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# Copyright 2015 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 Relu and ReluGrad."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
import tensorflow.python.ops.nn_grad # pylint: disable=unused-import
from tensorflow.python.platform import test
from tensorflow.python.training import gradient_descent
def _elu_grad_grad(activation):
if activation < 0:
return np.exp(activation)
return 0
class ReluTest(test.TestCase):
def _npRelu(self, np_features):
return np.maximum(np_features, np.zeros(np_features.shape))
def testNpRelu(self):
self.assertAllClose(
np.array([[0.0, 0.7, 0.0, 0.3, 0.0], [0.1, 0.0, 0.5, 0.0, 0.9]]),
self._npRelu(
np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7, 0.9]
])))
def _testRelu(self, np_features, use_gpu=False):
np_relu = self._npRelu(np_features)
with self.test_session(use_gpu=use_gpu):
relu = nn_ops.relu(np_features)
tf_relu = relu.eval()
self.assertAllClose(np_relu, tf_relu)
self.assertShapeEqual(np_relu, relu)
def testNumbers(self):
for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
self._testRelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=False)
if t in [np.float16, np.float32, np.float64]:
self._testRelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=True)
# The gradient test for ReLU is a bit tricky as the derivative is not well
# defined at around zero and we want to avoid that in terms of input values.
def testGradientFloat32(self):
with self.test_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5],
name="x")
y = nn_ops.relu(x, name="relu")
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float32,
order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], y, [2, 5], x_init_value=x_init)
print("relu (float32) gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradientFloat64(self):
with self.test_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5],
dtype=dtypes.float64,
name="x")
y = nn_ops.relu(x, name="relu")
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float64,
order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], y, [2, 5], x_init_value=x_init)
print("relu (float64) gradient err = ", err)
self.assertLess(err, 1e-10)
def testGradGradFloat32(self):
with self.test_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5],
name="x")
y = nn_ops.relu(x, name="relu")
z = gradients_impl.gradients(y, x)
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float32,
order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], z[0], [2, 5], x_init_value=x_init)
print("relu (float32) gradient of gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradGradFloat64(self):
with self.test_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5],
dtype=dtypes.float64,
name="x")
y = nn_ops.relu(x, name="relu")
z = gradients_impl.gradients(y, x)
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float64,
order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], z[0], [2, 5], x_init_value=x_init)
print("relu (float64) gradient of gradient err = ", err)
self.assertLess(err, 1e-10)
def testGradientScalar(self):
with self.test_session() as sess:
x = variables.Variable(100.)
y = nn_ops.relu(x)
loss = y**2
optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=0.25)
train_op = optimizer.minimize(loss)
sess.run(variables.global_variables_initializer())
sess.run(train_op)
self.assertAllClose(x.eval(), 50.0)
class Relu6Test(test.TestCase):
def _npRelu6(self, np_features):
sixes = np.copy(np_features)
sixes.fill(6.0)
return np.minimum(
np.maximum(np_features, np.zeros(np_features.shape)), sixes)
def testNpRelu6(self):
self.assertAllClose(
np.array([[0.0, 0.7, 0.0, 0.3, 6.0], [0.1, 0.0, 6.0, 0.0, 0.9]]),
self._npRelu6(
np.array([[-0.9, 0.7, -0.5, 0.3, 6.0], [0.1, -0.3, 6.5, -0.7, 0.9]
])))
def _testRelu6(self, np_features, use_gpu=False):
np_relu6 = self._npRelu6(np_features)
with self.test_session(use_gpu=use_gpu):
relu6 = nn_ops.relu6(np_features)
tf_relu6 = relu6.eval()
self.assertAllClose(np_relu6, tf_relu6)
self.assertShapeEqual(np_relu6, relu6)
def testNumbers(self):
for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
self._testRelu6(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=False)
if t in [np.float16, np.float, np.double]:
self._testRelu6(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=True)
# The gradient test for ReLU6 is a bit tricky as the derivative is
# not well defined at around zero and six and we want to avoid that
# in terms of input values.
def testGradientFloat32(self):
with self.test_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 6.1, 6.3, 6.5, 6.7, 6.9],
shape=[2, 5],
name="x")
y = nn_ops.relu6(x, name="relu6")
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [6.1, 6.3, 6.5, 6.7, 6.9]],
dtype=np.float32,
order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], y, [2, 5], x_init_value=x_init)
print("relu6 (float32) gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradientFloat64(self):
with self.test_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 6.1, 6.3, 6.5, 6.7, 6.9],
shape=[2, 5],
dtype=dtypes.float64,
name="x")
y = nn_ops.relu6(x, name="relu6")
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [6.1, 6.3, 6.5, 6.7, 6.9]],
dtype=np.float64,
order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], y, [2, 5], x_init_value=x_init)
print("relu6 (float64) gradient err = ", err)
self.assertLess(err, 1e-10)
class EluTest(test.TestCase):
def _npElu(self, np_features):
return np.where(np_features < 0, np.exp(np_features) - 1, np_features)
def testNpElu(self):
self.assertAllClose(
np.array([[-0.59343034025, 0.7, -0.39346934028, 0.3, -0.09516258196],
[0.1, -0.25918177931, 0.5, -0.5034146962, 0.9]]),
self._npElu(
np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7, 0.9]
])))
def _testElu(self, np_features, use_gpu=False):
np_elu = self._npElu(np_features)
with self.test_session(use_gpu=use_gpu):
elu = nn_ops.elu(np_features)
tf_elu = elu.eval()
self.assertAllClose(np_elu, tf_elu)
self.assertShapeEqual(np_elu, elu)
def testNumbers(self):
for t in [np.float16, np.float32, np.float64]:
self._testElu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=False)
self._testElu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=True)
def testGradientFloat32(self):
with self.test_session():
x_val = [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]]
x = constant_op.constant(x_val, name="x")
y = nn_ops.elu(x, name="elu")
x_init = np.asarray(x_val, dtype=np.float32, order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], y, [2, 5], x_init_value=x_init)
print("elu (float32) gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradientFloat64(self):
with self.test_session():
x_val = [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]]
x = constant_op.constant(x_val, dtype=dtypes.float64, name="x")
y = nn_ops.elu(x, name="elu")
x_init = np.asarray(x_val, dtype=np.float64, order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], y, [2, 5], x_init_value=x_init)
print("elu (float64) gradient err = ", err)
self.assertLess(err, 1e-6)
def testGradGrad(self):
with self.test_session():
x = array_ops.placeholder(dtype=dtypes.float32)
elu = nn_ops.elu(x)
g, = gradients_impl.gradients(elu, x)
gg, = gradients_impl.gradients(g, x)
for x_val in [-1, -0.5, 0.5, 1]:
err = np.abs(gg.eval(feed_dict={x: x_val}) - _elu_grad_grad(x_val))
self.assertLess(err, 1e-4)
def testGradGradFloat32(self):
with self.test_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5],
name="x")
y = nn_ops.elu(x, name="elu")
z = gradients_impl.gradients(y, x)
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float32,
order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], z[0], [2, 5], x_init_value=x_init)
print("elu (float32) gradient of gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradGradFloat64(self):
with self.test_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5],
dtype=dtypes.float64,
name="x")
y = nn_ops.elu(x, name="elu")
z = gradients_impl.gradients(y, x)
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float64,
order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], z[0], [2, 5], x_init_value=x_init)
print("elu (float64) gradient of gradient err = ", err)
self.assertLess(err, 1e-6)
class SeluTest(test.TestCase):
def _npSelu(self, np_features):
scale = 1.0507009873554804934193349852946
scale_alpha = 1.7580993408473768599402175208123
return np.where(np_features < 0, scale_alpha * (np.exp(np_features) - 1),
scale * np_features)
def testNpSelu(self):
self.assertAllClose(
np.array([[-1.0433095, 0.73549069, -0.6917582, 0.3152103 , -0.16730527],
[0.1050701 , -0.45566732, 0.5253505, -0.88505305, 0.9456309]]),
self._npSelu(
np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7, 0.9]
])))
def _testSelu(self, np_features, use_gpu=False):
np_selu = self._npSelu(np_features)
with self.test_session(use_gpu=use_gpu):
selu = nn_ops.selu(np_features)
tf_selu = selu.eval()
self.assertAllClose(np_selu, tf_selu)
self.assertShapeEqual(np_selu, selu)
def testNumbers(self):
for t in [np.float16, np.float32, np.float64]:
self._testSelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=False)
self._testSelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=True)
def testGradientFloat32(self):
with self.test_session():
x_val = [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]]
x = constant_op.constant(x_val, name="x")
y = nn_ops.selu(x, name="selu")
x_init = np.asarray(x_val, dtype=np.float32, order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], y, [2, 5], x_init_value=x_init)
print("selu (float32) gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradientFloat64(self):
with self.test_session():
x_val = [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]]
x = constant_op.constant(x_val, dtype=dtypes.float64, name="x")
y = nn_ops.selu(x, name="selu")
x_init = np.asarray(x_val, dtype=np.float64, order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], y, [2, 5], x_init_value=x_init)
print("selu (float64) gradient err = ", err)
self.assertLess(err, 1e-6)
def testGradGradFloat32(self):
with self.test_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5],
name="x")
y = nn_ops.selu(x, name="selu")
z = gradients_impl.gradients(y, x)
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float32,
order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], z[0], [2, 5], x_init_value=x_init)
print("selu (float32) gradient of gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradGradFloat64(self):
with self.test_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5],
dtype=dtypes.float64,
name="x")
y = nn_ops.selu(x, name="selu")
z = gradients_impl.gradients(y, x)
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float64,
order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], z[0], [2, 5], x_init_value=x_init)
print("selu (float64) gradient of gradient err = ", err)
self.assertLess(err, 1e-6)
class CreluTest(test.TestCase):
def testCreluShape(self):
f = random_ops.random_normal([50, 5, 7, 10])
t = nn_ops.crelu(f)
self.assertEqual([50, 5, 7, 20], t.get_shape())
def _testCrelu(self, np_features, use_gpu=False):
np_relu = np.maximum(np_features, np.zeros_like(np_features))
np_neg_relu = np.maximum(-np_features, np.zeros_like(np_features))
np_crelu = np.concatenate((np_relu, np_neg_relu),
len(np_features.shape) - 1)
with self.test_session(use_gpu=use_gpu):
crelu = nn_ops.crelu(np_features)
tf_relu = crelu.eval()
self.assertAllClose(np_crelu, tf_relu)
self.assertShapeEqual(np_crelu, crelu)
def testNumbers(self):
for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
self._testCrelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=False)
if t in [np.float16, np.float32, np.float64]:
self._testCrelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=True)
if __name__ == "__main__":
test.main()