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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 activation functions."""
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.platform import test
def _ref_softmax(values):
m = np.max(values)
e = np.exp(values - m)
return e / np.sum(e)
class KerasActivationsTest(test.TestCase):
def test_serialization(self):
all_activations = ['softmax', 'relu', 'elu', 'tanh',
'sigmoid', 'hard_sigmoid', 'linear',
'softplus', 'softsign', 'selu']
for name in all_activations:
fn = keras.activations.get(name)
ref_fn = getattr(keras.activations, name)
assert fn == ref_fn
config = keras.activations.serialize(fn)
fn = keras.activations.deserialize(config)
assert fn == ref_fn
def test_softmax(self):
with self.cached_session():
x = keras.backend.placeholder(ndim=2)
f = keras.backend.function([x], [keras.activations.softmax(x)])
test_values = np.random.random((2, 5))
result = f([test_values])[0]
expected = _ref_softmax(test_values[0])
self.assertAllClose(result[0], expected, rtol=1e-05)
with self.assertRaises(ValueError):
x = keras.backend.placeholder(ndim=1)
keras.activations.softmax(x)
def test_temporal_softmax(self):
with self.cached_session():
x = keras.backend.placeholder(shape=(2, 2, 3))
f = keras.backend.function([x], [keras.activations.softmax(x)])
test_values = np.random.random((2, 2, 3)) * 10
result = f([test_values])[0]
expected = _ref_softmax(test_values[0, 0])
self.assertAllClose(result[0, 0], expected, rtol=1e-05)
def test_selu(self):
x = keras.backend.placeholder(ndim=2)
f = keras.backend.function([x], [keras.activations.selu(x)])
alpha = 1.6732632423543772848170429916717
scale = 1.0507009873554804934193349852946
with self.cached_session():
positive_values = np.array([[1, 2]], dtype=keras.backend.floatx())
result = f([positive_values])[0]
self.assertAllClose(result, positive_values * scale, rtol=1e-05)
negative_values = np.array([[-1, -2]], dtype=keras.backend.floatx())
result = f([negative_values])[0]
true_result = (np.exp(negative_values) - 1) * scale * alpha
self.assertAllClose(result, true_result)
def test_softplus(self):
def softplus(x):
return np.log(np.ones_like(x) + np.exp(x))
with self.cached_session():
x = keras.backend.placeholder(ndim=2)
f = keras.backend.function([x], [keras.activations.softplus(x)])
test_values = np.random.random((2, 5))
result = f([test_values])[0]
expected = softplus(test_values)
self.assertAllClose(result, expected, rtol=1e-05)
def test_softsign(self):
def softsign(x):
return np.divide(x, np.ones_like(x) + np.absolute(x))
with self.cached_session():
x = keras.backend.placeholder(ndim=2)
f = keras.backend.function([x], [keras.activations.softsign(x)])
test_values = np.random.random((2, 5))
result = f([test_values])[0]
expected = softsign(test_values)
self.assertAllClose(result, expected, rtol=1e-05)
def test_sigmoid(self):
def ref_sigmoid(x):
if x >= 0:
return 1 / (1 + np.exp(-x))
else:
z = np.exp(x)
return z / (1 + z)
sigmoid = np.vectorize(ref_sigmoid)
with self.cached_session():
x = keras.backend.placeholder(ndim=2)
f = keras.backend.function([x], [keras.activations.sigmoid(x)])
test_values = np.random.random((2, 5))
result = f([test_values])[0]
expected = sigmoid(test_values)
self.assertAllClose(result, expected, rtol=1e-05)
def test_hard_sigmoid(self):
def ref_hard_sigmoid(x):
x = (x * 0.2) + 0.5
z = 0.0 if x <= 0 else (1.0 if x >= 1 else x)
return z
hard_sigmoid = np.vectorize(ref_hard_sigmoid)
with self.cached_session():
x = keras.backend.placeholder(ndim=2)
f = keras.backend.function([x], [keras.activations.hard_sigmoid(x)])
test_values = np.random.random((2, 5))
result = f([test_values])[0]
expected = hard_sigmoid(test_values)
self.assertAllClose(result, expected, rtol=1e-05)
def test_relu(self):
with self.cached_session():
x = keras.backend.placeholder(ndim=2)
f = keras.backend.function([x], [keras.activations.relu(x)])
test_values = np.random.random((2, 5))
result = f([test_values])[0]
# No negative values in test values...
self.assertAllClose(result, test_values, rtol=1e-05)
def test_elu(self):
with self.cached_session():
x = keras.backend.placeholder(ndim=2)
f = keras.backend.function([x], [keras.activations.elu(x, 0.5)])
test_values = np.random.random((2, 5))
result = f([test_values])[0]
self.assertAllClose(result, test_values, rtol=1e-05)
negative_values = np.array([[-1, -2]], dtype=keras.backend.floatx())
result = f([negative_values])[0]
true_result = (np.exp(negative_values) - 1) / 2
self.assertAllClose(result, true_result)
def test_tanh(self):
with self.cached_session():
test_values = np.random.random((2, 5))
x = keras.backend.placeholder(ndim=2)
exp = keras.activations.tanh(x)
f = keras.backend.function([x], [exp])
result = f([test_values])[0]
expected = np.tanh(test_values)
self.assertAllClose(result, expected, rtol=1e-05)
def test_linear(self):
x = np.random.random((10, 5))
self.assertAllClose(x, keras.activations.linear(x))
def test_invalid_usage(self):
with self.assertRaises(ValueError):
keras.activations.get('unknown')
# The following should be possible but should raise a warning:
keras.activations.get(keras.layers.LeakyReLU())
if __name__ == '__main__':
test.main()
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