diff --git "a/README.md" "b/README.md" --- "a/README.md" +++ "b/README.md" @@ -11,187 +11,242 @@ metrics: - recall - f1 widget: -- text: "

I'm having a problem serving my text classification model on Tensorflow\ - \ 1.12. I'm using tf.estimator.inputs.pandas_input_fn to read\ - \ in my data, and tf.estimator.DNNClassifier to train/evaluate. I'd\ - \ then like to serve my model.\n(Apologies in advance, it's tough to provide a\ - \ full working example here, but it's very much like the example TF provides at\ - \ https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier\ - \ )

\n\n

I'm currently saving my model with ...

\n\n
...\nestimator.export_savedmodel(\"./TEST_SERVING/\"\
-    , self.serving_input_receiver_fn, strip_default_attrs=True)\n...\ndef serving_input_receiver_fn(self):\n\
-    \      \"\"\"An input receiver that expects a serialized tf.Example.\"\"\"\n\n\
-    \      # feature spec dictionary  determines our input parameters for the model\n\
-    \      feature_spec = {\n          'Headline': tf.VarLenFeature(dtype=tf.string),\n\
-    \          'Description': tf.VarLenFeature(dtype=tf.string)\n      }\n\n     \
-    \ # the inputs will be initially fed as strings with data serialized by\n    \
-    \  # Google ProtoBuffers\n      serialized_tf_example = tf.placeholder(\n    \
-    \      dtype=tf.string, shape=None, name='input_example_tensor')\n      receiver_tensors\
-    \ = {'examples': serialized_tf_example}\n\n      # deserialize input\n      features\
-    \ = tf.parse_example(serialized_tf_example, feature_spec)\n      return tf.estimator.export.ServingInputReceiver(features,\
-    \ receiver_tensors)\n\n\n
\n\n

This actually fails to run with the\ - \ error:

\n\n
TypeError: Failed\
-    \ to convert object of type <class 'tensorflow.python.framework.sparse_tensor.SparseTensor'>\
-    \ to Tensor. Contents: SparseTensor(indices=Tensor(\"ParseExample/ParseExample:0\"\
-    , shape=(?, 2), \ndtype=int64), values=Tensor(\"ParseExample/ParseExample:2\"\
-    , shape=(?,), dtype=string), dense_shape=Tensor(\"ParseExample/ParseExample:4\"\
-    , shape=(2,), dtype=int64)). Consider casting elements to a supported type.\n\n\
-
\n\n

I tried to save a second way doing:

\n\n
def serving_input_receiver_fn(self):\n  \"\"\"\
-    Build the serving inputs.\"\"\"\n  INPUT_COLUMNS = [\"Headline\",\"Description\"\
-    ]\n  inputs = {}\n  for feat in INPUT_COLUMNS:\n    inputs[feat] = tf.placeholder(shape=[None],\
-    \ dtype=tf.string, name=feat)\n  return tf.estimator.export.ServingInputReceiver(inputs,\
-    \ inputs)\n
\n\n

This actually works, until I try testing it with\ - \ the saved_model_cli.\nSome output for saved_model_cli show\ - \ --all --dir TEST_SERVING/1553879255/:

\n\n
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:\n\
-    \nsignature_def['predict']:\n  The given SavedModel SignatureDef contains the\
-    \ following input(s):\n    inputs['Description'] tensor_info:\n        dtype:\
-    \ DT_STRING\n        shape: (-1)\n        name: Description:0\n    inputs['Headline']\
-    \ tensor_info:\n        dtype: DT_STRING\n        shape: (-1)\n        name: Headline:0\n\
-    \  The given SavedModel SignatureDef contains the following output(s):\n    outputs['class_ids']\
-    \ tensor_info:\n        dtype: DT_INT64\n        shape: (-1, 1)\n        name:\
-    \ dnn/head/predictions/ExpandDims:0\n    outputs['classes'] tensor_info:\n   \
-    \     dtype: DT_STRING\n        shape: (-1, 1)\n        name: dnn/head/predictions/str_classes:0\n\
-    \    outputs['logits'] tensor_info:\n        dtype: DT_FLOAT\n        shape: (-1,\
-    \ 3)\n        name: dnn/logits/BiasAdd:0\n    outputs['probabilities'] tensor_info:\n\
-    \        dtype: DT_FLOAT\n        shape: (-1, 3)\n        name: dnn/head/predictions/probabilities:0\n\
-    \  Method name is: tensorflow/serving/predict\n\n
\n\n

But now I\ - \ can't seem to test it.

\n\n
>>>\
-    \ saved_model_cli run --dir TEST_SERVING/1553879255/ --tag_set serve --signature_def\
-    \ predict --input_examples 'inputs=[{\"Description\":[\"What is going on\"],\"\
-    Headline\":[\"Help me\"]}]'\nTraceback (most recent call last):\n ...\n  File\
-    \ \"/Users/Josh/miniconda3/envs/python36/lib/python3.6/site-packages/tensorflow/python/tools/saved_model_cli.py\"\
-    , line 489, in _create_example_string\n    feature_list)\nTypeError: 'What is\
-    \ going on' has type str, but expected one of: bytes\n\n
\n\n

Ok,\ - \ lets turn it into a bytes object by changing to b[\"What is going on\"\ - ] and b[\"Help me\"]...

\n\n
ValueError: Type <class 'bytes'> for value b'What is going on' is\
-    \ not supported for tf.train.Feature.\n
\n\n

Any ideas/thoughts??\n\ - Thanks!

\n" -- text: '

In tensorflow tf.keras.Model.compile, you can pass a lambda - y_true, y_pred: val function as a metric (though, it seems not documented), - but I asked my self : "How does it aggregate it over the batches" ?

- - -

I searched the documentation, but I''ve found nowhere how it is done ?

- - -

By the way, I don''t even know if it is an undefined behavior to do so and - one should instead subclass the Metric class ? ( or at least provide the required - methods).

- - -

Also, is it pertinent to pass a loss as a metric (and in this case, same question - : how is it aggregated over the batches ? )

- - ' -- text: "

I'm working on a project where I have trained a series of binary classifiers\ - \ with Keras, with Tensorflow as the backend\ - \ engine. The input data I have is a series of images, where each binary classifier\ - \ must make the prediction on the images, later I save the predictions on a CSV\ - \ file.

\n

The problem I have is when I get the predictions from the first\ - \ series of binary classifiers there isn't any warning, but when the 5th or 6th\ - \ binary classifier calls the method predict on the input data\ - \ I get the following warning:

\n
\n

WARNING:tensorflow:5 out\ - \ of the last 5 calls to <function\nModel.make_predict_function..predict_function\ - \ at\n0x2b280ff5c158> triggered tf.function retracing. Tracing is expensive\n\ - and the excessive number of tracings could be due to (1) creating\n@tf.function\ - \ repeatedly in a loop, (2) passing tensors with different\nshapes, (3) passing\ - \ Python objects instead of tensors. For (1), please\ndefine your @tf.function\ - \ outside of the loop. For (2), @tf.function\nhas experimental_relax_shapes=True\ - \ option that relaxes argument shapes\nthat can avoid unnecessary retracing. For\ - \ (3), please refer to\nhttps://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args\n\ - and https://www.tensorflow.org/api_docs/python/tf/function for more\n\ - details.

\n
\n

To answer each point in the parenthesis, here\ - \ are my answers:

\n
    \n
  1. The predict method is called\ - \ inside a for loop.
    2. \n
  2. I don't pass tensors but a list of NumPy\ - \ arrays of gray scale images, all of them with the same size in width\ - \ and height. The only thing that can change is the batch size because the list\ - \ can have only 1 image or more than one.
    3. \n
  3. As I wrote in point 2, I pass\ - \ a list of NumPy arrays.
    4. \n
\n

I have debugged my program and found\ - \ that this warning always happens when the method predict is called. To summarize\ - \ the code I have written is the following:

\n
import cv2 as cv\n\
-    import tensorflow as tf\nfrom tensorflow.keras.models import load_model\n# Load\
-    \ the models\nbinary_classifiers = [load_model(path) for path in path2models]\n\
-    # Get the images\nimages = [#Load the images with OpenCV]\n# Apply the resizing\
-    \ and reshapes on the images.\nmy_list = list()\nfor image in images:\n    image_reworked\
-    \ = # Apply the resizing and reshaping on images\n    my_list.append(image_reworked)\n\
-    \n# Get the prediction from each model\n# This is where I get the warning\npredictions\
-    \ = [model.predict(x=my_list,verbose=0) for model in binary_classifiers]\n
\n\ -

What I have tried

\n

I have defined a function as tf.function and putted\ - \ the code of the predictions inside the tf.function like this

\n
@tf.function\n\
-    def testing(models, faces):\n    return [model.predict(x=faces,verbose=0) for\
-    \ model in models]\n
\n

But I ended up getting the following error:

\n\ -
\n

RuntimeError: Detected a call to Model.predict inside\ - \ a\ntf.function. Model.predict is a high-level endpoint that manages\n\ - its own tf.function. Please move the call to Model.predict\ - \ outside\nof all enclosing tf.functions. Note that you can call\ - \ a Model\ndirectly on Tensors inside a tf.function\ - \ like: model(x).

\n
\n

So calling the method predict\ - \ is basically already a tf.function. So it's useless to define a tf.function\ - \ when the warning I get it's from that method.

\n

I have also checked those\ - \ other two questions:

\n
    \n
  1. Tensorflow 2: Getting "WARNING:tensorflow:9 out of the last 9 calls to \ - \ triggered tf.function retracing. Tracing is expensive"
    2. \n
  2. Loading multiple saved tensorflow/keras models for prediction
    3. \n
\n\ -

But neither of the two questions answers my question about how to avoid this\ - \ warning. Plus I have also checked the links in the warning message but I couldn't\ - \ solve my problem.

\n

What I want

\n

I simply want to avoid this\ - \ warning. While I'm still getting the predictions from the models I noticed that\ - \ the python program takes way too much time on doing predictions for a list of\ - \ images.

\n

What I'm using

\n\n

Solution

\n

After some tries to suppress the warning\ - \ from the predict method, I have checked the documentation of Tensorflow\ - \ and in one of the first tutorials on how to use Tensorflow it is explained that,\ - \ by default, Tensorflow is executed in eager mode, which is useful for testing\ - \ and debugging the network models. Since I have already tested my models many\ - \ times, it was only required to disable the eager mode by writing this single\ - \ python line of code:

\n

tf.compat.v1.disable_eager_execution()

\n\ -

Now the warning doesn't show up anymore.

\n" -- text: '

Where one can find the github source code for tf.quantization.fake_quant_with_min_max_args. - Checking the TF API documentation, there is no link to the github - source file, and I could not find one on github.

- - ' -- text: "

I'm trying to use tf.Dataset for a 3D image CNN where the\ - \ shape of the 3D image fed into it from the training set and the validation set\ - \ are different (training: (64, 64, 64), validation: (176, 176, 160)). I didn't\ - \ even know this was possible, but I'm recreating this network based on a paper,\ - \ and using the classic feed_dict method the network indeed works.\ - \ For performance reasons (and just to learn) I'm trying to switch the network\ - \ to use tf.Dataset instead.

\n\n

I have two datasets and iterators\ - \ built like the following:

\n\n
def _data_parser(dataset, shape):\n        features = {\"input\": tf.FixedLenFeature((),\
-    \ tf.string),\n                    \"label\": tf.FixedLenFeature((), tf.string)}\n\
-    \        parsed_features = tf.parse_single_example(dataset, features)\n\n    \
-    \    image = tf.decode_raw(parsed_features[\"input\"], tf.float32)\n        image\
-    \ = tf.reshape(image, shape + (1,))\n\n        label = tf.decode_raw(parsed_features[\"\
-    label\"], tf.float32)\n        label = tf.reshape(label, shape + (1,))\n     \
-    \   return image, label\n\ntrain_datasets = [\"train.tfrecord\"]\ntrain_dataset\
-    \ = tf.data.TFRecordDataset(train_datasets)\ntrain_dataset = train_dataset.map(lambda\
-    \ x: _data_parser(x, (64, 64, 64)))\ntrain_dataset = train_dataset.batch(batch_size)\
-    \ # batch_size = 16\ntrain_iterator = train_dataset.make_initializable_iterator()\n\
-    \nval_datasets = [\"validation.tfrecord\"]\nval_dataset = tf.data.TFRecordDataset(val_datasets)\n\
-    val_dataset = val_dataset.map(lambda x: _data_parser(x, (176, 176, 160)))\nval_dataset\
-    \ = val_dataset.batch(1)\nval_iterator = val_dataset.make_initializable_iterator()\n\
-
\n\n

TensorFlow documentation has examples regarding\ - \ switching between datasets using reinitializable_iterator or feedable_iterator,\ - \ but they all switch between iterators of same output shape,\ - \ which is not the case here.

\n\n

How should I switch between training set\ - \ and validation set using tf.Dataset and tf.data.Iterator\ - \ in my case then?

\n" +- text: "

I am new to tensorflow, trying to build a Siamese CNN similar\ + \ to what's done in this guide.
\nMy model is built using a\ + \ base model, which is then fed twice with two different pictures that go through\ + \ the same network.
\nThis is the code for building the network:

\nclass BaseModel(Model):\n\n def\ + \ __init__(self, base_network):\n super(BaseModel, self).__init__()\n self.network\ + \ = base_network\n \n def call(self, inputs):\n print(inputs)\n return\ + \ self.network(inputs)\n\ndef get_base_model():\n inputs = tf.keras.Input(shape=INPUT)\n\ + \n conv2d_1 = layers.Conv2D(name='seq_1', filters=64, \n kernel_size=20,\ + \ \n activation='relu')(inputs)\n maxpool_1 = layers.MaxPooling2D(pool_size=(2,\ + \ 2))(conv2d_1)\n\n conv2d_2 = layers.Conv2D(filters=128, \n kernel_size=20,\ + \ \n activation='relu')(maxpool_1)\n maxpool_2 = layers.MaxPooling2D(pool_size=(2,\ + \ 2))(conv2d_2)\n\n conv2d_3 = layers.Conv2D(filters=128, \n kernel_size=20,\ + \ \n activation='relu')(maxpool_2)\n maxpool_3 = layers.MaxPooling2D(pool_size=(2,\ + \ 2))(conv2d_3)\n\n conv2d_4 = layers.Conv2D(filters=256, \n kernel_size=10,\ + \ \n activation='relu')(maxpool_3)\n\n flatten_1 = layers.Flatten()(conv2d_4)\n\ + \ outputs = layers.Dense(units=4096,\n activation='sigmoid')(flatten_1)\n\ + \ \n model = Model(inputs=inputs, outputs=outputs)\n\n return model\n\n\ +

Then, I'm building the Siamese network using the previous method like that:

\n\ + 
INPUT = (250, 250, 3)\n\ndef\
+    \ get_siamese_model():\n  left_input = layers.Input(name='img1', shape=INPUT)\n\
+    \  right_input = layers.Input(name='img2', shape=INPUT)\n  \n  base_model = get_base_model()\n\
+    \  base_model = BaseModel(base_model)\n\n  # bind the two input layers to the\
+    \ base network\n  left = base_model(left_input)\n  right = base_model(right_input)\n\
+    \n  # build distance measuring layer\n  l1_lambda = layers.Lambda(lambda tensors:abs(tensors[0]\
+    \ - tensors[1]))\n  l1_dist = l1_lambda([left, right])\n\n  pred = layers.Dense(1,activation='sigmoid')(l1_dist)\n\
+    \n  return Model(inputs=[left_input, right_input], outputs=pred)\n\nclass SiameseNetwork(Model):\n\
+    \n  def __init__(self, siamese_network):\n    super(SiameseNetwork, self).__init__()\n\
+    \    self.siamese_network = siamese_network\n  \n  def call(self, inputs):\n \
+    \   print(inputs)\n    return self.siamese_network(inputs)\n
\n

I'm\ + \ then training the network by passing a tf.data.Dataset to it:

\n\ + 
net.fit(x=train_dataset, epochs=10\
+    \ ,verbose=True)\n
\n

train_dataset is of type:

\n\ +
\n

<PrefetchDataset shapes: ((None, 250, 250, 3), (None, 250,\ + \ 250, 3)), types: (tf.float32, tf.float32)>

\n
\n

It seems\ + \ like the shape of the input is defined well, but I'm still encountering an error:

\n\ + 
ValueError                 \
+    \               Traceback (most recent call last)\n<ipython-input-144-6c5586e1e205>\
+    \ in <module>()\n----> 1 net.fit(x=train_dataset, epochs=10 ,verbose=True)\n\
+    \n9 frames\n/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py\
+    \ in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split,\
+    \ validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch,\
+    \ validation_steps, validation_batch_size, validation_freq, max_queue_size, workers,\
+    \ use_multiprocessing)\n   1098                 _r=1):\n   1099              \
+    \ callbacks.on_train_batch_begin(step)\n-> 1100               tmp_logs = self.train_function(iterator)\n\
+    \   1101               if data_handler.should_sync:\n   1102                 context.async_wait()\n\
+    \n/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\
+    \ in __call__(self, *args, **kwds)\n    826     tracing_count = self.experimental_get_tracing_count()\n\
+    \    827     with trace.Trace(self._name) as tm:\n--> 828       result = self._call(*args,\
+    \ **kwds)\n    829       compiler = "xla" if self._experimental_compile\
+    \ else "nonXla"\n    830       new_tracing_count = self.experimental_get_tracing_count()\n\
+    \n/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\
+    \ in _call(self, *args, **kwds)\n    869       # This is the first call of __call__,\
+    \ so we have to initialize.\n    870       initializers = []\n--> 871     \
+    \  self._initialize(args, kwds, add_initializers_to=initializers)\n    872   \
+    \  finally:\n    873       # At this point we know that the initialization is\
+    \ complete (or less\n\n/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\
+    \ in _initialize(self, args, kwds, add_initializers_to)\n    724     self._concrete_stateful_fn\
+    \ = (\n    725         self._stateful_fn._get_concrete_function_internal_garbage_collected(\
+    \  # pylint: disable=protected-access\n--> 726             *args, **kwds))\n\
+    \    727 \n    728     def invalid_creator_scope(*unused_args, **unused_kwds):\n\
+    \n/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in\
+    \ _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\n \
+    \  2967       args, kwargs = None, None\n   2968     with self._lock:\n-> 2969\
+    \       graph_function, _ = self._maybe_define_function(args, kwargs)\n   2970\
+    \     return graph_function\n   2971 \n\n/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\
+    \ in _maybe_define_function(self, args, kwargs)\n   3359 \n   3360           self._function_cache.missed.add(call_context_key)\n\
+    -> 3361           graph_function = self._create_graph_function(args, kwargs)\n\
+    \   3362           self._function_cache.primary[cache_key] = graph_function\n\
+    \   3363 \n\n/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\
+    \ in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\n  \
+    \ 3204             arg_names=arg_names,\n   3205             override_flat_arg_shapes=override_flat_arg_shapes,\n\
+    -> 3206             capture_by_value=self._capture_by_value),\n   3207    \
+    \     self._function_attributes,\n   3208         function_spec=self.function_spec,\n\
+    \n/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\
+    \ in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph,\
+    \ autograph, autograph_options, add_control_dependencies, arg_names, op_return_value,\
+    \ collections, capture_by_value, override_flat_arg_shapes)\n    988         _,\
+    \ original_func = tf_decorator.unwrap(python_func)\n    989 \n--> 990     \
+    \  func_outputs = python_func(*func_args, **func_kwargs)\n    991 \n    992  \
+    \     # invariant: `func_outputs` contains only Tensors, CompositeTensors,\n\n\
+    /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\
+    \ in wrapped_fn(*args, **kwds)\n    632             xla_context.Exit()\n    633\
+    \         else:\n--> 634           out = weak_wrapped_fn().__wrapped__(*args,\
+    \ **kwds)\n    635         return out\n    636 \n\n/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\
+    \ in wrapper(*args, **kwargs)\n    975           except Exception as e:  # pylint:disable=broad-except\n\
+    \    976             if hasattr(e, "ag_error_metadata"):\n--> 977\
+    \               raise e.ag_error_metadata.to_exception(e)\n    978           \
+    \  else:\n    979               raise\n\nValueError: in user code:\n\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:805\
+    \ train_function  *\n        return step_function(self, iterator)\n    <ipython-input-125-de3a74f810c3>:9\
+    \ call  *\n        return self.siamese_network(inputs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:998\
+    \ __call__  **\n        input_spec.assert_input_compatibility(self.input_spec,\
+    \ inputs, self.name)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_spec.py:207\
+    \ assert_input_compatibility\n        ' input tensors. Inputs received: ' + str(inputs))\n\
+    \n    ValueError: Layer model_16 expects 2 input(s), but it received 1 input tensors.\
+    \ Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 250, 250, 3)\
+    \ dtype=float32>]\n
\n

I do undertand that model_16\ + \ is the BaseModel, however I can't figure out what am I doing wrong here.

\n" +- text: "

New to TensorFlow, so apologies for newbie question.

\n\n

Following\ + \ this tutorial but instead of using image data I am\ + \ using numerical data.

\n\n

Load the dataset:

\n\n
train_dataset_url\
+    \ = \"xxx.csv\"\ntrain_dataset_fp = tf.keras.utils.get_file(\n  fname=os.path.basename(train_dataset_url),\n\
+    \  origin=train_dataset_url)\n
\n\n

Make training dataset:

\n\ + \n
batch_size = 32\n\ntrain_dataset = tf.contrib.data.make_csv_dataset(\n\
+    \    train_dataset_fp,\n    batch_size, \n    column_names=column_names,\n   \
+    \ label_name=label_name,\n    num_epochs=1)\n
\n\n

Train\ + \ classified model using:

\n\n

model = tf.keras.Sequential([\n\ + \ tf.keras.layers.Dense(10, activation=tf.nn.relu, input_shape=(1,)),\n tf.keras.layers.Dense(10,\ + \ activation=tf.nn.relu),\n tf.keras.layers.Dense(4)\n])

\n\n

But\ + \ when I \"test\" the model with the same inputs:

\n\n

predictions\ + \ = model(features)

\n\n

I receive the error:

\n\n

InvalidArgumentError:\ + \ cannot compute MatMul as input #0(zero-based) was expected to be a float tensor\ + \ but is a int32 tensor [Op:MatMul]

\n\n

It's possible I have missed\ + \ something fundamental. I feel like I need to specify a type somewhere.

\n" +- text: "

I have created a neural style transfer with Eager Execution, but it does\ + \ not work when I try to turn it into a tf.function.\nThe error message says:

\n\ + 
ValueError: tf.function only supports singleton tf.Variables created\
+    \ on the first call. Make sure the tf.Variable is only created once or created\
+    \ outside tf.function. See https://www.tensorflow.org/guide/function#creating_tfvariables\
+    \ for more information.\n
\n

However, no variable is being created\ + \ inside the function. Here is a simplified version of the code, which is just\ + \ a neural style transfer with one image (the goal is to make the generated image\ + \ look exactly like the content image):

\n
import tensorflow as tf\n\
+    import numpy as np\nfrom PIL import Image\n\n#Get and process the images\nimage\
+    \ = np.array(Image.open("frame7766.jpg")).reshape(1, 720, 1280, 3)/255\n\
+    content_image = tf.convert_to_tensor(image, dtype = tf.float32)\n# variable is\
+    \ defined outside of tf.function\ngenerated_image = tf.Variable(np.random.rand(1,\
+    \ 720, 1280, 3)/2 + content_image/2, dtype = tf.float32)\n\ndef clip_0_1(image):\
+    \ # keeps image values between 0 and 1\n    return tf.clip_by_value(image, clip_value_min=0,\
+    \ clip_value_max=1)\n\n@ tf.function\ndef train_step(generated_image, content_image):\
+    \ #turn generated image into tf variable\n    optimizer = tf.keras.optimizers.Adam(learning_rate\
+    \ = 0.01)\n    with tf.GradientTape() as tape:\n        cost = tf.reduce_sum(tf.square(generated_image\
+    \ - content_image))\n    grad = tape.gradient(cost, generated_image) \n    optimizer.apply_gradients([(grad,\
+    \ generated_image)]) # More information below\n    generated_image.assign(clip_0_1(generated_image))\n\
+    \    return generated_image\n\ngenerated_image = train_step(generated_image, content_image)\n\
+
\n

The error message points to the line

\n
optimizer.apply_gradients([(grad,\
+    \ generated_image)]) \n
\n

I have tried to change the input of \ + \ optimizer.apply_gradients to zip([grad], [generated_image]),\ + \ and every combination of lists and tuples I can think of, but the error still\ + \ remains. I have also looked through https://www.tensorflow.org/guide/function#creating_tfvariables\ + \ and https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Optimizer,\ + \ but neither of them shows examples where the variable is not explicitly defined.\n\ + The only conclusion that I can come to is that one of my commands (most likely\ + \ optimizer.apply_gradients) creates a variable because of an issue\ + \ in my earlier code. Is that correct?

\n" +- text: "

I am going through TensorFlow Eager Execution from here and find it difficult to understand the\ + \ customizing gradients part.

\n\n
@tfe.custom_gradient\ndef logexp(x):\n\
+    \    e = tf.exp(x)\n    def grad(dy):\n        return dy * (1 - 1/(1 + e))\n \
+    \   return tf.log(1 + e), grad\n
\n\n

First, it is difficult to\ + \ make sense what does dy do in the gradient function.

\n\n

When I read the\ + \ implementation of tf.contrib.eager.custom_gradient.\nI can't really make sense\ + \ the working mechanism behind tape. Following is the code I borrow from the implementation\ + \ of tf.contrib.eager.custom_gradient. Can anybody explain what does tape do here?

\n\ + \n
from tensorflow.python.eager import tape\nfrom tensorflow.python.ops\
+    \ import array_ops\nfrom tensorflow.python.ops import gen_array_ops\nfrom tensorflow.python.util\
+    \ import nest\nfrom tensorflow.python.framework import ops as tf_ops\n\ndef my_custom_gradient(f):\n\
+    \    def decorated(*args, **kwargs):\n        for x in args:\n            print('args\
+    \ {0}'.format(x))\n        input_tensors = [tf_ops.convert_to_tensor(x) for x\
+    \ in args]\n\n        with tape.stop_recording():\n            result, grad_fn\
+    \ = f(*args, **kwargs)\n            flat_result = nest.flatten(result)\n     \
+    \       flat_result = [gen_array_ops.identity(x) for x in flat_result]\n\n   \
+    \     def actual_grad_fn(*outputs):\n            print(*outputs)\n           \
+    \ return nest.flatten(grad_fn(*outputs))\n\n       tape.record_operation(\n  \
+    \          f.__name__, # the name of f, in this case logexp\n            flat_result,\n\
+    \            input_tensors,\n            actual_grad_fn) # backward_function\n\
+    \       flat_result = list(flat_result)\n       return nest.pack_sequence_as(result,\
+    \ flat_result)\nreturn decorated \n
\n\n

Even though I found the\ + \ implementation of tape from here. But I can't really get much out of it\ + \ due the poor documentation.

\n" +- text: "

I want to extract signals from time series data for machine learning using\ + \ tensorflow. I got this error when I try to run the program.

\n
\n\ +

/Users/renzha/Library/Application Support/JetBrains/PyCharmCE2021.1/scratches/pywavelet.py:38:\ + \ DeprecationWarning: np.float is a deprecated alias for the builtin\ + \ float. To silence this warning, use float by itself.\ + \ Doing this will not modify any behavior and is safe. If you specifically wanted\ + \ the numpy scalar type, use np.float64 here.\nDeprecated in NumPy\ + \ 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n\ + X = np.asarray(X).astype(np.float)\nTraceback (most recent call last):\nFile "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/core/series.py",\ + \ line 129, in wrapper\nraise TypeError(f"cannot convert the series to {converter}")\n\ + TypeError: cannot convert the series to <class 'float'>\nThe above exception\ + \ was the direct cause of the following exception:\nTraceback (most recent call\ + \ last):\nFile "/Users/renzha/Library/Application Support/JetBrains/PyCharmCE2021.1/scratches/pywavelet.py",\ + \ line 38, in \nX = np.asarray(X).astype(np.float)\nValueError: setting an array\ + \ element with a sequence.

\n
\n

However, if I use X =\ + \ np.array(X), then the error will be

\n
\n

ValueError:\ + \ Failed to convert a NumPy array to a Tensor (Unsupported object type Series).

\n\ +
\n

I have tried tf.convert_to_tensor(X), but it will\ + \ return the same error.

\n

The code is here:

\n
HBT_data =\
+    \ []\nfor i in range (0,10):\n    files = glob.glob('/Users/renzha/Documents/work/preparing\
+    \ papers/non-contact HCI/s letter' + str(i) +'/*')\n\n    for file in files:\n\
+    \        names = ['time', 'signal']\n        data = pd.read_csv (file, names=names)\n\
+    \        data = data.drop (data.index [0])\n        data = data.dropna (axis=0,\
+    \ how='any')\n        x = data.iloc[:,1]\n        filename = os.path.basename\
+    \ (file)\n        label = os.path.splitext(filename)[0]\n        labeledArray\
+    \ = [label, x]\n        HBT_data.append(labeledArray)\n\nHBT_data = pd.DataFrame(HBT_data,\
+    \ dtype=object)\ny = HBT_data.iloc[:, 0]\ny = np.asarray(y)\nX = HBT_data.iloc[:,\
+    \ 1:]\nX = np.asarray(X).astype(np.float) # tried but did not work\nX = tf.convert_to_tensor\
+    \ (X) # tried but did not work\n\nX_train, X_validation, Y_train, Y_validation\
+    \ = train_test_split(X, y, test_size=0.2, random_state=8)\n\nX_train = X_train.reshape((X_train.shape[0],\
+    \ X_train.shape[1], 1))\nX_test = X_validation.reshape((X_validation.shape[0],\
+    \ X_validation.shape[1], 1))\nnum_classes = len(np.unique(Y_train))\n\nidx = np.random.permutation(len(X_train))\n\
+    x_train = X_train[idx]\ny_train = X_train[idx]\n\nY_train[Y_train == -1] = 0\n\
+    Y_validation[Y_validation == -1] = 0\n\n\ndef make_model(input_shape):\n    input_layer\
+    \ = keras.layers.Input(input_shape)\n\n    conv1 = keras.layers.Conv1D(filters=64,\
+    \ kernel_size=3, padding="same")(input_layer)\n    conv1 = keras.layers.BatchNormalization()(conv1)\n\
+    \    conv1 = keras.layers.ReLU()(conv1)\n\n    conv2 = keras.layers.Conv1D(filters=64,\
+    \ kernel_size=3, padding="same")(conv1)\n    conv2 = keras.layers.BatchNormalization()(conv2)\n\
+    \    conv2 = keras.layers.ReLU()(conv2)\n\n    conv3 = keras.layers.Conv1D(filters=64,\
+    \ kernel_size=3, padding="same")(conv2)\n    conv3 = keras.layers.BatchNormalization()(conv3)\n\
+    \    conv3 = keras.layers.ReLU()(conv3)\n\n    gap = keras.layers.GlobalAveragePooling1D()(conv3)\n\
+    \n    output_layer = keras.layers.Dense(num_classes, activation="softmax")(gap)\n\
+    \n    return keras.models.Model(inputs=input_layer, outputs=output_layer)\n\n\n\
+    model = make_model(input_shape=X_train.shape[1:])\nkeras.utils.plot_model(model,\
+    \ show_shapes=True)\n\nepochs = 500\nbatch_size = 32\n\ncallbacks = [\n    keras.callbacks.ModelCheckpoint(\n\
+    \        "best_model.h5", save_best_only=True, monitor="val_loss"\n\
+    \    ),\n    keras.callbacks.ReduceLROnPlateau(\n        monitor="val_loss",\
+    \ factor=0.5, patience=20, min_lr=0.0001\n    ),\n    keras.callbacks.EarlyStopping(monitor="val_loss",\
+    \ patience=50, verbose=1),\n]\nmodel.compile(\n    optimizer="adam",\n\
+    \    loss="sparse_categorical_crossentropy",\n    metrics=["sparse_categorical_accuracy"],\n\
+    )\nhistory = model.fit(\n    X_train,\n    Y_train,\n    batch_size=batch_size,\n\
+    \    epochs=epochs,\n    callbacks=callbacks,\n    validation_split=0.2,\n   \
+    \ verbose=1,\n)\n\nmodel = keras.models.load_model("best_model.h5")\n\
+    \ntest_loss, test_acc = model.evaluate(X_validation, Y_validation)\n\nprint("Test\
+    \ accuracy", test_acc)\nprint("Test loss", test_loss)```\n\n
\n" pipeline_tag: text-classification inference: true -base_model: sentence-transformers/all-MiniLM-L6-v2 +base_model: sentence-transformers/all-mpnet-base-v2 model-index: -- name: SetFit with sentence-transformers/all-MiniLM-L6-v2 +- name: SetFit with sentence-transformers/all-mpnet-base-v2 results: - task: type: text-classification @@ -202,22 +257,22 @@ model-index: split: test metrics: - type: accuracy - value: 0.71 + value: 0.6966292134831461 name: Accuracy - type: precision - value: 0.7100840336134453 + value: 0.7056921772312911 name: Precision - type: recall - value: 0.71 + value: 0.6966292134831461 name: Recall - type: f1 - value: 0.70997099709971 + value: 0.6938491833661531 name: F1 --- -# SetFit with sentence-transformers/all-MiniLM-L6-v2 +# SetFit with sentence-transformers/all-mpnet-base-v2 -This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification. +This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: @@ -228,9 +283,9 @@ The model has been trained using an efficient few-shot learning technique that i ### Model Description - **Model Type:** SetFit -- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) +- **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance -- **Maximum Sequence Length:** 256 tokens +- **Maximum Sequence Length:** 384 tokens - **Number of Classes:** 2 classes @@ -243,17 +298,17 @@ The model has been trained using an efficient few-shot learning technique that i - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels -| Label | Examples | -|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| 0 | | -| 1 | | +| Label | Examples | +|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| 0 | | +| 1 | | ## Evaluation ### Metrics | Label | Accuracy | Precision | Recall | F1 | |:--------|:---------|:----------|:-------|:-------| -| **all** | 0.71 | 0.7101 | 0.71 | 0.7100 | +| **all** | 0.6966 | 0.7057 | 0.6966 | 0.6938 | ## Uses @@ -273,7 +328,47 @@ from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("sharukat/sbert-questionclassifier") # Run inference -preds = model("

Where one can find the github source code for tf.quantization.fake_quant_with_min_max_args. Checking the TF API documentation, there is no link to the github source file, and I could not find one on github.

+preds = model("

New to TensorFlow, so apologies for newbie question.

+ +

Following this tutorial but instead of using image data I am using numerical data.

+ +

Load the dataset:

+ +
train_dataset_url = \"xxx.csv\"
+train_dataset_fp = tf.keras.utils.get_file(
+  fname=os.path.basename(train_dataset_url),
+  origin=train_dataset_url)
+
+ +

Make training dataset:

+ +
batch_size = 32
+
+train_dataset = tf.contrib.data.make_csv_dataset(
+    train_dataset_fp,
+    batch_size, 
+    column_names=column_names,
+    label_name=label_name,
+    num_epochs=1)
+
+ +

Train classified model using:

+ +

model = tf.keras.Sequential([ + tf.keras.layers.Dense(10, activation=tf.nn.relu, input_shape=(1,)), + tf.keras.layers.Dense(10, activation=tf.nn.relu), + tf.keras.layers.Dense(4) +])

+ +

But when I \"test\" the model with the same inputs:

+ +

predictions = model(features)

+ +

I receive the error:

+ +

InvalidArgumentError: cannot compute MatMul as input #0(zero-based) was expected to be a float tensor but is a int32 tensor [Op:MatMul]

+ +

It's possible I have missed something fundamental. I feel like I need to specify a type somewhere.

") ``` @@ -306,7 +401,7 @@ preds = model("

Where one can find the github source code for tf.quantiz ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:-----| -| Word count | 15 | 336.203 | 3755 | +| Word count | 15 | 336.765 | 3755 | | Label | Training Sample Count | |:------|:----------------------| @@ -330,119 +425,22 @@ preds = model("

Where one can find the github source code for tf.quantiz - max_length: 256 - seed: 42 - eval_max_steps: -1 -- load_best_model_at_end: False +- load_best_model_at_end: True ### Training Results -| Epoch | Step | Training Loss | Validation Loss | -|:------:|:----:|:-------------:|:---------------:| -| 0.0004 | 1 | 0.26 | - | -| 0.02 | 50 | 0.2486 | - | -| 0.04 | 100 | 0.2383 | - | -| 0.06 | 150 | 0.309 | - | -| 0.08 | 200 | 0.2551 | - | -| 0.1 | 250 | 0.2675 | - | -| 0.12 | 300 | 0.2344 | - | -| 0.14 | 350 | 0.2686 | - | -| 0.16 | 400 | 0.2447 | - | -| 0.18 | 450 | 0.2317 | - | -| 0.2 | 500 | 0.2233 | - | -| 0.22 | 550 | 0.1999 | - | -| 0.24 | 600 | 0.2443 | - | -| 0.26 | 650 | 0.1667 | - | -| 0.28 | 700 | 0.2975 | - | -| 0.3 | 750 | 0.0902 | - | -| 0.32 | 800 | 0.1965 | - | -| 0.34 | 850 | 0.1571 | - | -| 0.36 | 900 | 0.1247 | - | -| 0.38 | 950 | 0.0494 | - | -| 0.4 | 1000 | 0.1222 | - | -| 0.42 | 1050 | 0.0828 | - | -| 0.44 | 1100 | 0.0393 | - | -| 0.46 | 1150 | 0.0104 | - | -| 0.48 | 1200 | 0.0143 | - | -| 0.5 | 1250 | 0.0505 | - | -| 0.52 | 1300 | 0.0053 | - | -| 0.54 | 1350 | 0.0337 | - | -| 0.56 | 1400 | 0.0013 | - | -| 0.58 | 1450 | 0.0061 | - | -| 0.6 | 1500 | 0.0519 | - | -| 0.62 | 1550 | 0.0068 | - | -| 0.64 | 1600 | 0.001 | - | -| 0.66 | 1650 | 0.0004 | - | -| 0.68 | 1700 | 0.0008 | - | -| 0.7 | 1750 | 0.0018 | - | -| 0.72 | 1800 | 0.0018 | - | -| 0.74 | 1850 | 0.0022 | - | -| 0.76 | 1900 | 0.0005 | - | -| 0.78 | 1950 | 0.0008 | - | -| 0.8 | 2000 | 0.0005 | - | -| 0.82 | 2050 | 0.0003 | - | -| 0.84 | 2100 | 0.0004 | - | -| 0.86 | 2150 | 0.0002 | - | -| 0.88 | 2200 | 0.0003 | - | -| 0.9 | 2250 | 0.0001 | - | -| 0.92 | 2300 | 0.0001 | - | -| 0.94 | 2350 | 0.0002 | - | -| 0.96 | 2400 | 0.0005 | - | -| 0.98 | 2450 | 0.0002 | - | -| 1.0 | 2500 | 0.0002 | - | -| 1.02 | 2550 | 0.0001 | - | -| 1.04 | 2600 | 0.0001 | - | -| 1.06 | 2650 | 0.0003 | - | -| 1.08 | 2700 | 0.0002 | - | -| 1.1 | 2750 | 0.0002 | - | -| 1.12 | 2800 | 0.0001 | - | -| 1.1400 | 2850 | 0.0001 | - | -| 1.16 | 2900 | 0.0002 | - | -| 1.18 | 2950 | 0.0594 | - | -| 1.2 | 3000 | 0.0002 | - | -| 1.22 | 3050 | 0.0002 | - | -| 1.24 | 3100 | 0.0001 | - | -| 1.26 | 3150 | 0.0262 | - | -| 1.28 | 3200 | 0.0001 | - | -| 1.3 | 3250 | 0.0001 | - | -| 1.32 | 3300 | 0.0001 | - | -| 1.34 | 3350 | 0.0001 | - | -| 1.3600 | 3400 | 0.0001 | - | -| 1.38 | 3450 | 0.0002 | - | -| 1.4 | 3500 | 0.0 | - | -| 1.42 | 3550 | 0.0001 | - | -| 1.44 | 3600 | 0.0001 | - | -| 1.46 | 3650 | 0.0001 | - | -| 1.48 | 3700 | 0.0001 | - | -| 1.5 | 3750 | 0.0001 | - | -| 1.52 | 3800 | 0.0001 | - | -| 1.54 | 3850 | 0.0001 | - | -| 1.56 | 3900 | 0.0001 | - | -| 1.58 | 3950 | 0.0001 | - | -| 1.6 | 4000 | 0.0001 | - | -| 1.62 | 4050 | 0.0002 | - | -| 1.6400 | 4100 | 0.0044 | - | -| 1.6600 | 4150 | 0.0001 | - | -| 1.6800 | 4200 | 0.0002 | - | -| 1.7 | 4250 | 0.0001 | - | -| 1.72 | 4300 | 0.0001 | - | -| 1.74 | 4350 | 0.0001 | - | -| 1.76 | 4400 | 0.0001 | - | -| 1.78 | 4450 | 0.0 | - | -| 1.8 | 4500 | 0.0001 | - | -| 1.8200 | 4550 | 0.0001 | - | -| 1.8400 | 4600 | 0.0 | - | -| 1.8600 | 4650 | 0.061 | - | -| 1.88 | 4700 | 0.0002 | - | -| 1.9 | 4750 | 0.0001 | - | -| 1.92 | 4800 | 0.0001 | - | -| 1.94 | 4850 | 0.0001 | - | -| 1.96 | 4900 | 0.0001 | - | -| 1.98 | 4950 | 0.0001 | - | -| 2.0 | 5000 | 0.0001 | - | +| Epoch | Step | Training Loss | Validation Loss | +|:-------:|:--------:|:-------------:|:---------------:| +| 0.0004 | 1 | 0.3216 | - | +| **1.0** | **2500** | **0.0001** | **0.3943** | +| 2.0 | 5000 | 0.0 | 0.3993 | +* The bold row denotes the saved checkpoint. ### Framework Versions -- Python: 3.10.12 +- Python: 3.10.13 - SetFit: 1.0.3 -- Sentence Transformers: 2.4.0 -- Transformers: 4.37.2 -- PyTorch: 2.1.0+cu121 +- Sentence Transformers: 2.5.0 +- Transformers: 4.38.1 +- PyTorch: 2.1.2 - Datasets: 2.17.1 - Tokenizers: 0.15.2