images = images / 255.0
model = tf.keras.Sequential([
keras.layers.InputLayer(input_shape=(227, 227, 3)),
keras.layers.Conv2D(96, [7, 7], [4, 4], data_format='channels_last'),
keras.layers.MaxPooling2D(pool_size=(3, 2), padding="same"),
keras.layers.Conv2D(2566, [5, 5], [1, 1], data_format='channels_last'),
keras.layers.MaxPooling2D(pool_size=(3, 2), padding='same'),
keras.layers.Conv2D(384, [3, 3], [1, 1], data_format='channels_last'),
keras.layers.MaxPooling2D(pool_size=(3, 2), padding='same'),
keras.layers.Flatten(),
keras.layers.Dense(512),
keras.layers.Dropout(1 - pkeep),
keras.layers.Dense(512),
keras.layers.Dropout(1 - pkeep),
])
prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude
batch_size = 128
epochs = 2
# Compute end step to finish pruning after 2 epochs.
validation_split = 0.1 # 10% of training set will be used for validation set.
num_images = int(images.shape[0]) * (1 - validation_split)
end_step = np.ceil(num_images / batch_size).astype(np.int32) * epochs
# Define model for pruning.
pruning_params = {
'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(initial_sparsity=0.50,
final_sparsity=0.80,
begin_step=0,
end_step=end_step)
}
model_for_pruning = prune_low_magnitude(model, **pruning_params)
# `prune_low_magnitude` requires a recompile.
model_for_pruning.compile(optimizer='Adadelta',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model_for_pruning.summary()
callbacks = [
tfmot.sparsity.keras.UpdatePruningStep(),
]
model_for_pruning.fit(images,
batch_size=batch_size, epochs=epochs, validation_split=validation_split, steps_per_epoch=100,
callbacks=callbacks)
with tf.variable_scope('output') as scope:
weights = tf.Variable(tf.random_normal([512, nlabels], mean=0.0, stddev=0.01), name='weights')
biases = tf.Variable(tf.constant(0.0, shape=[nlabels], dtype=tf.float32), name='biases')
output = tf.add(tf.matmul(model.output, weights), biases, name=scope.name)
return output
How do I fix images so it's not a symbolic tensor? Images is a tensor input for the preprocessed image size, with the input images being from the Adience benchmark.
The error says I can't use validation_split with a symbolic tensor. I tried converting the symbolic tensor to a variable and a numpy array, but the program still throws that the input images is a symbolic tensor.
Python 3.7
Keras 2.2.3
Tensorflow 1.14.0
