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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 training routines."""
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.framework import test_util
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.layers.convolutional import Conv2D
from tensorflow.python.platform import test
from tensorflow.python.training import rmsprop
class TrainingGPUTest(test.TestCase):
@test_util.run_in_graph_and_eager_modes
def test_model_with_crossentropy_losses_channels_first(self):
"""Tests use of all crossentropy losses with `channels_first`.
Tests `sparse_categorical_crossentropy`, `categorical_crossentropy`,
and `binary_crossentropy`.
Verifies that evaluate gives the same result with either `channels_first`
or `channels_last` image_data_format.
"""
def prepare_simple_model(input_tensor, loss_name, target):
axis = 1 if K.image_data_format() == 'channels_first' else -1
loss = None
num_channels = None
activation = None
if loss_name == 'sparse_categorical_crossentropy':
loss = lambda y_true, y_pred: K.sparse_categorical_crossentropy( # pylint: disable=g-long-lambda
y_true, y_pred, axis=axis)
num_channels = np.amax(target) + 1
activation = 'softmax'
elif loss_name == 'categorical_crossentropy':
loss = lambda y_true, y_pred: K.categorical_crossentropy( # pylint: disable=g-long-lambda
y_true, y_pred, axis=axis)
num_channels = target.shape[axis]
activation = 'softmax'
elif loss_name == 'binary_crossentropy':
loss = lambda y_true, y_pred: K.binary_crossentropy(y_true, y_pred) # pylint: disable=unnecessary-lambda
num_channels = target.shape[axis]
activation = 'sigmoid'
predictions = Conv2D(num_channels,
1,
activation=activation,
kernel_initializer='ones',
bias_initializer='ones')(input_tensor)
simple_model = keras.models.Model(inputs=input_tensor,
outputs=predictions)
simple_model.compile(optimizer=rmsprop.RMSPropOptimizer(1e-3), loss=loss)
return simple_model
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
losses_to_test = ['sparse_categorical_crossentropy',
'categorical_crossentropy', 'binary_crossentropy']
data_channels_first = np.array([[[[8., 7.1, 0.], [4.5, 2.6, 0.55],
[0.9, 4.2, 11.2]]]], dtype=np.float32)
# Labels for testing 4-class sparse_categorical_crossentropy, 4-class
# categorical_crossentropy, and 2-class binary_crossentropy:
labels_channels_first = [np.array([[[[0, 1, 3], [2, 1, 0], [2, 2, 1]]]], dtype=np.float32), # pylint: disable=line-too-long
np.array([[[[0, 1, 0], [0, 1, 0], [0, 0, 0]],
[[1, 0, 0], [0, 0, 1], [0, 1, 0]],
[[0, 0, 0], [1, 0, 0], [0, 0, 1]],
[[0, 0, 1], [0, 0, 0], [1, 0, 0]]]], dtype=np.float32), # pylint: disable=line-too-long
np.array([[[[0, 1, 0], [0, 1, 0], [0, 0, 1]],
[[1, 0, 1], [1, 0, 1], [1, 1, 0]]]], dtype=np.float32)] # pylint: disable=line-too-long
# Compute one loss for each loss function in the list `losses_to_test`:
loss_channels_last = [0., 0., 0.]
loss_channels_first = [0., 0., 0.]
old_data_format = K.image_data_format()
# Evaluate a simple network with channels last, with all three loss
# functions:
K.set_image_data_format('channels_last')
data = np.moveaxis(data_channels_first, 1, -1)
for index, loss_function in enumerate(losses_to_test):
labels = np.moveaxis(labels_channels_first[index], 1, -1)
inputs = keras.Input(shape=(3, 3, 1))
model = prepare_simple_model(inputs, loss_function, labels)
loss_channels_last[index] = model.evaluate(x=data, y=labels,
batch_size=1, verbose=0)
# Evaluate the same network with channels first, with all three loss
# functions:
K.set_image_data_format('channels_first')
data = data_channels_first
for index, loss_function in enumerate(losses_to_test):
labels = labels_channels_first[index]
inputs = keras.Input(shape=(1, 3, 3))
model = prepare_simple_model(inputs, loss_function, labels)
loss_channels_first[index] = model.evaluate(x=data, y=labels,
batch_size=1, verbose=0)
K.set_image_data_format(old_data_format)
np.testing.assert_allclose(loss_channels_first,
loss_channels_last,
err_msg='{}{}'.format(
'Computed different losses for ',
'channels_first and channels_last'))
if __name__ == '__main__':
test.main()
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