable names and properties are as follows:| Variable Name | Description | Dimensions | Units/Range|| ------------- | :----:|:----------- |:------|| image | Stack of single-color images. Each image is 600x400 pixels, ranging from 0-255 in intensity. | nHolograms, 600, 400 | 0 to 255 (grayscale image) || x | X-position of each particle in the dataset. The origin is at the center of the hologram image. | nParticles (can vary) | -888 to 888 micrometers || y | Y-position of each particle in the dataset. The origin is at the center of the hologram image. | nParticles (can vary) | -592 to 592 micrometers || z | Z-position of each particle in the dataset. The origin is at the focal plane of the instrument (all particles are unfocused). | nParticles (can vary) | 14000 to 158000 micrometers || d | Diameter of each simulated droplet | nParticles (can vary) | 20 to 70 micrometers || hid | Hologram ID specifies which hologram this particle is contained in. For example, if hid=1, the corresponding x, y, z, and d variables are found in the first hologram. | nParticles (can vary) | 1 to nHolograms || Dx (global attribute) | Resolution of each pixel, == 2.96 micrometers. Use if you wish to convert x/y position to pixel number | | |There are two datasets for this project, a single-particle dataset and a three-particle dataset. The single-particle dataset only contains one particle per hologram (nHolograms = nParticles). There are 50,000 holograms in the training dataset that correspond to 50,000 particles.The three-particle dataset contains three particles per hologram. This dataset also contains 50,000 holograms but 150,000 particles. Be sure to use the hid variable to figure out which hologram a particle is contained in.The ultimate goal of this project is to be able to find particles in the holograms and determine their x, y, z, and d values. This process is straightforward for finding a single particle, but finding multiple particles and their properties is much more challenging. A simpler objective that could also assist in speeding up the HOLODEC processing is calculating the relative distribution of particle mass in the z-direction from the holograms, which is a combination of information from z and d. Potential Input Variables| Variable Name | Units | Description | Relevance || ------------- | :----:|:----------- | :--------:|| hologram | arbitrary | 8 bit (0-255) amplitude captured by CCD | standard input data for processing | Output Variables| Variable Name | Units | Description || ------------- | :----:|:----------- || x | µm | particle horizontal position || y | µm | particle vertical position || z | µm | particle position in depth (along the direction of propagation) || d | µm | particle diameter || hid | arbitrary | hologram ID by particle| Training SetThe single-particle training dataset is in the zarr format described above, with 15,000 holograms and 15,000 corresponding particles.The three-particle training dataset contains 15,000 holograms and 45,000 particles. Validation SetThe single-particle validation dataset is in the zarr format described above, with 5,000 holograms and 5,000 corresponding particles.The three-particle validation dataset contains 5,000 holograms and 15,000 particles. Test SetThe single-particle test dataset is in the zarr format described above, with 5,000 holograms and 5,000 corresponding particles.The three-particle test dataset contains 5,000 holograms and 15,000 particles. Data TransformsThe input images only need to be normalized between 0 and 1 by dividing by 255. | # Module imports
import argparse
import random
import os
from os.path import join, exists
import sys
import s3fs
import yaml
import zarr
import xarray as xr
import numpy as np
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler, RobustScaler
from sklearn.metrics import mean_absolute_error, max_error
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, Dense, Flatten, MaxPool2D
from tensorflow.keras.models import Model, save_model
from tensorflow.keras.optimizers import Adam, SGD
seed = 328942
np.random.seed(seed)
random.seed(seed)
tf.random.set_seed(seed)
# Limit GPU memory usage
gpus = tf.config.get_visible_devices("GPU")
for device in gpus:
print(device)
tf.config.experimental.set_memory_growth(device, True)
# define some datset helper functions
num_particles_dict = {
1 : '1particle',
3 : '3particle',
'multi': 'multiparticle'}
split_dict = {
'train' : 'training',
'test' : 'test',
'valid': 'validation'}
def dataset_name(num_particles, split, file_extension='zarr'):
"""
Return the dataset filename given user inputs
Args:
num_particles: (int or str) Number of particles per hologram (1, 3, or 'multi')
split: (str) Dataset split of either 'train', 'valid', or 'test'
file_extension: (str) Dataset file extension
Returns:
dataset: (str) Dataset name
"""
valid = [1,3,'multi']
if num_particles not in valid:
raise ValueError("results: num_particles must be one of %r." % valid)
num_particles = num_particles_dict[num_particles]
valid = ['train','test','valid']
if split not in valid:
raise ValueError("results: split must be one of %r." % valid)
split = split_dict[split]
return f'synthetic_holograms_{num_particles}_{split}_small.{file_extension}'
def open_zarr(path_data, num_particles, split):
"""
Open a HOLODEC Zarr file hosted on AWS
Args:
path_data: (str) Path to directory containing datset
num_particles: (int or str) Number of particles per hologram (1, 3, or 'multi')
split: (str) Dataset split of either 'train', 'valid', or 'test'
Returns:
dataset: (xarray Dataset) Opened dataset
"""
path_data = os.path.join(path_data, dataset_name(num_particles, split))
fs = s3fs.S3FileSystem(anon=True, default_fill_cache=False)
store = s3fs.S3Map(root=path_data, s3=fs, check=False)
dataset = xr.open_zarr(store=store)
return dataset
def scale_images(images, scaler_vals=None):
"""
Takes in array of images and scales pixel values between 0 and 1
Args:
images: (np array) Array of images
scaler_vals: (dict) Image scaler 'max' and 'min' values
Returns:
images_scaled: (np array) Scaled array of images with pixel values between 0 and 1
scaler_vals: (dict) Image scaler 'max' and 'min' values
"""
if scaler_vals is None:
scaler_vals = {}
scaler_vals["min"] = images.min()
scaler_vals["max"] = images.max()
images_scaled = (images.astype(np.float32) - scaler_vals["min"]) / (scaler_vals["max"] - scaler_vals["min"])
return images_scaled, scaler_vals
def load_scaled_datasets(path_data, num_particles, output_cols, slice_idx,
split='train', scaler_vals=None):
"""
Given a path to training or validation datset, the number of particles per
hologram, and output columns, returns scaled inputs and raw outputs.
Args:
path_data: (str) Path to directory containing training and validation datsets
num_particles: (int or str) Number of particles per hologram (1, 3, or 'multi')
output_cols: (list of strings) List of feature columns to be used
Returns:
inputs_scaled: (np array) Input data scaled between 0 and 1
outputs: (df) Output data specified by output_cols
scaler_vals: (dict) list of training/validation/test files
"""
if split == 'valid':
slice_idx = int(slice_idx/3)
print("Slicing data into inputs/outputs")
ds = open_zarr(path_data, num_particles, split)
inputs = ds["image"].values[:slice_idx]
outputs = ds[output_cols].to_dataframe().loc[:slice_idx-1,:]
ds.close()
print(f"\t- outputs.shape: {outputs.shape}")
print("Scaling input data")
if split == 'train':
inputs_scaled, scaler_vals = scale_images(inputs)
else:
slice_idx = int(slice_idx/3)
inputs_scaled, _ = scale_images(inputs, scaler_vals)
inputs_scaled = np.expand_dims(inputs_scaled, -1)
print(f"\t- inputs_scaled.shape: {inputs_scaled.shape}")
return inputs_scaled, outputs, scaler_vals
# data definitions
path_data = "ncar-aiml-data-commons/holodec/"
num_particles = 3
output_cols = ["hid", "x", "y", "z", "d"]
num_z_bins = 20
slice_idx = 15000
# load and normalize data (this takes approximately 2 minutes)
train_inputs_scaled,\
train_outputs,\
scaler_vals = load_scaled_datasets(path_data,
num_particles,
output_cols,
slice_idx)
valid_inputs_scaled,\
valid_outputs, _ = load_scaled_datasets(path_data,
num_particles,
output_cols,
slice_idx,
split='valid',
scaler_vals=scaler_vals)
# Plot a single hologram with the particles overlaid
def plot_hologram(h, outputs):
"""
Given a hologram number, plot hologram and particle point
Args:
h: (int) hologram number
Returns:
print of pseudocolor plot of hologram and hologram particles
"""
x_vals = np.linspace(-888, 888, train_inputs_scaled[h, :, :, 0].shape[0])
y_vals = np.linspace(-592, 592, train_inputs_scaled[h, :, :, 0].shape[1])
plt.figure(figsize=(12, 8))
plt.pcolormesh(x_vals, y_vals, train_inputs_scaled[h, :, :, 0].T, cmap="RdBu_r")
h_particles = np.where(outputs["hid"] == h + 1)[0]
for h_particle in h_particles:
plt.scatter(outputs.loc[h_particle, "x"],
outputs.loc[h_particle, "y"],
outputs.loc[h_particle, "d"] ** 2,
outputs.loc[h_particle, "z"],
vmin=outputs["z"].min(),
vmax=outputs["z"].max(),
cmap="cool")
plt.annotate(f"d: {outputs.loc[h_particle,'d']:.1f} µm",
(outputs.loc[h_particle, "x"], outputs.loc[h_particle, "y"]))
plt.xlabel("horizontal particle position (µm)", fontsize=16)
plt.ylabel("vertical particle position (µm)", fontsize=16)
plt.title("Hologram and particle positions plotted in four dimensions", fontsize=20, pad=20)
plt.colorbar().set_label(label="z-axis particle position (µm)", size=16)
h = 300
plot_hologram(h, train_outputs) | _____no_output_____ | MIT | notebooks/holodec.ipynb | carlosenciso/ai4ess-hackathon-2020 |
Baseline Machine Learning ModelA baseline model for solving this problem uses a ConvNET architecture implemented in Keras. The first three convolution layers consist of 5 x 5 pixel kernels with rectified linear unit (relu) activation followed by a 4 x 4 pixel max pool layer. The first convolution layer has 8 channels, the second contains 16 channels, and the third contains 32 channels. The output of the third convolution layer is flattened and fed into a dense layer with 64 neurons and relu activation. Finally the output layer consists of the relative mass in 20 bins. The model is trained using a mean absolute error and categorical cross-entropy loss function.Training time: 20 epochs in ~2.5 minutes | class Conv2DNeuralNetwork(object):
"""
A Conv2D Neural Network Model that can support arbitrary numbers of layers.
Attributes:
filters: List of number of filters in each Conv2D layer
kernel_sizes: List of kernel sizes in each Conv2D layer
conv2d_activation: Type of activation function for conv2d layers
pool_sizes: List of Max Pool sizes
dense_sizes: Sizes of dense layers
dense_activation: Type of activation function for dense layers
output_activation: Type of activation function for output layer
lr: Optimizer learning rate
optimizer: Name of optimizer or optimizer object.
adam_beta_1: Exponential decay rate for the first moment estimates
adam_beta_2: Exponential decay rate for the first moment estimates
sgd_momentum: Stochastic Gradient Descent momentum
decay: Optimizer decay
loss: Name of loss function or loss object
batch_size: Number of examples per batch
epochs: Number of epochs to train
verbose: Level of detail to provide during training
model: Keras Model object
"""
def __init__(self, filters=(8,), kernel_sizes=(5,), conv2d_activation="relu",
pool_sizes=(4,), dense_sizes=(64,), dense_activation="relu", output_activation="softmax",
lr=0.001, optimizer="adam", adam_beta_1=0.9, adam_beta_2=0.999,
sgd_momentum=0.9, decay=0, loss="mse", batch_size=32, epochs=2, verbose=0):
self.filters = filters
self.kernel_sizes = [tuple((v,v)) for v in kernel_sizes]
self.conv2d_activation = conv2d_activation
self.pool_sizes = [tuple((v,v)) for v in pool_sizes]
self.dense_sizes = dense_sizes
self.dense_activation = dense_activation
self.output_activation = output_activation
self.lr = lr
self.optimizer = optimizer
self.optimizer_obj = None
self.adam_beta_1 = adam_beta_1
self.adam_beta_2 = adam_beta_2
self.sgd_momentum = sgd_momentum
self.decay = decay
self.loss = loss
self.batch_size = batch_size
self.epochs = epochs
self.verbose = verbose
self.model = None
def build_neural_network(self, input_shape, output_shape):
"""Create Keras neural network model and compile it."""
conv_input = Input(shape=(input_shape), name="input")
nn_model = conv_input
for h in range(len(self.filters)):
nn_model = Conv2D(self.filters[h], self.kernel_sizes[h], padding="same",
activation=self.conv2d_activation, name=f"conv2D_{h:02d}")(nn_model)
nn_model = MaxPool2D(self.pool_sizes[h], name=f"maxpool2D_{h:02d}")(nn_model)
nn_model = Flatten()(nn_model)
for h in range(len(self.dense_sizes)):
nn_model = Dense(self.dense_sizes[h], activation=self.dense_activation, name=f"dense_{h:02d}")(nn_model)
nn_model = Dense(output_shape, activation=self.output_activation, name=f"dense_output")(nn_model)
self.model = Model(conv_input, nn_model)
if self.optimizer == "adam":
self.optimizer_obj = Adam(lr=self.lr, beta_1=self.adam_beta_1, beta_2=self.adam_beta_2, decay=self.decay)
elif self.optimizer == "sgd":
self.optimizer_obj = SGD(lr=self.lr, momentum=self.sgd_momentum, decay=self.decay)
self.model.compile(optimizer=self.optimizer, loss=self.loss)
self.model.summary()
def fit(self, x, y, xv, yv):
if len(y.shape) == 1:
output_shape = 1
else:
output_shape = y.shape[1]
input_shape = x.shape[1:]
self.build_neural_network(input_shape, output_shape)
self.model.fit(x, y, batch_size=self.batch_size, epochs=self.epochs,
verbose=self.verbose, validation_data=(xv, yv))
return self.model.history.history
def predict(self, x):
y_out = self.model.predict(x, batch_size=self.batch_size)
return y_out
def predict_proba(self, x):
y_prob = self.model.predict(x, batch_size=self.batch_size)
return y_prob
| _____no_output_____ | MIT | notebooks/holodec.ipynb | carlosenciso/ai4ess-hackathon-2020 |
Z Relative Particle Mass ModelThis neural network is tasked to predict the distribution of particle mass in the z-plane of the instrument. The relative mass is calculated by calculating the volume of each sphere based on the area and dividing by the total mass of all particles. The advantage of this target is that it behaves like a probability density function and sums to 1, and it is agnostic to the number of particles in the image. | def calc_z_relative_mass(outputs, holograms, num_z_bins=20, z_bins=None):
"""
Calculate z-relative mass from particle data.
Args:
outputs: (np array) Output data previously specified by output_cols
holograms: (int) Number of holograms
num_z_bins: (int) Number of bins for z_bins linspace
z_bins: (np array) Bin linspace along the z-axis
Returns:
z_mass: (np array) Particle mass distribution by hologram
z_bins: (np array) Bin linspace along the z-axis
"""
if z_bins is None:
z_bins = np.linspace(outputs["z"].min()- 100, outputs["z"].max() + 100, num_z_bins)
print(z_bins)
else:
num_z_bins = z_bins.size
z_mass = np.zeros((holograms, num_z_bins), dtype=np.float32)
for i in range(outputs.shape[0]):
z_pos = np.searchsorted(z_bins, outputs.loc[i, "z"], side="right") - 1
mass = 4 / 3 * np.pi * (outputs.loc[i, "d"])**3
z_mass[int(outputs.loc[i, "hid"]) - 1, z_pos] += mass
z_mass /= np.expand_dims(z_mass.sum(axis=1), -1)
print(f"z_mass.shape: {z_mass.shape}\nz_bins.shape: {z_bins.shape}")
return z_mass, z_bins
z_bins = np.linspace(np.minimum(train_outputs["z"].min(), valid_outputs["z"].min()),
np.maximum(train_outputs["z"].max(), valid_outputs["z"].max()),
num_z_bins)
train_z_mass, _ = calc_z_relative_mass(train_outputs, len(train_outputs["hid"].unique()), z_bins=z_bins)
valid_z_mass, _ = calc_z_relative_mass(valid_outputs, len(valid_outputs["hid"].unique()), z_bins=z_bins)
train_inputs_scaled = train_inputs_scaled[0::3]
valid_inputs_scaled = valid_inputs_scaled[0::3]
| _____no_output_____ | MIT | notebooks/holodec.ipynb | carlosenciso/ai4ess-hackathon-2020 |
Three particle, z-mass model definition | # conv2d_network definitions for 3 particle z mass solution
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
path_out = "/content/gdrive/My Drive/micro_models/3particle_base"
else:
path_out = "./holodec_models/3particle_base/"
if not exists(path_out):
os.makedirs(path_out)
model_name = "cnn"
filters = [16, 24, 32]
kernel_sizes = [5, 5, 5]
conv2d_activation = "relu"
pool_sizes = [4, 4, 4]
dense_sizes = [64, 32]
dense_activation = "elu"
lr = 0.0003
decay = 0.1
optimizer = "adam"
loss = "categorical_crossentropy"
batch_size = 128
epochs = 40
verbose = 1
seed = 328942
np.random.seed(seed)
random.seed(seed)
tf.random.set_seed(seed)
# 3 particle z mass model build, compile, fit, and predict
three_start = datetime.now()
with tf.device('/device:GPU:0'):
mod = Conv2DNeuralNetwork(filters=filters, kernel_sizes=kernel_sizes,
conv2d_activation=conv2d_activation,
pool_sizes=pool_sizes, dense_sizes=dense_sizes,
dense_activation=dense_activation, lr=lr,
optimizer=optimizer, decay=decay, loss=loss,
batch_size=batch_size, epochs=epochs, verbose=verbose)
hist = mod.fit(train_inputs_scaled, train_z_mass, valid_inputs_scaled, valid_z_mass)
train_z_mass_pred = mod.predict(train_inputs_scaled)
valid_z_mass_pred = mod.predict(valid_inputs_scaled)
print(f"Running model took {datetime.now() - three_start} time")
# visualize loss history
plt.plot(hist['loss'])
plt.plot(hist['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['training', 'validation'], loc='upper left')
plt.show()
# save the model
print("Saving the model")
mod.model.save(join(path_out, model_name +".h5"))
# clear your tf session without needing to re-load and re-scale data
del mod
tf.keras.backend.clear_session()
| _____no_output_____ | MIT | notebooks/holodec.ipynb | carlosenciso/ai4ess-hackathon-2020 |
Three Particle MetricsHow well do individual predictions (red) match with the actual particle locations (blue)? | valid_index = 11
bin_size = z_bins[1] - z_bins[0]
plt.figure(figsize=(10, 6))
plt.bar(z_bins / 1000, valid_z_mass_pred[valid_index], bin_size / 1000, color='red', label="Predicted")
plt.bar(z_bins / 1000, valid_z_mass[valid_index], bin_size / 1000, edgecolor='blue', facecolor="none", lw=3, label="True")
plt.ylim(0, 1)
plt.xlabel("z-axis particle position (mm)", fontsize=16)
plt.ylabel("relative mass", fontsize=16)
plt.legend(loc="best")
| _____no_output_____ | MIT | notebooks/holodec.ipynb | carlosenciso/ai4ess-hackathon-2020 |
If the model was completely unbiased, then mean relative mass in each bin should be nearly the same across all validation examples. In this case we see that the CNN preferentially predicts that the mass is closer to the camera, likely due to a combination of particles closer to the camera blocking those farther away along with more distant particles influencing the entire image. Since the CNN assumes image properties are more localized, it will struggle to detect the particles that are farther away. | plt.bar(z_bins / 1000, valid_z_mass_pred.mean(axis=0), (z_bins[1] - z_bins[0]) / 1000, color='red')
plt.bar(z_bins / 1000, valid_z_mass.mean(axis=0), (z_bins[1]-z_bins[0]) / 1000, edgecolor='blue', facecolor="none", lw=3)
plt.xlabel("z location (mm)", fontsize=16)
plt.ylabel("Mean Relative Mass", fontsize=16)
def ranked_probability_score(y_true, y_pred):
return np.mean((np.cumsum(y_true, axis=1) - np.cumsum(y_pred, axis=1)) ** 2) / (y_true.shape[1] -1)
rps_nn = ranked_probability_score(valid_z_mass, valid_z_mass_pred)
rps_climo = ranked_probability_score(valid_z_mass, np.ones(valid_z_mass_pred.shape) / valid_z_mass_pred.shape[1])
print(rps_nn, rps_climo)
rpss = 1 - rps_nn / rps_climo
print(f"RPSS: {rpss:0.3f}") | _____no_output_____ | MIT | notebooks/holodec.ipynb | carlosenciso/ai4ess-hackathon-2020 |
One Particle ModelAn easier problem is predicting the location and properties of synthetic single particles. | # data definitions
path_data = "ncar-aiml-data-commons/holodec/"
num_particles = 1
output_cols_one = ["x", "y", "z", "d"]
scaler_one = MinMaxScaler()
slice_idx = 15000
# load and normalize data (this takes approximately 2 minutes)
train_inputs_scaled_one,\
train_outputs_one,\
scaler_vals_one = load_scaled_datasets(path_data,
num_particles,
output_cols_one,
slice_idx)
valid_inputs_scaled_one,\
valid_outputs_one, _ = load_scaled_datasets(path_data,
num_particles,
output_cols_one,
slice_idx,
split='valid',
scaler_vals=scaler_vals_one)
# extra transform step for output_cols_one in lieu of z mass
train_outputs_scaled_one = scaler_one.fit_transform(train_outputs_one[output_cols_one])
valid_outputs_scaled_one = scaler_one.transform(valid_outputs_one[output_cols_one])
# conv2d_network definitions for 1 particle 4D solution
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
path_out = "/content/gdrive/My Drive/micro_models/1particle_base"
else:
path_out = "./holodec_models/1particle_base/"
if not exists(path_out):
os.makedirs(path_out)
model_name = "cnn"
filters = [16, 24, 32]
kernel_sizes = [5, 5, 5]
conv2d_activation = "relu"
pool_sizes = [4, 4, 4]
dense_sizes = [64, 32]
dense_activation = "relu"
lr = 0.0001
optimizer = "adam"
loss = "mae"
batch_size = 128
epochs = 20
verbose = 1
if not exists(path_out):
os.makedirs(path_out)
# 1 particle 4D model build, compile, fit, and predict
one_start = datetime.now()
with tf.device('/device:GPU:0'):
mod = Conv2DNeuralNetwork(filters=filters, kernel_sizes=kernel_sizes, conv2d_activation=conv2d_activation,
pool_sizes=pool_sizes, dense_sizes=dense_sizes, dense_activation=dense_activation,
lr=lr, optimizer=optimizer, loss=loss, batch_size=batch_size, epochs=epochs, verbose=verbose)
mod.fit(train_inputs_scaled_one, train_outputs_scaled_one, valid_inputs_scaled_one, valid_outputs_scaled_one)
train_preds_scaled_one = pd.DataFrame(mod.predict(train_inputs_scaled_one), columns=output_cols_one)
valid_preds_scaled_one = pd.DataFrame(mod.predict(valid_inputs_scaled_one), columns=output_cols_one)
print(f"Running model took {datetime.now() - one_start} time")
# inverse transform of scaled predictions
train_preds_one = pd.DataFrame(scaler_one.inverse_transform(train_preds_scaled_one.values), columns=output_cols_one)
valid_preds_one = pd.DataFrame(scaler_one.inverse_transform(valid_preds_scaled_one.values), columns=output_cols_one)
| _____no_output_____ | MIT | notebooks/holodec.ipynb | carlosenciso/ai4ess-hackathon-2020 |
One Particle MetricsAn ideal solution to HOLODEC processing would leverage all the advantages of the instrument (unparalleled particle position and size accuracy) but reduce the drawbacks (processing time). For this reason, the major components of the model assessment should include:Mean absolute error in predictions for single-particle dataset:| Variable Name | Error || ------------- |:----------- || x | 290 µm || y | 170 µm || z | 53,271 µm || d | 16 µm | | # calculate error by output_cols_one
valid_maes_one = np.zeros(len(output_cols_one))
max_errors_one = np.zeros(len(output_cols_one))
for o, output_col in enumerate(output_cols_one):
valid_maes_one[o] = mean_absolute_error(valid_outputs_one[output_col], valid_preds_one[output_col])
max_errors_one[o] = max_error(valid_outputs_one[output_col], valid_preds_one[output_col])
print(f"{output_col} MAE: {valid_maes_one[o]:,.0f} µm \t\t Max Error: {max_errors_one[o]:,.0f} µm")
| _____no_output_____ | MIT | notebooks/holodec.ipynb | carlosenciso/ai4ess-hackathon-2020 |
Hackathon Challenges Monday* Load the data* Create an exploratory visualization of the data* Test two different transformation and scaling methods* Test one dimensionality reduction method* Train a linear model* Train a decision tree ensemble method of your choice | # Monday's code goes here
| _____no_output_____ | MIT | notebooks/holodec.ipynb | carlosenciso/ai4ess-hackathon-2020 |
Tuesday* Train a densely connected neural network* Train a convolutional or recurrent neural network (depends on problem)* Experiment with different architectures | # Tuesday's code goes here
| _____no_output_____ | MIT | notebooks/holodec.ipynb | carlosenciso/ai4ess-hackathon-2020 |
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