def testPruneStopAndRestartOnModel(self, save_restore_fn):
params = {
'pruning_schedule':
pruning_schedule.PolynomialDecay(0.2, 0.6, 0, 4, 3, 1),
'block_size': (1, 1),
'block_pooling_type':
'AVG'
}
model = prune.prune_low_magnitude(
keras_test_utils.build_simple_dense_model(), **params)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
# Model hasn't been trained yet. Sparsity 0.0
test_utils.assert_model_sparsity(self, 0.0, model)
model.fit(np.random.rand(20, 10),
np_utils.to_categorical(np.random.randint(5, size=(20, 1)),
5),
batch_size=20,
callbacks=[pruning_callbacks.UpdatePruningStep()])
# Training has run only 1 step. Sparsity 0.2 (initial_sparsity)
test_utils.assert_model_sparsity(self, 0.2, model)
model = save_restore_fn(model)
model.fit(np.random.rand(20, 10),
np_utils.to_categorical(np.random.randint(5, size=(20, 1)),
5),
batch_size=20,
epochs=3,
callbacks=[pruning_callbacks.UpdatePruningStep()])
# Training has run all 4 steps. Sparsity 0.6 (final_sparsity)
test_utils.assert_model_sparsity(self, 0.6, model)
self._check_strip_pruning_matches_original(model, 0.6)
def testPrunesModel_CustomTrainingLoop_ReachesTargetSparsity(self):
pruned_model = prune.prune_low_magnitude(
keras_test_utils.build_simple_dense_model())
batch_size = 20
x_train = np.random.rand(20, 10)
y_train = keras.utils.to_categorical(
np.random.randint(5, size=(batch_size, 1)), 5)
loss = keras.losses.categorical_crossentropy
optimizer = keras.optimizers.SGD()
unused_arg = -1
step_callback = pruning_callbacks.UpdatePruningStep()
step_callback.set_model(pruned_model)
pruned_model.optimizer = optimizer
step_callback.on_train_begin()
# 2 epochs
for _ in range(2):
step_callback.on_train_batch_begin(batch=unused_arg)
inp = np.reshape(x_train,
[batch_size, 10]) # original shape: from [10].
with tf.GradientTape() as tape:
logits = pruned_model(inp, training=True)
loss_value = loss(y_train, logits)
grads = tape.gradient(loss_value,
pruned_model.trainable_variables)
optimizer.apply_gradients(
zip(grads, pruned_model.trainable_variables))
step_callback.on_epoch_end(batch=unused_arg)
test_utils.assert_model_sparsity(self, 0.5, pruned_model)
def train(model, x_train, y_train, x_test, y_test):
model.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer='adam',
metrics=['accuracy'])
# Print the model summary.
model.summary()
# Add a pruning step callback to peg the pruning step to the optimizer's
# step. Also add a callback to add pruning summaries to tensorboard
callbacks = [
pruning_callbacks.UpdatePruningStep(),
pruning_callbacks.PruningSummaries(log_dir='/tmp/logs')
]
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
callbacks=callbacks,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
model = prune.strip_pruning(model)
return model
def testPrunesMnist_ReachesTargetSparsity(self, model_type):
model = test_utils.build_mnist_model(model_type, self.params)
if model_type == 'layer_list':
model = keras.Sequential(
prune.prune_low_magnitude(model, **self.params))
elif model_type in ['sequential', 'functional']:
model = prune.prune_low_magnitude(model, **self.params)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
test_utils.assert_model_sparsity(self,
0.0,
model,
rtol=1e-4,
atol=1e-4)
model.fit(np.random.rand(32, 28, 28, 1),
keras.utils.to_categorical(
np.random.randint(10, size=(32, 1)), 10),
callbacks=[pruning_callbacks.UpdatePruningStep()])
test_utils.assert_model_sparsity(self,
0.5,
model,
rtol=1e-4,
atol=1e-4)
self._check_strip_pruning_matches_original(model, 0.5)
def testPruneCheckpoints_CheckpointsNotSparse(self):
is_model_sparsity_not_list = []
# Run multiple times since problem doesn't always happen.
for _ in range(3):
model = keras_test_utils.build_simple_dense_model()
pruned_model = prune.prune_low_magnitude(model, **self.params)
checkpoint_dir = tempfile.mkdtemp()
checkpoint_path = checkpoint_dir + '/weights.{epoch:02d}.tf'
callbacks = [
pruning_callbacks.UpdatePruningStep(),
tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_path, save_weights_only=True, save_freq=1)
]
# Train one step. Sparsity reaches final sparsity.
self._train_model(pruned_model, epochs=1, callbacks=callbacks)
test_utils.assert_model_sparsity(self, 0.5, pruned_model)
latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
same_architecture_model = keras_test_utils.build_simple_dense_model()
pruned_model = prune.prune_low_magnitude(same_architecture_model,
**self.params)
# Sanity check.
test_utils.assert_model_sparsity(self, 0, pruned_model)
pruned_model.load_weights(latest_checkpoint)
is_model_sparsity_not_list.append(
test_utils.is_model_sparsity_not(0.5, pruned_model))
self.assertTrue(any(is_model_sparsity_not_list))
def testPrunesSimpleDenseModel(self, distribution):
with distribution.scope():
model = prune.prune_low_magnitude(
keras_test_utils.build_simple_dense_model(), **self.params)
model.compile(
loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
# Model hasn't been trained yet. Sparsity 0.0
test_utils.assert_model_sparsity(self, 0.0, model)
# Simple unpruned model. No sparsity.
model.fit(
np.random.rand(20, 10),
keras.utils.np_utils.to_categorical(
np.random.randint(5, size=(20, 1)), 5),
epochs=2,
callbacks=[pruning_callbacks.UpdatePruningStep()],
batch_size=20)
model.predict(np.random.rand(20, 10))
test_utils.assert_model_sparsity(self, 0.5, model)
_, keras_file = tempfile.mkstemp('.h5')
keras.models.save_model(model, keras_file)
with prune.prune_scope():
loaded_model = keras.models.load_model(keras_file)
test_utils.assert_model_sparsity(self, 0.5, loaded_model)
def testRNNLayersWithRNNCellParams(self):
model = keras.Sequential()
model.add(
prune.prune_low_magnitude(keras.layers.RNN([
layers.LSTMCell(10),
layers.GRUCell(10),
layers.PeepholeLSTMCell(10),
layers.SimpleRNNCell(10)
]),
input_shape=(3, 4),
**self.params))
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
test_utils.assert_model_sparsity(self, 0.0, model)
model.fit(np.random.randn(
*self._batch(model.input.get_shape().as_list(), 32)),
np.random.randn(
*self._batch(model.output.get_shape().as_list(), 32)),
callbacks=[pruning_callbacks.UpdatePruningStep()])
test_utils.assert_model_sparsity(self, 0.5, model)
self._check_strip_pruning_matches_original(model, 0.5)
def testPrunesPreviouslyUnprunedModel(self):
model = keras_test_utils.build_simple_dense_model()
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
# Simple unpruned model. No sparsity.
model.fit(np.random.rand(20, 10),
np_utils.to_categorical(np.random.randint(5, size=(20, 1)),
5),
epochs=2,
batch_size=20)
test_utils.assert_model_sparsity(self, 0.0, model)
# Apply pruning to model.
model = prune.prune_low_magnitude(model, **self.params)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
# Since newly compiled, iterations starts from 0.
model.fit(np.random.rand(20, 10),
np_utils.to_categorical(np.random.randint(5, size=(20, 1)),
5),
batch_size=20,
callbacks=[pruning_callbacks.UpdatePruningStep()])
test_utils.assert_model_sparsity(self, 0.5, model)
self._check_strip_pruning_matches_original(model, 0.5)
def train_and_save(models, x_train, y_train, x_test, y_test):
for model in models:
model.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer='adam',
metrics=['accuracy'])
# Print the model summary.
model.summary()
# Add a pruning step callback to peg the pruning step to the optimizer's
# step. Also add a callback to add pruning summaries to tensorboard
callbacks = [
pruning_callbacks.UpdatePruningStep(),
pruning_callbacks.PruningSummaries(log_dir=FLAGS.output_dir)
]
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
callbacks=callbacks,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
# Export and import the model. Check that accuracy persists.
_, keras_file = tempfile.mkstemp('.h5')
print('Saving model to: ', keras_file)
keras.models.save_model(model, keras_file)
with prune.prune_scope():
loaded_model = keras.models.load_model(keras_file)
score = loaded_model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
def testMbyNSparsityPruning_SupportedLayers(self,
layer_type,
layer_arg,
input_shape,
m_by_n=(2, 4),
sparsity_ratio=0.50):
"""Check that we prune supported layers with m by n sparsity."""
self.params.update({'sparsity_m_by_n': m_by_n})
model = keras.Sequential()
model.add(
prune.prune_low_magnitude(layer_type(*layer_arg),
input_shape=input_shape,
**self.params))
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
test_utils.assert_model_sparsity(self, 0.0, model)
model.fit(np.random.randn(
*self._batch(model.input.get_shape().as_list(), 32)),
np.random.randn(
*self._batch(model.output.get_shape().as_list(), 32)),
callbacks=[pruning_callbacks.UpdatePruningStep()])
test_utils.assert_model_sparsity_m_by_n(self, model, m_by_n)
self._check_strip_pruning_matches_original(model, sparsity_ratio)
def _train_model(model,
epochs=1,
x_train=None,
y_train=None,
callbacks=None):
if x_train is None:
x_train = np.random.rand(20, 10),
if y_train is None:
y_train = keras.utils.to_categorical(
np.random.randint(5, size=(20, 1)), 5)
if model.optimizer is None:
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
if callbacks is None:
callbacks = []
if PruneIntegrationTest._is_pruned(model):
callbacks = [pruning_callbacks.UpdatePruningStep()]
model.fit(x_train,
y_train,
epochs=epochs,
batch_size=20,
callbacks=callbacks)
def train_and_save(models, x_train, y_train, x_test, y_test):
for model in models:
model.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer='adam',
metrics=['accuracy'])
# Print the model summary.
model.summary()
# Add a pruning step callback to peg the pruning step to the optimizer's
# step. Also add a callback to add pruning summaries to tensorboard
if not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
# print(FLAGS.output_dir)
callbacks = [
pruning_callbacks.UpdatePruningStep(),
pruning_callbacks.PruningSummaries(log_dir=FLAGS.output_dir)
]
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
callbacks=callbacks,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
# Export and import the model. Check that accuracy persists.
saved_model_dir = '/tmp/saved_model'
if not os.path.exists(saved_model_dir):
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
callbacks=callbacks,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
os.makedirs(saved_model_dir)
print('Saving model to: ', saved_model_dir)
tf.keras.models.save_model(model,
saved_model_dir,
save_format='tf')
print('Loading model from: ', saved_model_dir)
loaded_model = tf.keras.models.load_model(saved_model_dir)
score = loaded_model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
def testDeepLayerUpdatePruningSteps(self):
pruned_model, x_train, y_train = self._pruned_model_setup(has_deep_layer=True)
pruned_model.fit(
x_train,
y_train,
batch_size=self._BATCH_SIZE,
epochs=3,
callbacks=[
pruning_callbacks.UpdatePruningStep()
])
self.assertEqual(2, pruned_model.layers[-1]._layers[0].pruning_step)
def fit(self, X_train, y_train):
with self.graph.as_default(), self.session.as_default():
callbacks = None
if self.prune_params:
callbacks = [
pruning_callbacks.UpdatePruningStep(),
]
self.model.fit(X_train,
y_train,
epochs=self.train_epochs,
batch_size=self.batch_size,
callbacks=callbacks)
def fitModel(model, train_data, val_data, test_data, stepsPerEpoch,
evalStepsPerEpoch):
"""Runs Keras fit and saves model.
Arguments:
STRATEGY: Mirrored strategy
models: list of models to train
train_data: training data
val_data: validation data
Returns:
None
"""
if not os.path.exists(FLAGS.outdir + '/%s/' % model.name):
os.system('mkdir ' + FLAGS.outdir + '/%s/' % model.name)
callbacks = getCallbacks(FLAGS.outdir + '/%s/' % model.name)
if FLAGS.prune == True:
callbacks.append(pruning_callbacks.UpdatePruningStep())
start = time.time()
LOSS = tf.keras.losses.CategoricalCrossentropy()
OPTIMIZER = Adam(learning_rate=FLAGS.lr,
beta_1=FLAGS.beta_1,
beta_2=FLAGS.beta_2,
epsilon=FLAGS.epsilon,
amsgrad=True)
model.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=["accuracy"])
model.summary()
history = model.fit(train_data,
epochs=FLAGS.epochs,
validation_data=val_data,
callbacks=callbacks,
verbose=1)
model.load_weights(FLAGS.outdir + '/%s/weights_best.h5' % model.name)
history_dict = history.history
pd.DataFrame.from_dict(history.history).to_csv(
FLAGS.outdir + '/%s/history_dict.csv' % model.name, index=False)
test_score = model.evaluate(test_data)
print("Done training model {}".format(model.name))
print('\n Test loss:', test_score[0])
print('\n Test accuracy:', test_score[1])
np.savez(FLAGS.outdir + '/%s/scores' % model.name, test_score)
if FLAGS.prune == True:
model_stripped = strip_pruning(model)
model_stripped.save(FLAGS.outdir + '/%s/%s.h5' %
(model.name, model.name))
else:
model.save(FLAGS.outdir + '/%s/%s.h5' % (model.name, model.name))
end = time.time()
print('\n It took {} minutes to train!\n'.format((end - start) / 60.))
def _prune_model(original_model):
"""Apply the pruning wrapper, compile and train the model."""
prune_epoch = 1
pruning_params = {
'pruning_schedule':
pruning_schedule.ConstantSparsity(0.50, begin_step=0, frequency=10)
}
pruning_model = prune.prune_low_magnitude(original_model, **pruning_params)
callbacks = [pruning_callbacks.UpdatePruningStep()]
pruning_model = _train_model(pruning_model, callbacks, prune_epoch)
pruning_model_stripped = prune.strip_pruning(pruning_model)
return pruning_model, pruning_model_stripped
def testUpdatesPruningStep(self):
model = prune.prune_low_magnitude(
keras_test_utils.build_simple_dense_model())
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
model.fit(np.random.rand(20, 10),
keras.utils.np_utils.to_categorical(
np.random.randint(5, size=(20, 1)), 5),
batch_size=20,
epochs=3,
callbacks=[pruning_callbacks.UpdatePruningStep()])
self.assertEqual(2, K.get_value(model.layers[0].pruning_step))
self.assertEqual(2, K.get_value(model.layers[1].pruning_step))
def testRNNLayersSingleCell_ReachesTargetSparsity(self, layer_type):
model = keras.Sequential()
model.add(
prune.prune_low_magnitude(
layer_type(10), input_shape=(3, 4), **self.params))
model.compile(
loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
test_utils.assert_model_sparsity(self, 0.0, model)
model.fit(
np.random.randn(*self._batch(model.input.get_shape().as_list(), 32)),
np.random.randn(*self._batch(model.output.get_shape().as_list(), 32)),
callbacks=[pruning_callbacks.UpdatePruningStep()])
test_utils.assert_model_sparsity(self, 0.5, model)
self._check_strip_pruning_matches_original(model, 0.5)
def testUpdatePruningStepsAndLogsSummaries(self):
log_dir = tempfile.mkdtemp()
pruned_model, x_train, y_train = self._pruned_model_setup()
pruned_model.fit(
x_train,
y_train,
batch_size=self._BATCH_SIZE,
epochs=3,
callbacks=[
pruning_callbacks.UpdatePruningStep(),
pruning_callbacks.PruningSummaries(log_dir=log_dir)
])
self.assertEqual(
2, tf.keras.backend.get_value(pruned_model.layers[0].pruning_step))
self.assertEqual(
2, tf.keras.backend.get_value(pruned_model.layers[1].pruning_step))
self._assertLogsExist(log_dir)
def testSparsityPruningMbyN_SupportedSubclassLayers(self):
"""Check subclass layer that is supported for m by n sparsity."""
m_by_n = (2, 4)
self.params.update({'sparsity_m_by_n': m_by_n})
class SubclassLayer(tf.keras.layers.Layer):
def __init__(self):
super(SubclassLayer, self).__init__()
self.conv1 = tf.keras.layers.Conv2D(2,
3,
activation='relu',
padding='same',
input_shape=[7, 7, 3])
self.conv2 = tf.keras.layers.DepthwiseConv2D(3)
self.flatten = keras.layers.Flatten()
self.dense = layers.Dense(10, activation='sigmoid')
def call(self, inputs):
x = self.conv1(inputs)
x = self.conv2(x)
x = self.flatten(x)
x = self.dense(x)
return x
inputs = keras.Input(shape=(7, 7, 3))
outputs = SubclassLayer()(inputs)
model = keras.Model(inputs, outputs)
with self.assertRaises(ValueError):
model = prune.prune_low_magnitude(model, **self.params)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
test_utils.assert_model_sparsity(self, 0.0, model)
model.fit(np.random.randn(
*self._batch(model.input.get_shape().as_list(), 32)),
np.random.randn(
*self._batch(model.output.get_shape().as_list(), 32)),
callbacks=[pruning_callbacks.UpdatePruningStep()])
test_utils.assert_model_sparsity_m_by_n(self, model, m_by_n)
self._check_strip_pruning_matches_original(model, 0.5)
def testPrunesSingleLayer_ReachesTargetSparsity(self, layer_type):
model = keras.Sequential()
args, input_shape = self._get_params_for_layer(layer_type)
if args is None:
return # Test for layer not supported yet.
model.add(prune.prune_low_magnitude(
layer_type(*args), input_shape=input_shape, **self.params))
model.compile(
loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
test_utils.assert_model_sparsity(self, 0.0, model)
model.fit(
np.random.randn(*self._batch(model.input.get_shape().as_list(), 32)),
np.random.randn(*self._batch(model.output.get_shape().as_list(), 32)),
callbacks=[pruning_callbacks.UpdatePruningStep()])
test_utils.assert_model_sparsity(self, 0.5, model)
self._check_strip_pruning_matches_original(model, 0.5)
def train_and_save(model, x_train, y_train, x_test, y_test):
model.compile(
loss="categorical_crossentropy",
optimizer="adam",
metrics=["accuracy"])
# Print the model summary.
model.summary()
# Add a pruning step callback to peg the pruning step to the optimizer's
# step. Also add a callback to add pruning summaries to tensorboard
callbacks = [
pruning_callbacks.UpdatePruningStep(),
#pruning_callbacks.PruningSummaries(log_dir=tempfile.mkdtemp())
pruning_callbacks.PruningSummaries(log_dir="/tmp/mnist_prune")
]
model.fit(
x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
callbacks=callbacks,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])
print_model_sparsity(model)
# Export and import the model. Check that accuracy persists.
_, keras_file = tempfile.mkstemp(".h5")
print("Saving model to: ", keras_file)
save_model(model, keras_file)
print("Reloading model")
with prune.prune_scope():
loaded_model = load_qmodel(keras_file)
score = loaded_model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])
def testPruneRecursivelyReachesTargetSparsity(self):
internal_model = keras.Sequential(
[keras.layers.Dense(10, input_shape=(10, ))])
model = keras.Sequential([
internal_model,
layers.Flatten(),
layers.Dense(1),
])
model.compile(loss='binary_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
test_utils.assert_model_sparsity(self, 0.0, model)
model.fit(np.random.randint(10, size=(32, 10)),
np.random.randint(2, size=(32, 1)),
callbacks=[pruning_callbacks.UpdatePruningStep()])
test_utils.assert_model_sparsity(self, 0.5, model)
input_data = np.random.randint(10, size=(32, 10))
self._check_strip_pruning_matches_original(model, 0.5, input_data)
def testPrunesEmbedding(self):
model = keras.Sequential()
model.add(
prune.prune_low_magnitude(keras.layers.Embedding(input_dim=10,
output_dim=3),
input_shape=(5, ),
**self.params))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
test_utils.assert_model_sparsity(self, 0.0, model)
model.fit(np.random.randint(10, size=(32, 5)),
np.random.randint(2, size=(32, 1)),
callbacks=[pruning_callbacks.UpdatePruningStep()])
test_utils.assert_model_sparsity(self, 0.5, model)
self._check_strip_pruning_matches_original(model, 0.5)
def testUpdatePruningStepsAndLogsSummaries_CustomTrainingLoop(self):
log_dir = tempfile.mkdtemp()
pruned_model, loss, optimizer, x_train, y_train = self._pruned_model_setup(
custom_training_loop=True)
unused_arg = -1
step_callback = pruning_callbacks.UpdatePruningStep()
log_callback = pruning_callbacks.PruningSummaries(log_dir=log_dir)
# TODO(tfmot): we need a separate API for custom training loops
# that doesn't rely on users setting the model and optimizer.
#
# Example is currently based on callbacks.py configure_callbacks
# and model.compile internals.
step_callback.set_model(pruned_model)
log_callback.set_model(pruned_model)
pruned_model.optimizer = optimizer
step_callback.on_train_begin()
for _ in range(3):
log_callback.on_epoch_begin(epoch=unused_arg)
# only one batch given batch_size = 20 and input shape.
step_callback.on_train_batch_begin(batch=unused_arg)
inp = np.reshape(
x_train, [self._BATCH_SIZE, 10]) # original shape: from [10].
with tf.GradientTape() as tape:
logits = pruned_model(inp, training=True)
loss_value = loss(y_train, logits)
grads = tape.gradient(loss_value,
pruned_model.trainable_variables)
optimizer.apply_gradients(
zip(grads, pruned_model.trainable_variables))
step_callback.on_epoch_end(batch=unused_arg)
self.assertEqual(
3, tf.keras.backend.get_value(pruned_model.layers[0].pruning_step))
self.assertEqual(
3, tf.keras.backend.get_value(pruned_model.layers[1].pruning_step))
self._assertLogsExist(log_dir)
def testUpdatePruningStepsAndLogsSummaries(self):
log_dir = tempfile.mkdtemp()
model = prune.prune_low_magnitude(
keras_test_utils.build_simple_dense_model())
model.compile(
loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
model.fit(
np.random.rand(20, 10),
tf.keras.utils.to_categorical(np.random.randint(5, size=(20, 1)), 5),
batch_size=20,
epochs=3,
callbacks=[
pruning_callbacks.UpdatePruningStep(),
pruning_callbacks.PruningSummaries(log_dir=log_dir)
])
self.assertEqual(2,
tf.keras.backend.get_value(model.layers[0].pruning_step))
self.assertEqual(2,
tf.keras.backend.get_value(model.layers[1].pruning_step))
self._assertLogsExist(log_dir)
def testPruneTrainingRaisesError_PruningStepCallbackMissing(self):
model = prune.prune_low_magnitude(
keras.Sequential([
layers.Dense(10, activation='relu', input_shape=(100, )),
layers.Dense(2, activation='sigmoid')
]), **self.params)
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.SGD(),
metrics=['accuracy'])
# Throws an error since UpdatePruningStep is missing.
with self.assertRaises(errors_impl.InvalidArgumentError):
model.fit(
np.random.rand(1000, 100),
keras.utils.to_categorical(np.random.randint(2,
size=(1000, 1))))
model.fit(
np.random.rand(1000, 100),
keras.utils.to_categorical(np.random.randint(2, size=(1000, 1))),
# Works when callback is provided.
callbacks=[pruning_callbacks.UpdatePruningStep()])
def prune_model(original_model, train_images, train_labels):
batch_size = 256
epochs = 5
pruning_params = {
'pruning_schedule':
pruning_schedule.ConstantSparsity(0.75, begin_step=0, frequency=100)
}
pruning_model = prune.prune_low_magnitude(original_model, **pruning_params)
pruning_model.summary()
callbacks = [pruning_callbacks.UpdatePruningStep()]
fit_kwargs = {
'batch_size': batch_size,
'epochs': epochs,
'callbacks': callbacks,
}
compile_and_fit(pruning_model,
train_images,
train_labels,
compile_kwargs={},
fit_kwargs=fit_kwargs)
return pruning_model
callbacks=all_callbacks(stop_patience=yamlparameters["Training_early_stopping"],
initial_lr=yamlparameters["Training_learning_rate"],
lr_factor=yamlparameters["Training_lr_factor"],
lr_patience=yamlparameters["Training_lr_patience"],
lr_epsilon=yamlparameters["Training_lr_min_delta"],
lr_cooldown=yamlparameters["Training_lr_cooldown"],
lr_minimum=yamlparameters["Training_lr_minimum"],
Prune_begin=experiment.get_parameter("pruning_begin_epoch"),
Prune_end=experiment.get_parameter("pruning_end_epoch"),
prune_lrs=[experiment.get_parameter("pruning_lr_factor_1"),
experiment.get_parameter("pruning_lr_factor_2"),
experiment.get_parameter("pruning_lr_factor_3")],
outputDir=yamlparameters["TrainDir"])
callbacks.callbacks.append(pruning_callbacks.UpdatePruningStep())
with experiment.train():
keras_model.fit(X_train,y_train,
batch_size=yamlparameters["Training_batch_size"],
epochs=yamlparameters["Training_epochs"],
callbacks=callbacks.callbacks,
verbose=1,
validation_split=yamlparameters["Training_validation_split"],
shuffle=True)
keras_model = strip_pruning(keras_model)
keras_model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['binary_accuracy'])
keras_model.save(yamlparameters["TrainDir"]+"/Best_model.h5")
model.add(keras.layers.Embedding(max_features, 128, input_length=maxlen))
model.add(keras.layers.LSTM(128)) # try using a GRU instead, for fun
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(1))
model.add(keras.layers.Activation("sigmoid"))
model = prune.prune_low_magnitude(
model,
pruning_schedule.PolynomialDecay(initial_sparsity=0.3,
final_sparsity=0.7,
begin_step=1000,
end_step=3000))
# try using different optimizers and different optimizer configs
model.compile(loss="binary_crossentropy",
optimizer="adam",
metrics=["accuracy"])
print_model_sparsity(model)
print("Train...")
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=3,
callbacks=[pruning_callbacks.UpdatePruningStep()],
validation_data=(x_test, y_test))
score, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print_model_sparsity(model)
print("Test score:", score)
print("Test accuracy:", acc)
