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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for normalization layers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python import keras
from tensorflow.python.keras import testing_utils
from tensorflow.python.platform import test
class NormalizationLayersTest(test.TestCase):
def test_basic_batchnorm(self):
with self.test_session():
testing_utils.layer_test(
keras.layers.BatchNormalization,
kwargs={
'momentum': 0.9,
'epsilon': 0.1,
'gamma_regularizer': keras.regularizers.l2(0.01),
'beta_regularizer': keras.regularizers.l2(0.01)
},
input_shape=(3, 4, 2))
testing_utils.layer_test(
keras.layers.BatchNormalization,
kwargs={
'gamma_initializer': 'ones',
'beta_initializer': 'ones',
'moving_mean_initializer': 'zeros',
'moving_variance_initializer': 'ones'
},
input_shape=(3, 4, 2))
testing_utils.layer_test(
keras.layers.BatchNormalization,
kwargs={'scale': False,
'center': False},
input_shape=(3, 3))
def test_batchnorm_weights(self):
with self.test_session():
layer = keras.layers.BatchNormalization(scale=False, center=False)
layer.build((None, 3, 4))
self.assertEqual(len(layer.trainable_weights), 0)
self.assertEqual(len(layer.weights), 2)
layer = keras.layers.BatchNormalization()
layer.build((None, 3, 4))
self.assertEqual(len(layer.trainable_weights), 2)
self.assertEqual(len(layer.weights), 4)
def test_batchnorm_regularization(self):
with self.test_session():
layer = keras.layers.BatchNormalization(
gamma_regularizer='l1', beta_regularizer='l1')
layer.build((None, 3, 4))
self.assertEqual(len(layer.losses), 2)
max_norm = keras.constraints.max_norm
layer = keras.layers.BatchNormalization(
gamma_constraint=max_norm, beta_constraint=max_norm)
layer.build((None, 3, 4))
self.assertEqual(layer.gamma.constraint, max_norm)
self.assertEqual(layer.beta.constraint, max_norm)
def test_batchnorm_correctness(self):
with self.test_session():
model = keras.models.Sequential()
norm = keras.layers.BatchNormalization(input_shape=(10,), momentum=0.8)
model.add(norm)
model.compile(loss='mse', optimizer='sgd')
# centered on 5.0, variance 10.0
x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 10))
model.fit(x, x, epochs=4, verbose=0)
out = model.predict(x)
out -= keras.backend.eval(norm.beta)
out /= keras.backend.eval(norm.gamma)
np.testing.assert_allclose(out.mean(), 0.0, atol=1e-1)
np.testing.assert_allclose(out.std(), 1.0, atol=1e-1)
def test_batchnorm_convnet(self):
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
model = keras.models.Sequential()
norm = keras.layers.BatchNormalization(
axis=1, input_shape=(3, 4, 4), momentum=0.8)
model.add(norm)
model.compile(loss='mse', optimizer='sgd')
# centered on 5.0, variance 10.0
x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 3, 4, 4))
model.fit(x, x, epochs=4, verbose=0)
out = model.predict(x)
out -= np.reshape(keras.backend.eval(norm.beta), (1, 3, 1, 1))
out /= np.reshape(keras.backend.eval(norm.gamma), (1, 3, 1, 1))
np.testing.assert_allclose(np.mean(out, axis=(0, 2, 3)), 0.0, atol=1e-1)
np.testing.assert_allclose(np.std(out, axis=(0, 2, 3)), 1.0, atol=1e-1)
def test_batchnorm_convnet_channel_last(self):
with self.test_session():
# keras.backend.set_learning_phase(True)
model = keras.models.Sequential()
norm = keras.layers.BatchNormalization(
axis=-1, input_shape=(4, 4, 3), momentum=0.8)
model.add(norm)
model.compile(loss='mse', optimizer='sgd')
# centered on 5.0, variance 10.0
x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 4, 4, 3))
model.fit(x, x, epochs=4, verbose=0)
out = model.predict(x)
out -= np.reshape(keras.backend.eval(norm.beta), (1, 1, 1, 3))
out /= np.reshape(keras.backend.eval(norm.gamma), (1, 1, 1, 3))
np.testing.assert_allclose(np.mean(out, axis=(0, 1, 2)), 0.0, atol=1e-1)
np.testing.assert_allclose(np.std(out, axis=(0, 1, 2)), 1.0, atol=1e-1)
def test_shared_batchnorm(self):
"""Test that a BN layer can be shared across different data streams.
"""
with self.test_session():
# Test single layer reuse
bn = keras.layers.BatchNormalization()
x1 = keras.layers.Input(shape=(10,))
_ = bn(x1)
x2 = keras.layers.Input(shape=(10,))
y2 = bn(x2)
x = np.random.normal(loc=5.0, scale=10.0, size=(2, 10))
model = keras.models.Model(x2, y2)
model.compile('sgd', 'mse')
model.train_on_batch(x, x)
self.assertEqual(len(bn.updates), 4)
self.assertEqual(len(model.updates), 2)
self.assertEqual(len(model.get_updates_for(x1)), 0)
self.assertEqual(len(model.get_updates_for(x2)), 2)
# Test model-level reuse
x3 = keras.layers.Input(shape=(10,))
y3 = model(x3)
new_model = keras.models.Model(x3, y3, name='new_model')
self.assertEqual(len(new_model.updates), 2)
self.assertEqual(len(model.updates), 4)
self.assertEqual(len(new_model.get_updates_for(x3)), 2)
new_model.compile('sgd', 'mse')
new_model.train_on_batch(x, x)
def test_that_trainable_disables_updates(self):
with self.test_session():
val_a = np.random.random((10, 4))
val_out = np.random.random((10, 4))
a = keras.layers.Input(shape=(4,))
layer = keras.layers.BatchNormalization(input_shape=(4,))
b = layer(a)
model = keras.models.Model(a, b)
model.trainable = False
assert not model.updates
model.compile('sgd', 'mse')
assert not model.updates
x1 = model.predict(val_a)
model.train_on_batch(val_a, val_out)
x2 = model.predict(val_a)
self.assertAllClose(x1, x2, atol=1e-7)
model.trainable = True
model.compile('sgd', 'mse')
assert model.updates
model.train_on_batch(val_a, val_out)
x2 = model.predict(val_a)
assert np.abs(np.sum(x1 - x2)) > 1e-5
layer.trainable = False
model.compile('sgd', 'mse')
assert not model.updates
x1 = model.predict(val_a)
model.train_on_batch(val_a, val_out)
x2 = model.predict(val_a)
self.assertAllClose(x1, x2, atol=1e-7)
def test_batchnorm_trainable(self):
"""Tests that batchnorm layer is trainable when learning phase is enabled.
Computes mean and std for current inputs then
applies batch normalization using them.
"""
with self.test_session():
bn_mean = 0.5
bn_std = 10.
val_a = np.expand_dims(np.arange(10.), axis=1)
def get_model(bn_mean, bn_std):
inp = keras.layers.Input(shape=(1,))
x = keras.layers.BatchNormalization()(inp)
model1 = keras.models.Model(inp, x)
model1.set_weights([
np.array([1.]),
np.array([0.]),
np.array([bn_mean]),
np.array([bn_std**2])
])
return model1
# Simulates training-mode with trainable layer.
# Should use mini-batch statistics.
keras.backend.set_learning_phase(1)
model = get_model(bn_mean, bn_std)
model.compile(loss='mse', optimizer='rmsprop')
out = model.predict(val_a)
self.assertAllClose(
(val_a - np.mean(val_a)) / np.std(val_a), out, atol=1e-3)
if __name__ == '__main__':
test.main()
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