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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 unittest
import numpy as np
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.platform import test
from tensorflow.python.platform import tf_logging as logging
class SoftmaxTest(test.TestCase):
def _npSoftmax(self, features, dim=-1, log=False):
if dim is -1:
dim = len(features.shape) - 1
one_only_on_dim = list(features.shape)
one_only_on_dim[dim] = 1
is_fp16 = features.dtype == np.float16
if is_fp16:
# Do the compute in fp32 and cast the input back to fp32.
features = features.astype(np.float32)
e = np.exp(features - np.reshape(
np.amax(
features, axis=dim), one_only_on_dim))
softmax = e / np.reshape(np.sum(e, axis=dim), one_only_on_dim)
if log:
res = np.log(softmax)
else:
res = softmax
if is_fp16:
res = res.astype(np.float16)
return res
def _testSoftmax(self, np_features, dim=-1, 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, dim=dim, log=log)
with self.test_session(use_gpu=use_gpu):
if log:
tf_softmax = nn_ops.log_softmax(np_features, axis=dim, name=name)
else:
tf_softmax = nn_ops.softmax(np_features, axis=dim, 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 dimension dim.
sum_along_dim = np.sum(out, axis=dim)
self.assertAllCloseAccordingToType(
np.ones(sum_along_dim.shape), sum_along_dim)
def _testAll(self, features):
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 _testOverflow(self, use_gpu=False):
if use_gpu:
type = np.float32 # pylint: disable=redefined-builtin
else:
type = np.float64 # pylint: disable=redefined-builtin
max = np.finfo(type).max # pylint: disable=redefined-builtin
features = np.array([[1., 1., 1., 1.], [max, 1., 2., 3.]]).astype(type)
with self.test_session(use_gpu=use_gpu):
tf_log_softmax = nn_ops.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))
@unittest.skipUnless(test.is_built_with_cuda(),
"Test only applicable when running on GPUs")
def testFloatGPU(self):
if test.is_gpu_available(cuda_only=True):
rows = [2**x + np.random.randint(0, 16) for x in range(1, 4)]
cols = [2**x + np.random.randint(0, 16) for x in range(1, 4)]
for row, col in zip(rows, cols):
logging.info("Testing softmax float dtype in shape [%d, %d]", row, col)
data = np.random.rand(row, col)
self._testAll(data.astype(np.float32))
def testHalf(self):
self._testAll(
np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float16))
@unittest.skipUnless(test.is_built_with_cuda(),
"Test only applicable when running on GPUs")
def testHalfGPU(self):
if test.is_gpu_available(cuda_only=True):
rows = [2**x + np.random.randint(0, 16) for x in range(1, 4)]
cols = [2**x + np.random.randint(0, 16) for x in range(1, 4)]
for row, col in zip(rows, cols):
logging.info("Testing softmax half dtype in shape [%d, %d]", row, col)
data = np.random.rand(row, col)
self._testAll(data.astype(np.float16))
def testDouble(self):
self._testSoftmax(
np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float64))
self._testOverflow()
def test1DTensorAsInput(self):
self._testSoftmax(
np.array([3., 2., 3., 9.]).astype(np.float64), use_gpu=False)
self._testOverflow(use_gpu=False)
def test1DTensorAsInputNoReshape(self):
self._testSoftmax(
np.array([3., 2., 3., 9.]).astype(np.float64), use_gpu=False)
self._testOverflow(use_gpu=False)
def test3DTensorAsInput(self):
self._testSoftmax(
np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
[[2., 3., 4., 5.], [6., 7., 8., 9.]],
[[5., 4., 3., 2.], [1., 2., 3., 4.]]]).astype(np.float32),
use_gpu=False)
self._testOverflow(use_gpu=False)
def test3DTensorAsInputNoReshape(self):
self._testSoftmax(
np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
[[2., 3., 4., 5.], [6., 7., 8., 9.]],
[[5., 4., 3., 2.], [1., 2., 3., 4.]]]).astype(np.float32),
use_gpu=False)
self._testOverflow(use_gpu=False)
def testAlongFirstDimension(self):
self._testSoftmax(
np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
[[2., 3., 4., 5.], [6., 7., 8., 9.]],
[[5., 4., 3., 2.], [1., 2., 3., 4.]]]).astype(np.float32),
dim=0,
use_gpu=False)
self._testOverflow(use_gpu=False)
def testAlongSecondDimension(self):
self._testSoftmax(
np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
[[2., 3., 4., 5.], [6., 7., 8., 9.]],
[[5., 4., 3., 2.], [1., 2., 3., 4.]]]).astype(np.float32),
dim=1,
use_gpu=False)
self._testOverflow(use_gpu=False)
def testShapeInference(self):
op = nn_ops.softmax([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
[[2., 3., 4., 5.], [6., 7., 8., 9.]],
[[5., 4., 3., 2.], [1., 2., 3., 4.]]])
self.assertEqual([3, 2, 4], op.get_shape())
def testEmptyInput(self):
with self.cached_session():
x = array_ops.placeholder(dtypes.float32, shape=[0, 3])
self.assertEqual(0, array_ops.size(x).eval())
# reshape would raise if logits is empty
with self.assertRaises(errors_impl.InvalidArgumentError):
nn_ops.softmax(x, axis=0).eval()
def testDimTooLarge(self):
with self.cached_session():
# Use placeholder to make sure we get runtime error instead of shape
# inference error.
dim = array_ops.placeholder_with_default(100, shape=[])
with self.assertRaises(errors_impl.InvalidArgumentError):
nn_ops.softmax([1., 2., 3., 4.], axis=dim).eval()
def testLargeDims(self):
# Make sure that we properly handle large inputs. See
# https://github.com/tensorflow/tensorflow/issues/4425 for details
for dims in [129, 256]:
ones = np.random.rand(dims, dims).astype(np.float32)
np_softmax = self._npSoftmax(ones)
for use_gpu in [True, False]:
with self.test_session(use_gpu=use_gpu) as sess:
x = array_ops.placeholder(dtypes.float32)
y = nn_ops.softmax(x)
tf_softmax = sess.run(y, feed_dict={x: ones})
self.assertAllClose(tf_softmax, np_softmax)
if __name__ == "__main__":
test.main()
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