diff options
Diffstat (limited to 'tensorflow/python/keras/engine/training_gpu_test.py')
| -rw-r--r-- | tensorflow/python/keras/engine/training_gpu_test.py | 125 |
1 files changed, 125 insertions, 0 deletions
diff --git a/tensorflow/python/keras/engine/training_gpu_test.py b/tensorflow/python/keras/engine/training_gpu_test.py new file mode 100644 index 0000000000..5825ce814f --- /dev/null +++ b/tensorflow/python/keras/engine/training_gpu_test.py @@ -0,0 +1,125 @@ +# 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() |