645 lines
22 KiB
Python
645 lines
22 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for Relu and ReluGrad."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from six.moves import xrange # pylint: disable=redefined-builtin
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from tensorflow.python import tf2
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from tensorflow.python.eager import backprop
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import errors
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import gradient_checker_v2
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops import variables
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import tensorflow.python.ops.nn_grad # pylint: disable=unused-import
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from tensorflow.python.platform import test
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from tensorflow.python.training import gradient_descent
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def _elu_grad_grad(activation):
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if activation < 0:
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return np.exp(activation)
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return 0
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class ReluTest(test.TestCase):
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def _npRelu(self, np_features):
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return np.maximum(np_features, np.zeros(np_features.shape))
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def testNpRelu(self):
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self.assertAllClose(
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np.array([[0.0, 0.7, 0.0, 0.3, 0.0], [0.1, 0.0, 0.5, 0.0, 0.9]]),
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self._npRelu(
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np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7,
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0.9]])))
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def _testRelu(self, np_features):
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np_relu = self._npRelu(np_features)
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tf_relu = nn_ops.relu(np_features)
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self.assertAllClose(np_relu, tf_relu)
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self.assertShapeEqual(np_relu, tf_relu)
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def testNumbersCPU(self):
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for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
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# Force execution on CPU even if a GPU kernel is available for the type.
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with ops.device("/device:CPU:0"):
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self._testRelu(
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np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
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def testNumbersGPU(self):
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if not test.is_gpu_available():
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self.skipTest("No GPU available")
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for t in [np.float16, np.float32, np.float64]:
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self._testRelu(
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np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
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def testReluInt8x4GoodShape(self):
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if not test.is_gpu_available(cuda_only=True):
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self.skipTest("No GPU available")
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inputs = np.array([[-50, 7, 23, 0], [-1, -5, 6, 11]])
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np_relu = self._npRelu(inputs)
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tf_relu = nn_ops.relu(constant_op.constant(inputs, dtypes.qint8))
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self.assertAllClose(np_relu, tf_relu)
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self.assertShapeEqual(np_relu, tf_relu)
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@test_util.disable_xla("b/123338077") # Passes with XLA
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def testReluInt8x4BadShape(self):
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if not test.is_gpu_available(cuda_only=True):
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self.skipTest("No GPU available")
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inputs = constant_op.constant(
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np.array([[-50, 7, 23], [0, 1, -5], [6, -2, 11]]), dtypes.qint8)
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with self.assertRaisesRegexp(
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errors.InvalidArgumentError,
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"Tensor size must be a multiple of 4 for Relu<qint8>. Got 9"):
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self.evaluate(nn_ops.relu(inputs))
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inputs = constant_op.constant(
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np.array([1, -2, 3, -4, 5, -6, 7, -8, 9, -8, 7, -6, 5, -4, 3, -2, 1]),
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dtypes.qint8)
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with self.assertRaisesRegexp(
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errors.InvalidArgumentError,
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"Tensor size must be a multiple of 4 for Relu<qint8>. Got 17"):
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self.evaluate(nn_ops.relu(inputs))
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def testNoElement(self):
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self._testRelu(np.array([[], []], dtype=np.float32))
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# The gradient test for ReLU is a bit tricky as the derivative is not well
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# defined at around zero and we want to avoid that in terms of input values.
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def testGradientFloat32(self):
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with self.cached_session():
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x = np.asarray(
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[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
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dtype=np.float32,
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order="F")
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err = gradient_checker_v2.max_error(
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*gradient_checker_v2.compute_gradient(nn_ops.relu, [x]))
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self.assertLess(err, 1e-4)
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# The gradient for fp16 is inaccurate due to the low-precision.
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# We compare the fp16 analytical gradient against their fp32 counterpart.
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def testGradientFloat16(self):
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def grad(x):
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with backprop.GradientTape() as tape:
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tape.watch(x)
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y = nn_ops.l2_loss(nn_ops.relu(x))
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return tape.gradient(y, x)
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def f():
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with test_util.use_gpu():
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# Randomly construct a 1D shape from [1, 40)
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shape = random_ops.random_uniform([1],
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minval=1,
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maxval=40,
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dtype=dtypes.int32)
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x32 = random_ops.random_uniform(shape, minval=-1, maxval=1)
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x16 = math_ops.cast(x32, dtype=dtypes.float16)
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return grad(x32), grad(x16)
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# We're going to ensure that the fp16 and fp32 gradients
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# are "close" to each other for ~100 random values.
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#
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# In TensorFlow 1.x, invoking f() (without eager execution enabled)
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# would construct a graph. Instead of construct a graph with O(100) nodes,
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# we construct a single graph to be executed ~100 times in a Session.
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if not tf2.enabled():
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d32_tensor, d16_tensor = f()
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with self.cached_session() as sess:
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f = lambda: sess.run([d32_tensor, d16_tensor])
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# Repeat the experiment for 100 times. All tensor shapes and its tensor
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# values are randomly generated for each run.
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for _ in xrange(100):
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d32, d16 = f()
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self.assertAllClose(d32, d16, atol=3e-4)
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def testGradientFloat64(self):
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with self.cached_session():
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x = np.asarray(
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[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
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dtype=np.float64,
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order="F")
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err = gradient_checker_v2.max_error(
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*gradient_checker_v2.compute_gradient(nn_ops.relu, [x]))
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self.assertLess(err, 1e-10)
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def testGradGradFloat32(self):
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with self.cached_session():
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def f(x):
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assert x.dtype == dtypes.float32
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with backprop.GradientTape() as tape:
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tape.watch(x)
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y = nn_ops.relu(x)
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return tape.gradient(y, x)
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x = np.asarray(
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[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
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dtype=np.float32,
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order="F")
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err = gradient_checker_v2.max_error(
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*gradient_checker_v2.compute_gradient(f, [x]))
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self.assertLess(err, 1e-4)
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def testGradGradFloat64(self):
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with self.cached_session():
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def f(x):
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assert x.dtype == dtypes.float64
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with backprop.GradientTape() as tape:
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tape.watch(x)
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y = nn_ops.relu(x)
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return tape.gradient(y, x)
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x = np.asarray(
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[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
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dtype=np.float64,
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order="F")
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err = gradient_checker_v2.max_error(
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*gradient_checker_v2.compute_gradient(f, [x]))
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self.assertLess(err, 1e-10)
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def testGradientScalar(self):
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x = variables.Variable(100.)
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def loss():
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return nn_ops.relu(x)**2
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optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=0.25)
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self.evaluate(variables.global_variables_initializer())
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self.evaluate(optimizer.minimize(loss))
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self.assertAllClose(x.read_value(), 50.0)
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def testGradientNoElement(self):
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with self.cached_session():
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def f(x):
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with backprop.GradientTape() as tape:
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tape.watch(x)
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y = nn_ops.relu(x)
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return tape.gradient(y, x)
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x = np.asarray([[], []], dtype=np.float32)
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z = list(gradient_checker_v2.compute_gradient(f, [x]))[0][0]
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self.assertAllEqual(z, np.reshape(x, (0, 0)))
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class Relu6Test(test.TestCase):
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def _npRelu6(self, np_features):
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sixes = np.copy(np_features)
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sixes.fill(6.0)
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return np.minimum(
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np.maximum(np_features, np.zeros(np_features.shape)), sixes)
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def testNpRelu6(self):
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self.assertAllClose(
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np.array([[0.0, 0.7, 0.0, 0.3, 6.0], [0.1, 0.0, 6.0, 0.0, 0.9]]),
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self._npRelu6(
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np.array([[-0.9, 0.7, -0.5, 0.3, 6.0], [0.1, -0.3, 6.5, -0.7,
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0.9]])))
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def _testRelu6(self, np_features):
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np_relu6 = self._npRelu6(np_features)
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tf_relu6 = nn_ops.relu6(np_features)
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self.assertAllClose(np_relu6, tf_relu6)
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self.assertShapeEqual(np_relu6, tf_relu6)
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def testNumbersCPU(self):
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for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
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# Force execution on CPU even if a GPU kernel is available for the type.
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with ops.device("/device:CPU:0"):
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self._testRelu6(
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np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
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def testNumbersGPU(self):
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if not test.is_gpu_available():
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self.skipTest("No GPU available")
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for t in [np.float16, np.float, np.double]:
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self._testRelu6(
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np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
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# The gradient test for ReLU6 is a bit tricky as the derivative is
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# not well defined at around zero and six and we want to avoid that
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# in terms of input values.
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def testGradientFloat32(self):
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with self.cached_session():
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x = np.asarray(
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[[-0.9, -0.7, -0.5, -0.3, -0.1], [6.1, 6.3, 6.5, 6.7, 6.9]],
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dtype=np.float32,
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order="F")
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err = gradient_checker_v2.max_error(
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*gradient_checker_v2.compute_gradient(nn_ops.relu6, [x]))
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self.assertLess(err, 1e-4)
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def testGradientFloat64(self):
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with self.cached_session():
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x = np.asarray(
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[[-0.9, -0.7, -0.5, -0.3, -0.1], [6.1, 6.3, 6.5, 6.7, 6.9]],
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dtype=np.float64,
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order="F")
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err = gradient_checker_v2.max_error(
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*gradient_checker_v2.compute_gradient(nn_ops.relu6, [x]))
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self.assertLess(err, 1e-10)
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class LeakyReluTest(test.TestCase):
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def _npLeakyRelu(self, np_features, alpha=0.1):
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return np.maximum(np_features, alpha * np_features)
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def testNpLeakyRelu(self):
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self.assertAllClose(
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np.array([[-0.09, 0.7, -0.05, 0.3, -0.01],
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[0.1, -0.03, 0.5, -0.07, 0.9]]),
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self._npLeakyRelu(
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np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7,
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0.9]]),
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alpha=0.1))
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def _testLeakyRelu(self, np_features, alpha):
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np_leaky_relu = self._npLeakyRelu(np_features, alpha)
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tf_leaky_relu = nn_ops.leaky_relu(np_features, alpha)
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self.assertAllClose(np_leaky_relu, tf_leaky_relu)
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self.assertShapeEqual(np_leaky_relu, tf_leaky_relu)
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def testNumbersCPU(self):
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for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
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# Force execution on CPU even if a GPU kernel is available for the type.
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with ops.device("/device:CPU:0"):
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self._testLeakyRelu(
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np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
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alpha=0.2)
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def testNumbersGPU(self):
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if not test.is_gpu_available():
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self.skipTest("No GPU available")
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for t in [np.float16, np.float32, np.float64]:
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self._testLeakyRelu(
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np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
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alpha=0.1)
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# The gradient test for Leaky ReLU is a bit tricky as the derivative is not
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# well defined at around zero and we want to avoid that in terms of input
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# values.
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def testGradientFloat32(self):
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with self.cached_session():
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x = np.asarray(
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[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
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dtype=np.float32,
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order="F")
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err = gradient_checker_v2.max_error(
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*gradient_checker_v2.compute_gradient(nn_ops.leaky_relu, [x]))
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self.assertLess(err, 1e-4)
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def testGradientFloat64(self):
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with self.cached_session():
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x = np.asarray(
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[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
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dtype=np.float64,
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order="F")
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err = gradient_checker_v2.max_error(
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*gradient_checker_v2.compute_gradient(nn_ops.leaky_relu, [x]))
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self.assertLess(err, 1e-10)
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def testGradGradFloat32(self):
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with self.cached_session():
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def f(x):
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assert x.dtype == dtypes.float32
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with backprop.GradientTape() as tape:
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tape.watch(x)
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y = nn_ops.leaky_relu(x)
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return tape.gradient(y, x)
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x = np.asarray(
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[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
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dtype=np.float32,
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order="F")
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err = gradient_checker_v2.max_error(
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*gradient_checker_v2.compute_gradient(f, [x]))
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self.assertLess(err, 1e-4)
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def testGradGradFloat64(self):
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with self.cached_session():
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def f(x):
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assert x.dtype == dtypes.float64
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with backprop.GradientTape() as tape:
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tape.watch(x)
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y = nn_ops.leaky_relu(x)
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return tape.gradient(y, x)
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x = np.asarray(
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[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
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dtype=np.float64,
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order="F")
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err = gradient_checker_v2.max_error(
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*gradient_checker_v2.compute_gradient(f, [x]))
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self.assertLess(err, 1e-10)
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def testGradientScalar(self):
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x = variables.Variable(-100.)
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def loss():
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return nn_ops.leaky_relu(x, 0.05)**2
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optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=0.2)
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self.evaluate(variables.global_variables_initializer())
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self.evaluate(optimizer.minimize(loss))
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self.assertAllClose(x.read_value(), -99.9)
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def testUnexpectedAlphaValue(self):
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self.assertAllClose(
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np.array([[-9.0, 0.7, -5.0, 0.3, -0.1], [0.1, -3.0, 0.5, -27.0, 0.9]]),
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nn_ops.leaky_relu(
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np.array([[-0.9, 0.7, -0.5, 0.3, -0.01],
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[0.1, -0.3, 0.5, -2.7, 0.9]]),
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alpha=10))
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self.assertAllClose(
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np.array([[9.0, 0.7, 5.0, 0.3, 0.1], [0.1, 3.0, 0.5, 27.0, 0.9]]),
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nn_ops.leaky_relu(
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np.array([[-0.9, 0.7, -0.5, 0.3, -0.01],
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[0.1, -0.3, 0.5, -2.7, 0.9]]),
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alpha=-10))
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class EluTest(test.TestCase):
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def _npElu(self, np_features):
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return np.where(np_features < 0, np.exp(np_features) - 1, np_features)
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def testNpElu(self):
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self.assertAllClose(
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np.array([[-0.59343034025, 0.7, -0.39346934028, 0.3, -0.09516258196],
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[0.1, -0.25918177931, 0.5, -0.5034146962, 0.9]]),
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self._npElu(
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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]))
|
|
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]))
|
|
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]))
|
|
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]))
|
|
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]))
|
|
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]))
|
|
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]))
|
|
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]))
|
|
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()
|