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Chapter 5
predictions = tf.keras.layers.Dense(LABEL_DIMENSIONS,
activation='softmax')(x)
model = tf.keras.Model(inputs=inputs, outputs=predictions)
model.summary()
Compile it:
optimizer = tf.keras.optimizers.SGD()
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
Define a strategy, which is None for now because we run on CPUs first:
strategy = None
#strategy = tf.distribute.MirroredStrategy()
config = tf.estimator.RunConfig(train_distribute=strategy)
Now let's convert the tf.keras model into a convenient Estimator:
estimator = tf.keras.estimator.model_to_estimator(model,
config=config)
The next step is to define input functions for training and for testing, which is pretty
easy if we use tf.data:
def input_fn(images, labels, epochs, batch_size):
# Convert the inputs to a Dataset
dataset = tf.data.Dataset.from_tensor_slices((images, labels))
# Shuffle, repeat, and batch the examples.
SHUFFLE_SIZE = 5000
dataset = dataset.shuffle(SHUFFLE_SIZE).repeat(epochs).
batch(batch_size)
dataset = dataset.prefetch(None)
# Return the dataset.
return dataset
We are ready to start the training with the following code:
BATCH_SIZE = 512
EPOCHS = 50
estimator_train_result = estimator.train(input_fn=lambda:input_
fn(train_images, train_labels,
epochs=EPOCHS,
batch_size=BATCH_SIZE))
print(estimator_train_result)
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