def testNewOptSameVarScope(self):
model = _simple_mlp()
opt = optimizers.Kfac(
learning_rate=0.01, damping=0.01, model=model, loss='mse')
opt._create_optimizer()
opt2 = optimizers.Kfac(
learning_rate=0.02, damping=0.03, model=model, loss='mse')
opt2._create_optimizer()
def testConfig(self):
fisher_approx = {layers.Dense: 'kron_in_diag', 'dense_1': 'kron_both_diag'}
kwargs = {
'loss': 'mse',
'momentum': 7,
'num_burnin_steps': 11,
'min_damping': 9,
'invert_every': 13,
'fisher_approx': fisher_approx,
'seed': 12,
}
opt = optimizers.Kfac(
learning_rate=3, damping=5, model=_simple_mlp(), **kwargs)
opt.learning_rate = 23
opt.damping = 27
config = opt.get_config()
self.assertEqual(config['learning_rate'], 23)
self.assertEqual(config['damping'], 27)
dense_approx = fisher_approx.pop(layers.Dense)
fisher_approx[utils._CLASS_NAME_PREFIX + 'Dense'] = dense_approx
for key, val in kwargs.items():
self.assertEqual(config[key], val)
# Below is how Keras's model.save saves the configs. If the config is not
# serializable, it will throw a TypeError or OverflowError.
json.dumps(config, default=serialization.get_json_type).encode('utf8')
def testCustomLossFn(self):
rands = lambda: np.random.random((100, 1)).astype(np.float32)
dataset = tf.data.Dataset.from_tensor_slices((rands(), rands()))
dataset = dataset.repeat().batch(10, drop_remainder=True)
train_batch = dataset.make_one_shot_iterator().get_next()
model = tf.keras.Sequential(
[tf.keras.layers.Dense(1, input_shape=(1, ))])
def loss_fn(inputs):
mse = tf.keras.losses.mean_squared_error(model(inputs[0]),
inputs[1])
return tf.reduce_mean(mse)
loss = 'mse'
train_batch = dataset.make_one_shot_iterator().get_next()
optimizer = optimizers.Kfac(damping=10.,
train_batch=train_batch,
adaptive=True,
model=model,
loss=loss,
loss_fn=loss_fn,
qmodel_update_rescale=0.01)
model.compile(optimizer, loss)
model.fit(train_batch, steps_per_epoch=10, epochs=1)
self.assertEqual(loss_fn, optimizer.optimizer._loss_fn)
def testRegisterLayersWithLayerCollection(self): model, loss = _mnist_model(), 'categorical_crossentropy' lc = utils.get_layer_collection(model, loss) opt = optimizers.Kfac(learning_rate=0.01, damping=0.001) opt.register_layers(layer_collection=lc) model.compile(optimizer=opt, loss=loss) opt.get_updates(model.total_loss, model.trainable_weights)
def testCustomTrainingLoopMakeOptimizerBeforeModelCall(self):
# We defer the creation of the layer_collection to the minimize call for
# this situation, because if we make the layer_collection immediately it
# will capture the wrong inbound node.
model = tf.keras.Sequential([
layers.Conv2D(13, 5),
layers.BatchNormalization(fused=False),
layers.Conv2D(23, 3),
layers.LayerNormalization(),
layers.GlobalMaxPool2D(),
layers.Dense(10, activation='softmax', name='output_test')
])
optimizer = optimizers.Kfac(learning_rate=0.01,
damping=0.01,
model=model,
loss='binary_crossentropy')
x, y = _get_synthetic_mnist_train_tensors(batch_size=10)
model_input = tf.keras.Input(tensor=x)
output = model(model_input)
loss = tf.keras.losses.binary_crossentropy(output, y)
train_op = optimizer.minimize(loss, var_list=model.trainable_weights)
with self.cached_session() as sess:
sess.run(tf.global_variables_initializer())
for _ in range(3):
sess.run([train_op])
def test_functional_model_saving(self):
if h5py is None:
self.skipTest('h5py required to run this test')
with self.cached_session():
inputs = keras.layers.Input(shape=(3, ))
x = keras.layers.Dense(2)(inputs)
output = keras.layers.Dense(3)(x)
model = keras.models.Model(inputs, output)
model.compile(loss=keras.losses.MSE,
optimizer=optimizers.Kfac(model=model,
**_KFAC_KWARGS),
metrics=[
keras.metrics.categorical_accuracy,
keras.metrics.CategoricalAccuracy()
],
weighted_metrics=[
keras.metrics.categorical_accuracy,
keras.metrics.CategoricalAccuracy()
])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
fd, fname = tempfile.mkstemp('.h5')
keras.models.save_model(model, fname)
model = saving_utils.load_model(fname, optimizer_name='new')
os.close(fd)
os.remove(fname)
out2 = model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
def testRegisterLayersCompiledModel(self, loss): opt = optimizers.Kfac(learning_rate=0.01, damping=0.001) model = _mnist_model() model.compile(optimizer=opt, loss=loss) opt.register_layers(model=model) model.compile(optimizer=opt, loss=loss) opt.get_updates(model.total_loss, model.trainable_weights)
def testCustomTrainingLoopFunctionalInpShape(self):
# We need to ensure correct inbound node is used for layer collection.
x, y = _get_synthetic_mnist_train_tensors(batch_size=10)
model_input = tf.keras.Input(tensor=x)
# Build Model
inp = tf.keras.Input(shape=(28, 28, 1))
x = layers.Conv2D(13, 5)(inp)
x = layers.BatchNormalization(fused=True)(x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(23, 3)(x)
x = layers.LayerNormalization()(x)
x = layers.GlobalMaxPool2D()(x)
out = layers.Dense(10, activation='softmax', name='output_test')(x)
model = tf.keras.Model(inputs=inp, outputs=out)
output = model(model_input)
loss = tf.keras.losses.binary_crossentropy(output, y)
optimizer = optimizers.Kfac(damping=0.01, learning_rate=0.01,
model=model, loss='binary_crossentropy')
train_op = optimizer.minimize(loss, var_list=model.trainable_weights)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for _ in range(3):
sess.run([train_op])
def testTrainWithoutCreatingOptimizerFails(self):
with self.assertRaisesRegex(ValueError, '.*provide a model with a loss.*'):
opt = optimizers.Kfac(learning_rate=0.01, damping=0.001)
model = _mnist_model()
model.compile(optimizer=opt, loss='categorical_crossentropy')
grads_vars = opt.get_gradients(model.total_loss, model.trainable_weights)
opt.apply_gradients(grads_vars)
def testInstantiationWithLayerCollection(self):
model = _simple_mlp()
lc = utils.get_layer_collection(model, 'mse')
opt = optimizers.Kfac(
learning_rate=0.1, damping=0.2, layer_collection=lc)
model.compile(optimizer=opt, loss='mse')
opt.get_updates(model.total_loss, model.trainable_weights)
def testFunctionalInstantiation(self):
inputs = layers.Input(shape=(3,))
x = layers.Dense(4, activation=tf.nn.relu)(inputs)
outputs = layers.Dense(5, activation=tf.nn.softmax)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
optimizers.Kfac(learning_rate=0.002, damping=0.04,
model=model, loss='binary_crossentropy')
def _train_model(data,
model,
loss,
lr=0.001,
damping=0.001,
batch_size=32,
epochs=1,
loss_weights=None):
"""Compiles and fits model to data and returns trainging results.
Args:
data: Tuple of numpy arrays shaped ((x_train, y_train), (x_test, y_test)).
model: Uncompiled Keras model with inputs/output shapes matching the data.
loss: tf.keras.losses loss function or serialized (string) loss function.
lr: Learning rate for optimizer.
damping: Damping parameter for KFAC.
batch_size: Batch size used for training.
epochs: Number of training epochs.
loss_weights: List of weights or dict mapping layer names to loss function
weight.
Returns:
A History object. Calling History.history gives you a dictionary with
training and validation results.
"""
(x_train, y_train), valid_data = data
opt = optimizers.Kfac(learning_rate=lr, damping=damping, model=model,
loss=loss, loss_weights=loss_weights)
model.compile(opt, loss, loss_weights=loss_weights)
return model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,
validation_data=valid_data, verbose=0)
def testExponentialDampingValuesWithDecayRate(self):
init_value = 0.01
decay_rate = 0.3
num_decay_steps = 4
num_delay_steps = 3
opt = optimizers.Kfac(learning_rate=0.01,
damping=init_value,
model=self.model,
loss='mse')
self.model.compile(opt, 'mse')
damping_list = []
cbs = [
callbacks.ExponentialDecay(hyperparameter='damping',
init_value=init_value,
decay_rate=decay_rate,
num_decay_steps=num_decay_steps,
num_delay_steps=num_delay_steps,
verbose=1),
HyperParamTracker('damping', damping_list, HyperParamTracker.BATCH)
]
self.model.fit(self.data,
self.labels,
batch_size=self.batch_size,
callbacks=cbs)
expected_list = [init_value] * num_delay_steps + [
init_value * decay_rate**min(i, num_decay_steps)
for i in range(self.num_steps - num_delay_steps)
]
self.assertAllClose(damping_list, expected_list)
def testClipValueFails(self):
with self.assertRaises(ValueError):
optimizers.Kfac(learning_rate=0.01,
damping=0.01,
model=_simple_mlp(),
loss='mse',
clipvalue=0.1)
def testFromConfig(self, kwargs_updates):
kwargs = {
'learning_rate': 3,
'damping': 5,
'momentum': 7,
'min_damping': 9,
'num_burnin_steps': 11,
'invert_every': 13,
'fisher_approx': {
layers.Dense: 'kron_in_diag',
'dense_1': 'kron_both_diag'
},
}
kwargs.update(kwargs_updates)
opt = optimizers.Kfac(model=_simple_mlp(), **kwargs)
config = opt.get_config()
config['name'] = 'diff_scope_name'
opt2 = optimizers.Kfac.from_config(config)
config2 = opt2.get_config()
config2.pop('name')
config.pop('name')
self.assertEqual(config, config2)
# Below is how Keras's model.save saves the configs. If the config is not
# serializable, it will throw a TypeError or OverflowError.
json.dumps(config, default=serialization.get_json_type).encode('utf8')
json.dumps(config2, default=serialization.get_json_type).encode('utf8')
def testInferredBatchSizeFail(self, kfac_kwargs):
dataset = tf.data.Dataset.from_tensors(([1.], [1.]))
dataset = dataset.repeat().batch(11, drop_remainder=False)
train_batch = dataset.make_one_shot_iterator().get_next()
with self.assertRaisesRegex(ValueError, 'Could not infer batch_size.*'):
optimizer = optimizers.Kfac(damping=10.,
train_batch=train_batch,
**kfac_kwargs)
def testLossTensor(self):
loss_tensor = tf.convert_to_tensor(2.0)
opt = optimizers.Kfac(learning_rate=0.01,
damping=0.01,
model=_simple_mlp(),
loss='mse',
loss_tensor=loss_tensor)
self.assertEqual(opt.optimizer._loss_tensor, loss_tensor)
def testSeed(self):
opt = optimizers.Kfac(learning_rate=0.01,
damping=0.01,
model=_simple_mlp(),
loss='mse',
seed=4321)
lc = opt.optimizer.layers
self.assertEqual(lc._loss_dict['squared_error_loss'][0]._default_seed,
4321)
def testGettingVariableHyperFails(self):
self.skipTest('This is not fixed in TF 1.14 yet.')
opt = optimizers.Kfac(model=_simple_mlp(),
loss='mse',
learning_rate=tf.Variable(0.1),
damping=tf.Variable(0.1))
with self.assertRaisesRegex(tf.errors.FailedPreconditionError,
'.*uninitialized.*'):
backend.get_value(opt.learning_rate)
def testAdaptiveWithLR(self, kfac_kwargs):
dataset = tf.data.Dataset.from_tensors(([1.], [1.]))
dataset = dataset.repeat().batch(11, drop_remainder=True)
train_batch = dataset.make_one_shot_iterator().get_next()
with self.assertRaisesRegex(ValueError, 'learning_rate must be None.*'):
optimizer = optimizers.Kfac(damping=10.,
train_batch=train_batch,
learning_rate=0.1,
**kfac_kwargs)
def testSequentialInstantiation(self):
model = tf.keras.Sequential([
layers.Conv2D(7, (3, 3), input_shape=(28, 28, 3)),
layers.Activation('relu'),
layers.Conv2D(13, (3, 3), activation='relu'),
layers.GlobalMaxPool2D(),
layers.Activation('softmax')
])
optimizers.Kfac(learning_rate=0.03, damping=0.00007,
model=model, loss='binary_crossentropy')
def testRNNFails(self):
model = tf.keras.Sequential()
model.add(layers.Embedding(43, 128))
model.add(layers.LSTM(128, dropout=0.2, recurrent_dropout=0.2))
model.add(layers.Dense(1, activation='sigmoid'))
opt = optimizers.Kfac(learning_rate=0.003, damping=0.003,
model=model, loss='binary_crossentropy')
with self.assertRaisesRegex(ValueError,
'.*lstm.* has more than one parent tensor.$'):
opt._create_optimizer()
def testModifyingTensorHypersFails(self, name, val):
kwargs = {'learning_rate': 3, 'damping': 5, 'momentum': 7}
kwargs[name] = tf.convert_to_tensor(val)
opt = optimizers.Kfac(model=_simple_mlp(), loss='mse', **kwargs)
with self.subTest(name='AssignedCorrectly'):
self.assertEqual(backend.get_value(getattr(opt, name)), val)
with self.subTest(name='RaisesError'):
with self.assertRaisesRegex(AttributeError,
"Can't set attribute: {}".format(name)):
setattr(opt, name, 17)
def test_saving_model_with_long_weights_names(self):
self.skipTest('KFAC does not support nested models yet.')
if h5py is None:
self.skipTest('h5py required to run this test')
with self.cached_session():
x = keras.Input(shape=(2, ), name='nested_model_input')
f = x
for i in range(4):
f = keras.layers.Dense(2,
name='nested_model_dense_%d' % (i, ))(f)
# This layer name will make the `weights_name`
# HDF5 attribute blow out of proportion.
f = keras.layers.Dense(2,
name='nested_model_output' + ('x' *
(2**14)))(f)
nested_model = keras.Model(inputs=[x],
outputs=[f],
name='nested_model')
x = keras.Input(shape=(2, ), name='outer_model_input')
f = nested_model(x)
f = keras.layers.Dense(2, name='outer_model_output')(f)
model = keras.Model(inputs=[x], outputs=[f])
model.compile(loss='mse',
optimizer=optimizers.Kfac(model=model,
**_KFAC_KWARGS),
metrics=['acc'])
x = np.random.random((1, 2))
y = np.random.random((1, 2))
model.train_on_batch(x, y)
out = model.predict(x)
fd, fname = tempfile.mkstemp('.h5')
keras.models.save_model(model, fname)
model = saving_utils.load_model(fname, optimizer_name='new')
# Check that the HDF5 files contains chunked array
# of weight names.
with h5py.File(fname, 'r') as h5file:
num_weight_arrays = len([
attr
for attr in h5file['model_weights']['nested_model'].attrs
if attr.startswith('weight_names')
])
# The chunking of layer names array should have happened.
self.assertGreater(num_weight_arrays, 0)
out2 = model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
# Cleanup
os.close(fd)
os.remove(fname)
def test_sequential_model_saving(self):
if h5py is None:
self.skipTest('h5py required to run this test')
with self.cached_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(2, )))
model.add(keras.layers.RepeatVector(3))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(3))
model.compile(loss=keras.losses.MSE,
optimizer=optimizers.Kfac(model=model,
**_KFAC_KWARGS),
metrics=[
keras.metrics.categorical_accuracy,
keras.metrics.CategoricalAccuracy()
])
x = np.random.random((1, 2))
y = np.random.random((1, 3))
# TODO(b/136561651): Since we use TFP distributions to sample from the
# output distribution, optimizer's won't match exactly unless they are run
# for the same number of steps. Even with a random seed, the internal
# state of TFP changes with each call. We must switch to a stateless
# sampler. Uncomment the train line below once this is implemented.
# model.train_on_batch(x, y)
out = model.predict(x)
fd, fname = tempfile.mkstemp('.h5')
keras.models.save_model(model, fname)
new_model = saving_utils.load_model(fname, optimizer_name='new')
os.close(fd)
os.remove(fname)
out2 = new_model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
# test that new updates are the same with both models
x = np.random.random((1, 2))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
new_model.train_on_batch(x, y)
x = np.random.random((1, 2))
y = np.random.random((1, 3))
eval_out = model.evaluate(x, y)
eval_out2 = new_model.evaluate(x, y)
self.assertArrayNear(eval_out, eval_out2, 1e-03)
out = model.predict(x)
out2 = new_model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
def testGettingHyper(self, hyper_ctor):
kwarg_values = {'learning_rate': 3, 'damping': 20, 'momentum': 13}
kwargs = {k: hyper_ctor(v) for k, v in kwarg_values.items()}
opt = optimizers.Kfac(model=_simple_mlp(), loss='mse', **kwargs)
get_value = backend.get_value
tf_opt = opt.optimizer
with self.subTest(name='MatchesFloat'):
for name, val in kwarg_values.items():
self.assertEqual(get_value(getattr(opt, name)), val)
with self.subTest(name='MatchesTfOpt'):
self.assertEqual(get_value(opt.lr), get_value(tf_opt.learning_rate))
self.assertEqual(get_value(opt.damping), get_value(tf_opt.damping))
self.assertEqual(get_value(opt.momentum), get_value(tf_opt.momentum))
def testTrainModelWithFusedBN(self, has_shift):
model = tf.keras.Sequential([
layers.Conv2D(13, 5, input_shape=(28, 28, 1)),
layers.BatchNormalization(center=has_shift, fused=True),
layers.GlobalMaxPool2D(),
layers.Dense(10, activation='softmax')
])
(x_train, y_train), _ = _get_synthetic_mnist_dataset()
loss = 'categorical_crossentropy'
opt = optimizers.Kfac(
learning_rate=0.01, damping=0.01, model=model, loss=loss)
model.compile(opt, loss)
return model.fit(x_train, y_train, batch_size=32, epochs=1, verbose=0)
def testSettingName(self):
model = _simple_mlp()
optimizer = optimizers.Kfac(damping=0.01, learning_rate=0.01,
model=model, loss='mse')
optimizer.name = 'new_name'
self.assertEqual(optimizer._name, 'new_name')
self.assertEqual(optimizer.get_config()['name'], 'new_name')
self.assertEqual(optimizer._kfac_kwargs['name'], 'new_name')
model.compile(optimizer, 'mse')
model._make_train_function()
with self.assertRaisesRegex(ValueError,
'.*after the variables are created.*'):
optimizer.name = 'another_name'
def testLRBackwardsCompatibility(self):
"""This tests learning rate getting/setting used by old Keras callbacks."""
opt = optimizers.Kfac(
learning_rate=3, damping=5, model=_simple_mlp(), loss='mse')
self.assertEqual(backend.get_value(opt.lr), 3)
self.assertEqual(backend.get_value(opt.learning_rate), 3)
opt.lr = 7
self.assertEqual(backend.get_value(opt.lr), 7)
self.assertEqual(backend.get_value(opt.learning_rate), 7)
backend.set_value(opt.lr, 9)
self.assertEqual(backend.get_value(opt.lr), 9)
self.assertEqual(backend.get_value(opt.learning_rate), 9)
backend.set_value(opt.learning_rate, 11)
self.assertEqual(backend.get_value(opt.lr), 11)
self.assertEqual(backend.get_value(opt.learning_rate), 11)
def testAdaptiveModelFit(self, adaptive_kwargs):
rands = lambda: np.random.random((100, 1)).astype(np.float32)
dataset = tf.data.Dataset.from_tensor_slices((rands(), rands()))
dataset = dataset.repeat().batch(10, drop_remainder=True)
train_batch = dataset.make_one_shot_iterator().get_next()
model = tf.keras.Sequential([tf.keras.layers.Dense(1, input_shape=(1,))])
loss = 'mse'
train_batch = dataset.make_one_shot_iterator().get_next()
optimizer = optimizers.Kfac(damping=10.,
train_batch=train_batch,
model=model,
loss=loss,
**adaptive_kwargs)
model.compile(optimizer, loss)
model.fit(train_batch, steps_per_epoch=10, epochs=1)
