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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 six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python import tf2
from tensorflow.python.compat import compat
from tensorflow.python.eager import backprop
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import gradient_checker_v2
from tensorflow.python.ops import math_ops
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):
np_relu = self._npRelu(np_features)
tf_relu = nn_ops.relu(np_features)
self.assertAllClose(np_relu, tf_relu)
self.assertShapeEqual(np_relu, tf_relu)
def testNumbersCPU(self):
for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
# Force execution on CPU even if a GPU kernel is available for the type.
with ops.device("/device:CPU:0"):
self._testRelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
def testNumbersGPU(self):
if not test.is_gpu_available():
self.skipTest("No GPU available")
for t in [np.float16, np.float32, np.float64]:
self._testRelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
def testReluInt8x4GoodShape(self):
if not test.is_gpu_available(cuda_only=True):
self.skipTest("No GPU available")
if test.is_built_with_rocm():
self.skipTest("ROCm does not support int8x4 type")
inputs = np.array([[-50, 7, 23, 0], [-1, -5, 6, 11]])
np_relu = self._npRelu(inputs)
tf_relu = nn_ops.relu(constant_op.constant(inputs, dtypes.qint8))
self.assertAllClose(np_relu, tf_relu)
self.assertShapeEqual(np_relu, tf_relu)
@test_util.disable_xla("b/123338077") # Passes with XLA
def testReluInt8x4BadShape(self):
if not test.is_gpu_available(cuda_only=True):
self.skipTest("No GPU available")
if test.is_built_with_rocm():
self.skipTest("ROCm does not support int8x4 type")
inputs = constant_op.constant(
np.array([[-50, 7, 23], [0, 1, -5], [6, -2, 11]]), dtypes.qint8)
with self.assertRaisesRegexp(
errors.InvalidArgumentError,
"Tensor size must be a multiple of 4 for Relu<qint8>. Got 9"):
self.evaluate(nn_ops.relu(inputs))
inputs = constant_op.constant(
np.array([1, -2, 3, -4, 5, -6, 7, -8, 9, -8, 7, -6, 5, -4, 3, -2, 1]),
dtypes.qint8)
with self.assertRaisesRegexp(
errors.InvalidArgumentError,
"Tensor size must be a multiple of 4 for Relu<qint8>. Got 17"):
self.evaluate(nn_ops.relu(inputs))
def testNoElement(self):
self._testRelu(np.array([[], []], dtype=np.float32))
# 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.cached_session():
x = 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_v2.max_error(
*gradient_checker_v2.compute_gradient(nn_ops.relu, [x]))
print("relu (float32) gradient err = ", err)
self.assertLess(err, 1e-4)
# The gradient for fp16 is inaccurate due to the low-precision.
# We compare the fp16 analytical gradient against their fp32 counterpart.
def testGradientFloat16(self):
def grad(x):
with backprop.GradientTape() as tape:
tape.watch(x)
y = nn_ops.l2_loss(nn_ops.relu(x))
return tape.gradient(y, x)
def f():
with test_util.use_gpu():
# Randomly construct a 1D shape from [1, 40)
shape = random_ops.random_uniform([1],
minval=1,
maxval=40,
dtype=dtypes.int32)
x32 = random_ops.random_uniform(shape, minval=-1, maxval=1)
x16 = math_ops.cast(x32, dtype=dtypes.float16)
return grad(x32), grad(x16)
# We're going to ensure that the fp16 and fp32 gradients
# are "close" to each other for ~100 random values.
#
# In TensorFlow 1.x, invoking f() (without eager execution enabled)
# would construct a graph. Instead of construct a graph with O(100) nodes,
# we construct a single graph to be executed ~100 times in a Session.
if not tf2.enabled():
d32_tensor, d16_tensor = f()
with self.cached_session() as sess:
f = lambda: sess.run([d32_tensor, d16_tensor])
# Repeat the experiment for 100 times. All tensor shapes and its tensor
# values are randomly generated for each run.
for _ in xrange(100):
d32, d16 = f()
self.assertAllClose(d32, d16, atol=3e-4)
def testGradientFloat64(self):
with self.cached_session():
x = 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_v2.max_error(
*gradient_checker_v2.compute_gradient(nn_ops.relu, [x]))
print("relu (float64) gradient err = ", err)
self.assertLess(err, 1e-10)
def testGradGradFloat32(self):
with self.cached_session():
def f(x):
assert x.dtype == dtypes.float32
with backprop.GradientTape() as tape:
tape.watch(x)
y = nn_ops.relu(x)
return tape.gradient(y, x)
x = 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_v2.max_error(
*gradient_checker_v2.compute_gradient(f, [x]))
print("relu (float32) gradient of gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradGradFloat64(self):
with self.cached_session():
def f(x):
assert x.dtype == dtypes.float64
with backprop.GradientTape() as tape:
tape.watch(x)
y = nn_ops.relu(x)
return tape.gradient(y, x)
x = 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_v2.max_error(
*gradient_checker_v2.compute_gradient(f, [x]))
print("relu (float64) gradient of gradient err = ", err)
self.assertLess(err, 1e-10)
def testGradientScalar(self):
x = variables.Variable(100.)
def loss():
return nn_ops.relu(x)**2
optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=0.25)
self.evaluate(variables.global_variables_initializer())
self.evaluate(optimizer.minimize(loss))
self.assertAllClose(x.read_value(), 50.0)
def testGradientNoElement(self):
with self.cached_session():
def f(x):
with backprop.GradientTape() as tape:
tape.watch(x)
y = nn_ops.relu(x)
return tape.gradient(y, x)
x = np.asarray([[], []], dtype=np.float32)
z = list(gradient_checker_v2.compute_gradient(f, [x]))[0][0]
self.assertAllEqual(z, np.reshape(x, (0, 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):
np_relu6 = self._npRelu6(np_features)
tf_relu6 = nn_ops.relu6(np_features)
self.assertAllClose(np_relu6, tf_relu6)
self.assertShapeEqual(np_relu6, tf_relu6)
def testNumbersCPU(self):
for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
# Force execution on CPU even if a GPU kernel is available for the type.
with ops.device("/device:CPU:0"):
self._testRelu6(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
def testNumbersGPU(self):
if not test.is_gpu_available():
self.skipTest("No GPU available")
for t in [np.float16, np.float, np.double]:
self._testRelu6(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
# 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.cached_session():
x = 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_v2.max_error(
*gradient_checker_v2.compute_gradient(nn_ops.relu6, [x]))
print("relu6 (float32) gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradientFloat64(self):
with self.cached_session():
x = 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_v2.max_error(
*gradient_checker_v2.compute_gradient(nn_ops.relu6, [x]))
print("relu6 (float64) gradient err = ", err)
self.assertLess(err, 1e-10)
class LeakyReluTest(test.TestCase):
def _npLeakyRelu(self, np_features, alpha=0.1):
return np.maximum(np_features, alpha * np_features)
def testNpLeakyRelu(self):
self.assertAllClose(
np.array([[-0.09, 0.7, -0.05, 0.3, -0.01],
[0.1, -0.03, 0.5, -0.07, 0.9]]),
self._npLeakyRelu(
np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7,
0.9]]),
alpha=0.1))
def _testLeakyRelu(self, np_features, alpha):
np_leaky_relu = self._npLeakyRelu(np_features, alpha)
tf_leaky_relu = nn_ops.leaky_relu(np_features, alpha)
self.assertAllClose(np_leaky_relu, tf_leaky_relu)
self.assertShapeEqual(np_leaky_relu, tf_leaky_relu)
def testNumbersCPU(self):
for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
# Force execution on CPU even if a GPU kernel is available for the type.
with ops.device("/device:CPU:0"):
self._testLeakyRelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
alpha=0.2)
def testNumbersGPU(self):
if not test.is_gpu_available():
self.skipTest("No GPU available")
for t in [np.float16, np.float32, np.float64]:
self._testLeakyRelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
alpha=0.1)
# The gradient test for Leaky 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.cached_session():
x = 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_v2.max_error(
*gradient_checker_v2.compute_gradient(nn_ops.leaky_relu, [x]))
print("leaky_relu (float32) gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradientFloat64(self):
with self.cached_session():
x = 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_v2.max_error(
*gradient_checker_v2.compute_gradient(nn_ops.leaky_relu, [x]))
print("leaky_relu (float64) gradient err = ", err)
self.assertLess(err, 1e-10)
def testGradGradFloat32(self):
with compat.forward_compatibility_horizon(2018, 11, 2):
with self.cached_session():
def f(x):
assert x.dtype == dtypes.float32
with backprop.GradientTape() as tape:
tape.watch(x)
y = nn_ops.leaky_relu(x)
return tape.gradient(y, x)
x = 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_v2.max_error(
*gradient_checker_v2.compute_gradient(f, [x]))
print("leaky_relu (float32) gradient of gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradGradFloat64(self):
with compat.forward_compatibility_horizon(2018, 11, 2):
with self.cached_session():
def f(x):
assert x.dtype == dtypes.float64
with backprop.GradientTape() as tape:
tape.watch(x)
y = nn_ops.leaky_relu(x)
return tape.gradient(y, x)
x = 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_v2.max_error(
*gradient_checker_v2.compute_gradient(f, [x]))
print("leaky_relu (float64) gradient of gradient err = ", err)
self.assertLess(err, 1e-10)
def testGradientScalar(self):
x = variables.Variable(-100.)
def loss():
return nn_ops.leaky_relu(x, 0.05)**2
optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=0.2)
self.evaluate(variables.global_variables_initializer())
self.evaluate(optimizer.minimize(loss))
self.assertAllClose(x.read_value(), -99.9)
def testUnexpectedAlphaValue(self):
self.assertAllClose(
np.array([[-9.0, 0.7, -5.0, 0.3, -0.1], [0.1, -3.0, 0.5, -27.0, 0.9]]),
nn_ops.leaky_relu(
np.array([[-0.9, 0.7, -0.5, 0.3, -0.01],
[0.1, -0.3, 0.5, -2.7, 0.9]]),
alpha=10))
self.assertAllClose(
np.array([[9.0, 0.7, 5.0, 0.3, 0.1], [0.1, 3.0, 0.5, 27.0, 0.9]]),
nn_ops.leaky_relu(
np.array([[-0.9, 0.7, -0.5, 0.3, -0.01],
[0.1, -0.3, 0.5, -2.7, 0.9]]),
alpha=-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):
np_elu = self._npElu(np_features)
tf_elu = nn_ops.elu(np_features)
self.assertAllCloseAccordingToType(np_elu, tf_elu)
self.assertShapeEqual(np_elu, tf_elu)
def testNumbersCPU(self):
for t in [np.float16, np.float32, np.float64]:
# Force execution on CPU even if a GPU kernel is available for the type.
with ops.device("/device:CPU:0"):
self._testElu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
def testNumbersGPU(self):
if not test.is_gpu_available():
self.skipTest("No GPU available")
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))
def testGradientFloat32(self):
with self.cached_session():
x_val = [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]]
x = np.asarray(x_val, dtype=np.float32, order="F")
err = gradient_checker_v2.max_error(
*gradient_checker_v2.compute_gradient(nn_ops.elu, [x]))
print("elu (float32) gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradientFloat64(self):
with self.cached_session():
x_val = [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]]
x = np.asarray(x_val, dtype=np.float64, order="F")
err = gradient_checker_v2.max_error(
*gradient_checker_v2.compute_gradient(nn_ops.elu, [x]))
print("elu (float64) gradient err = ", err)
self.assertLess(err, 1e-6)
def testGradGrad(self):
with self.cached_session():
def f(x):
with backprop.GradientTape(persistent=True) as tape:
tape.watch(x)
y = nn_ops.elu(x)
dy = tape.gradient(y, x)
return tape.gradient(dy, x)
for x in [-1., -0.5, 0.5, 1.]:
got = self.evaluate(f(constant_op.constant(x)))
want = _elu_grad_grad(x)
err = np.abs(got - want)
self.assertLess(err, 1e-4)
def testGradGradFloat32(self):
with self.cached_session():
def f(x):
assert x.dtype == dtypes.float32
with backprop.GradientTape() as tape:
tape.watch(x)
y = nn_ops.elu(x)
return tape.gradient(y, x)
x = 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_v2.max_error(
*gradient_checker_v2.compute_gradient(f, [x]))
print("elu (float32) gradient of gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradGradFloat64(self):
with self.cached_session():
def f(x):
assert x.dtype == dtypes.float64
with backprop.GradientTape() as tape:
tape.watch(x)
y = nn_ops.elu(x)
return tape.gradient(y, x)
x = 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_v2.max_error(
*gradient_checker_v2.compute_gradient(f, [x]))
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):
np_selu = self._npSelu(np_features)
tf_selu = nn_ops.selu(np_features)
self.assertAllCloseAccordingToType(np_selu, tf_selu)
self.assertShapeEqual(np_selu, tf_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))
# Force executed on CPU in case GPU kernels are available.
with ops.device("/device:CPU:0"):
self._testSelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
def testGradientFloat32(self):
with self.cached_session():
x_val = [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]]
x = np.asarray(x_val, dtype=np.float32, order="F")
err = gradient_checker_v2.max_error(
*gradient_checker_v2.compute_gradient(nn_ops.selu, [x]))
print("selu (float32) gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradientFloat64(self):
with self.cached_session():
x_val = [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]]
x = np.asarray(x_val, dtype=np.float64, order="F")
err = gradient_checker_v2.max_error(
*gradient_checker_v2.compute_gradient(nn_ops.selu, [x]))
print("selu (float64) gradient err = ", err)
self.assertLess(err, 1e-6)
def testGradGradFloat32(self):
with self.cached_session():
def f(x):
assert x.dtype == dtypes.float32
with backprop.GradientTape() as tape:
tape.watch(x)
y = nn_ops.selu(x)
return tape.gradient(y, x)
x = 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_v2.max_error(
*gradient_checker_v2.compute_gradient(f, [x]))
print("selu (float32) gradient of gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradGradFloat64(self):
with self.cached_session():
def f(x):
assert x.dtype == dtypes.float64
with backprop.GradientTape() as tape:
tape.watch(x)
y = nn_ops.selu(x)
return tape.gradient(y, x)
x = 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_v2.max_error(
*gradient_checker_v2.compute_gradient(f, [x]))
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):
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)
tf_crelu = nn_ops.crelu(np_features)
self.assertAllClose(np_crelu, tf_crelu)
self.assertShapeEqual(np_crelu, tf_crelu)
def testNumbersCPU(self):
for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
# Force execution on CPU even if a GPU kernel is available for the type.
with ops.device("/device:CPU:0"):
self._testCrelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
def testNumbersGPU(self):
if not test.is_gpu_available():
self.skipTest("No GPU available")
for t in [np.float16, np.float32, np.float64]:
self._testCrelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
def testNumbersWithAxis0(self):
tf_crelu = nn_ops.crelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]), axis=0)
np_crelu = np.array([[0, 7, 0, 3, 0], [1, 0, 5, 0, 9], [9, 0, 5, 0, 1],
[0, 3, 0, 7, 0]])
self.assertAllEqual(np_crelu, tf_crelu)
def testNumbersWithAxis1(self):
tf_crelu = nn_ops.crelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]), axis=1)
np_crelu = np.array([[0, 7, 0, 3, 0, 9, 0, 5, 0, 1],
[1, 0, 5, 0, 9, 0, 3, 0, 7, 0]])
self.assertAllEqual(np_crelu, tf_crelu)
if __name__ == "__main__":
test.main()
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