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import tensorflow as tf
import numpy as np
import os
# Load the MNIST dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# Normalize and reshape the data
x_train, x_test = x_train.astype(np.float32) / 255.0, x_test.astype(np.float32) / 255.0
x_train = x_train.reshape(-1, 784)
x_test = x_test.reshape(-1, 784)
# Convert labels to one-hot encoding
y_train = tf.one_hot(y_train, depth=10)
y_test = tf.one_hot(y_test, depth=10)
# Create TensorFlow datasets for better performance
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(60000).batch(100)
test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(100)
# Network parameters
input_layer_size = 784
hidden_layer_one = 256
hidden_layer_two = 256
number_classes = 10
weights = {
'w1': tf.Variable(tf.random.normal([input_layer_size, hidden_layer_one], dtype=tf.float32)),
'w2': tf.Variable(tf.random.normal([hidden_layer_one, hidden_layer_two], dtype=tf.float32)),
'w_out': tf.Variable(tf.random.normal([hidden_layer_two, number_classes], dtype=tf.float32))
}
biases = {
'b1': tf.Variable(tf.random.normal([hidden_layer_one], dtype=tf.float32)),
'b2': tf.Variable(tf.random.normal([hidden_layer_two], dtype=tf.float32)),
'b_out': tf.Variable(tf.random.normal([number_classes], dtype=tf.float32))
}
# Network architecture
def feedforward_network(x):
layer_1 = tf.nn.relu(tf.add(tf.matmul(x, weights['w1']), biases['b1']))
layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, weights['w2']), biases['b2']))
output_layer = tf.matmul(layer_2, weights['w_out']) + biases['b_out']
return output_layer
# Training hyperparameters
epochs = 45
learning_rate = 0.001
job_dir = 'mnist_model'
# Training loop
for epoch in range(epochs):
for step, (batch_x, batch_y) in enumerate(train_dataset):
with tf.GradientTape() as tape:
logits = feedforward_network(batch_x)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=batch_y))
gradients = tape.gradient(loss, list(weights.values()) + list(biases.values()))
optimizer = tf.optimizers.Adam(learning_rate=learning_rate)
optimizer.apply_gradients(zip(gradients, list(weights.values()) + list(biases.values())))
# Print loss every epoch
print(f"Epoch {epoch+1}, Loss: {loss.numpy()}")
# Evaluation
def evaluate(dataset):
correct_predictions = 0
total_predictions = 0
for batch_x, batch_y in dataset:
logits = feedforward_network(batch_x)
correct_predictions += tf.reduce_sum(tf.cast(tf.equal(tf.argmax(logits, 1), tf.argmax(batch_y, 1)), tf.int32)).numpy()
total_predictions += batch_x.shape[0]
return correct_predictions / total_predictions
accuracy = evaluate(test_dataset)
print(f"Test accuracy: {accuracy}")
@tf.function(input_signature=[tf.TensorSpec(shape=[None, 784], dtype=tf.float32)])
def serve_model(x):
return {'output': feedforward_network(x)}
# Save the model
class MyModel(tf.Module):
def __init__(self, weights, biases):
super(MyModel, self).__init__()
self.weights = weights
self.biases = biases
self.serve_model = serve_model
model = MyModel(weights, biases)
save_path = os.path.join(job_dir, 'model')
tf.saved_model.save(model, save_path) |