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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 Keras loss functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import shutil
import numpy as np
from tensorflow.python import keras
from tensorflow.python.platform import test
try:
import h5py # pylint:disable=g-import-not-at-top
except ImportError:
h5py = None
ALL_LOSSES = [keras.losses.mean_squared_error,
keras.losses.mean_absolute_error,
keras.losses.mean_absolute_percentage_error,
keras.losses.mean_squared_logarithmic_error,
keras.losses.squared_hinge,
keras.losses.hinge,
keras.losses.categorical_crossentropy,
keras.losses.binary_crossentropy,
keras.losses.kullback_leibler_divergence,
keras.losses.poisson,
keras.losses.cosine_proximity,
keras.losses.logcosh,
keras.losses.categorical_hinge]
class _MSEMAELoss(object):
"""Loss function with internal state, for testing serialization code."""
def __init__(self, mse_fraction):
self.mse_fraction = mse_fraction
def __call__(self, y_true, y_pred):
return (self.mse_fraction * keras.losses.mse(y_true, y_pred) +
(1 - self.mse_fraction) * keras.losses.mae(y_true, y_pred))
def get_config(self):
return {'mse_fraction': self.mse_fraction}
class KerasLossesTest(test.TestCase):
def test_objective_shapes_3d(self):
with self.cached_session():
y_a = keras.backend.variable(np.random.random((5, 6, 7)))
y_b = keras.backend.variable(np.random.random((5, 6, 7)))
for obj in ALL_LOSSES:
objective_output = obj(y_a, y_b)
self.assertListEqual(objective_output.get_shape().as_list(), [5, 6])
def test_objective_shapes_2d(self):
with self.cached_session():
y_a = keras.backend.variable(np.random.random((6, 7)))
y_b = keras.backend.variable(np.random.random((6, 7)))
for obj in ALL_LOSSES:
objective_output = obj(y_a, y_b)
self.assertListEqual(objective_output.get_shape().as_list(), [6,])
def test_cce_one_hot(self):
with self.cached_session():
y_a = keras.backend.variable(np.random.randint(0, 7, (5, 6)))
y_b = keras.backend.variable(np.random.random((5, 6, 7)))
objective_output = keras.losses.sparse_categorical_crossentropy(y_a, y_b)
assert keras.backend.eval(objective_output).shape == (5, 6)
y_a = keras.backend.variable(np.random.randint(0, 7, (6,)))
y_b = keras.backend.variable(np.random.random((6, 7)))
objective_output = keras.losses.sparse_categorical_crossentropy(y_a, y_b)
assert keras.backend.eval(objective_output).shape == (6,)
def test_serialization(self):
fn = keras.losses.get('mse')
config = keras.losses.serialize(fn)
new_fn = keras.losses.deserialize(config)
self.assertEqual(fn, new_fn)
def test_categorical_hinge(self):
y_pred = keras.backend.variable(np.array([[0.3, 0.2, 0.1],
[0.1, 0.2, 0.7]]))
y_true = keras.backend.variable(np.array([[0, 1, 0], [1, 0, 0]]))
expected_loss = ((0.3 - 0.2 + 1) + (0.7 - 0.1 + 1)) / 2.0
loss = keras.backend.eval(keras.losses.categorical_hinge(y_true, y_pred))
self.assertAllClose(expected_loss, np.mean(loss))
def test_serializing_loss_class(self):
orig_loss_class = _MSEMAELoss(0.3)
with keras.utils.custom_object_scope({'_MSEMAELoss': _MSEMAELoss}):
serialized = keras.losses.serialize(orig_loss_class)
with keras.utils.custom_object_scope({'_MSEMAELoss': _MSEMAELoss}):
deserialized = keras.losses.deserialize(serialized)
assert isinstance(deserialized, _MSEMAELoss)
assert deserialized.mse_fraction == 0.3
def test_serializing_model_with_loss_class(self):
tmpdir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, tmpdir)
model_filename = os.path.join(tmpdir, 'custom_loss.h5')
with self.cached_session():
with keras.utils.custom_object_scope({'_MSEMAELoss': _MSEMAELoss}):
loss = _MSEMAELoss(0.3)
inputs = keras.layers.Input((2,))
outputs = keras.layers.Dense(1, name='model_output')(inputs)
model = keras.models.Model(inputs, outputs)
model.compile(optimizer='sgd', loss={'model_output': loss})
model.fit(np.random.rand(256, 2), np.random.rand(256, 1))
if h5py is None:
return
model.save(model_filename)
with keras.utils.custom_object_scope({'_MSEMAELoss': _MSEMAELoss}):
loaded_model = keras.models.load_model(model_filename)
loaded_model.predict(np.random.rand(128, 2))
if __name__ == '__main__':
test.main()
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