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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 SoftmaxOp and LogSoftmaxOp."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import numpy as np
import tensorflow as tf
class SoftmaxTest(tf.test.TestCase):
def _npSoftmax(self, features, log=False):
batch_dim = 0
class_dim = 1
batch_size = features.shape[batch_dim]
e = np.exp(features -
np.reshape(np.amax(features, axis=class_dim), [batch_size, 1]))
softmax = e / np.reshape(np.sum(e, axis=class_dim), [batch_size, 1])
if log:
return np.log(softmax)
else:
return softmax
def _testSoftmax(self, np_features, log=False, use_gpu=False):
# A previous version of the code checked the op name rather than the op type
# to distinguish between log and non-log. Use an arbitrary name to catch
# this bug in future.
name = "arbitrary"
np_softmax = self._npSoftmax(np_features, log=log)
with self.test_session(use_gpu=use_gpu):
if log:
tf_softmax = tf.nn.log_softmax(np_features, name=name)
else:
tf_softmax = tf.nn.softmax(np_features, name=name)
out = tf_softmax.eval()
self.assertAllCloseAccordingToType(np_softmax, out)
self.assertShapeEqual(np_softmax, tf_softmax)
if not log:
# Bonus check: the softmaxes should add to one in each
# batch element.
self.assertAllCloseAccordingToType(np.ones(out.shape[0]),
np.sum(out, axis=1))
def _testAll(self, features):
self._testSoftmax(features, use_gpu=False)
self._testSoftmax(features, log=True, use_gpu=False)
self._testSoftmax(features, use_gpu=True)
self._testSoftmax(features, log=True, use_gpu=True)
self._testOverflow(use_gpu=True)
def testNpSoftmax(self):
features = [[1., 1., 1., 1.], [1., 2., 3., 4.]]
# Batch 0: All exps are 1. The expected result is
# Softmaxes = [0.25, 0.25, 0.25, 0.25]
# LogSoftmaxes = [-1.386294, -1.386294, -1.386294, -1.386294]
#
# Batch 1:
# exps = [1., 2.718, 7.389, 20.085]
# sum = 31.192
# Softmaxes = exps / sum = [0.0320586, 0.08714432, 0.23688282, 0.64391426]
# LogSoftmaxes = [-3.44019 , -2.44019 , -1.44019 , -0.44019]
np_sm = self._npSoftmax(np.array(features))
self.assertAllClose(
np.array([[0.25, 0.25, 0.25, 0.25],
[0.0320586, 0.08714432, 0.23688282, 0.64391426]]),
np_sm,
rtol=1.e-5, atol=1.e-5)
np_lsm = self._npSoftmax(np.array(features), log=True)
self.assertAllClose(
np.array([[-1.386294, -1.386294, -1.386294, -1.386294],
[-3.4401897, -2.4401897, -1.4401897, -0.4401897]]),
np_lsm,
rtol=1.e-5, atol=1.e-5)
def testShapeMismatch(self):
with self.assertRaises(ValueError):
tf.nn.softmax([0., 1., 2., 3.])
with self.assertRaises(ValueError):
tf.nn.log_softmax([0., 1., 2., 3.])
def _testOverflow(self, use_gpu=False):
if use_gpu:
type = np.float32
else:
type = np.float64
max = np.finfo(type).max
features = np.array(
[[1., 1., 1., 1.],
[max, 1., 2., 3.]]).astype(type)
with self.test_session(use_gpu=use_gpu):
tf_log_softmax = tf.nn.log_softmax(features)
out = tf_log_softmax.eval()
self.assertAllClose(
np.array([[-1.386294, -1.386294, -1.386294, -1.386294],
[0, -max, -max, -max]]),
out,
rtol=1.e-5, atol=1.e-5)
def testFloat(self):
self._testAll(
np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float32))
def testHalf(self):
self._testAll(
np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float16))
def testDouble(self):
self._testSoftmax(
np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float64),
use_gpu=False)
self._testOverflow(use_gpu=False)
def testEmpty(self):
with self.test_session():
x = tf.constant([[]], shape=[0, 3])
self.assertEqual(0, tf.size(x).eval())
expected_y = np.array([]).reshape(0, 3)
np.testing.assert_array_equal(expected_y, tf.nn.softmax(x).eval())
if __name__ == "__main__":
tf.test.main()