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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 SoftmaxCrossEntropyWithLogits op."""
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 gen_nn_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
import tensorflow.python.ops.nn_grad # pylint: disable=unused-import
from tensorflow.python.platform import test
class XentTest(test.TestCase):
def _npXent(self, features, labels, dim=-1):
if dim is -1:
dim = len(features.shape) - 1
one_only_on_dim = list(features.shape)
one_only_on_dim[dim] = 1
e = np.exp(features - np.reshape(
np.amax(
features, axis=dim), one_only_on_dim))
probs = e / np.reshape(np.sum(e, axis=dim), one_only_on_dim)
bp = (probs - labels)
l = -np.sum(labels * np.log(probs + 1.0e-20), axis=dim)
return l, bp
def _testXent(self, np_features, np_labels, use_gpu=False):
np_loss, np_backprop = self._npXent(np_features, np_labels)
with self.test_session(use_gpu=use_gpu) as sess:
loss, backprop = gen_nn_ops._softmax_cross_entropy_with_logits(
np_features, np_labels)
tf_loss, tf_backprop = sess.run([loss, backprop])
self.assertAllCloseAccordingToType(np_loss, tf_loss)
self.assertAllCloseAccordingToType(np_backprop, tf_backprop)
def _testXentWrapper(self, np_features, np_labels, dim=-1, use_gpu=False):
np_loss, _ = self._npXent(np_features, np_labels, dim=dim)
with self.test_session(use_gpu=use_gpu) as sess:
loss = nn_ops.softmax_cross_entropy_with_logits(
labels=np_labels, logits=np_features, dim=dim)
tf_loss = sess.run(loss)
print("np_loss:", np_loss)
print("tf_loss:", tf_loss)
self.assertAllCloseAccordingToType(np_loss, tf_loss)
def _testAll(self, features, labels):
self._testXent(features, labels, use_gpu=False)
self._testXent(features, labels, use_gpu=True)
def _testSingleClass(self, use_gpu=False):
for dtype in np.float16, np.float32:
with self.test_session(use_gpu=use_gpu) as sess:
loss, backprop = gen_nn_ops._softmax_cross_entropy_with_logits(
np.array([[1.], [-1.], [0.]]).astype(dtype),
np.array([[-1.], [0.], [1.]]).astype(dtype))
tf_loss, tf_backprop = sess.run([loss, backprop])
self.assertAllClose([0.0, 0.0, 0.0], tf_loss)
self.assertAllClose([[2.0], [1.0], [0.0]], tf_backprop)
def testSingleClass(self):
self._testSingleClass(True)
self._testSingleClass(False)
def testRankTooLarge(self):
for dtype in np.float16, np.float32:
np_features = np.array(
[[[1., 1., 1., 1.]], [[1., 2., 3., 4.]]]).astype(dtype)
np_labels = np.array(
[[[0., 0., 0., 1.]], [[0., .5, .5, 0.]]]).astype(dtype)
self.assertRaisesRegexp(ValueError, "must be rank 2",
gen_nn_ops._softmax_cross_entropy_with_logits,
np_features, np_labels)
def testNpXent(self):
# We create 2 batches of logits for testing.
# batch 0 is the boring uniform distribution: 1, 1, 1, 1, with target 3.
# batch 1 has a bit of difference: 1, 2, 3, 4, with soft targets (1, 2).
features = [[1., 1., 1., 1.], [1., 2., 3., 4.]]
labels = [[0., 0., 0., 1.], [0., .5, .5, 0.]]
# For batch 0, we expect the uniform distribution: 0.25, 0.25, 0.25, 0.25
# With a hard target 3, the backprop is [0.25, 0.25, 0.25, -0.75]
# The loss for this batch is -log(0.25) = 1.386
#
# For batch 1, we have:
# exp(0) = 1
# exp(1) = 2.718
# exp(2) = 7.389
# exp(3) = 20.085
# SUM = 31.192
# So we have as probabilities:
# exp(0) / SUM = 0.032
# exp(1) / SUM = 0.087
# exp(2) / SUM = 0.237
# exp(3) / SUM = 0.644
# With a soft target (1, 2), the backprop is
# [0.032, 0.087 - 0.5 = -0.413, 0.237 - 0.5 = -0.263, 0.644]
# The loss for this batch is [0.5 * -log(0.087), 0.5 * -log(0.237)]
# = [1.3862, 1.9401]
np_loss, np_backprop = self._npXent(np.array(features), np.array(labels))
self.assertAllClose(
np.array([[0.25, 0.25, 0.25, -0.75],
[0.0321, -0.4129, -0.2632, 0.6439]]),
np_backprop,
rtol=1.e-3,
atol=1.e-3)
self.assertAllClose(
np.array([1.3862, 1.9401]), np_loss, rtol=1.e-3, atol=1.e-3)
def testShapeMismatch(self):
with self.test_session():
with self.assertRaises(ValueError):
gen_nn_ops._softmax_cross_entropy_with_logits(
[[0., 1.], [2., 3.]], [[0., 1., 0.], [1., 0., 0.]])
def testNotMatrix(self):
with self.test_session():
with self.assertRaises(ValueError):
gen_nn_ops._softmax_cross_entropy_with_logits([0., 1., 2., 3.],
[0., 1., 0., 1.])
def testHalf(self):
self._testAll(
np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float16),
np.array([[0., 0., 0., 1.], [0., .5, .5, 0.]]).astype(np.float16))
def testFloat(self):
self._testAll(
np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float32),
np.array([[0., 0., 0., 1.], [0., .5, .5, 0.]]).astype(np.float32))
def testDouble(self):
self._testAll(
np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float64),
np.array([[0., 0., 0., 1.], [0., .5, .5, 0.]]).astype(np.float64))
def testGradient(self):
with self.test_session() as sess:
l = constant_op.constant(
[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.5],
shape=[3, 4],
dtype=dtypes.float64,
name="l")
f = constant_op.constant(
[0.1, 0.2, 0.3, 0.4, 0.1, 0.4, 0.9, 1.6, 0.1, 0.8, 2.7, 6.4],
shape=[3, 4],
dtype=dtypes.float64,
name="f")
x = nn_ops.softmax_cross_entropy_with_logits(labels=l, logits=f,
name="xent")
err = gradient_checker.compute_gradient_error(f, [3, 4], x, [3])
# Check that no extra computation performed. When only first derivative is requested,
# second derivative must not be computed. So when there is no second derivative,
# there is no `BatchMatMul` op in the graph.
op_names = [op.op_def.name for op in sess.graph.get_operations() if op.op_def]
self.assertNotIn('BatchMatMul', op_names)
print("cross entropy gradient err = ", err)
self.assertLess(err, 5e-8)
def testGradientLabelWithV2(self):
with self.test_session():
l = constant_op.constant(
[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.5],
shape=[3, 4],
dtype=dtypes.float64,
name="l")
f = constant_op.constant(
[0.1, 0.2, 0.3, 0.4, 0.1, 0.4, 0.9, 1.6, 0.1, 0.8, 2.7, 6.4],
shape=[3, 4],
dtype=dtypes.float64,
name="f")
x = nn_ops.softmax_cross_entropy_with_logits_v2(labels=l, logits=f,
name="xent")
err = gradient_checker.compute_gradient_error(l, [3, 4], x, [3])
self.assertLess(err, 5e-8)
def testSecondGradient(self):
with self.test_session() as sess:
l = constant_op.constant([0.0, 0.0, 1.0/3, 0.0,
1.0/3, 0.0, 0.0, 0.0,
0.0, 0.5/3, 0.0, 0.5/3], shape=[12],
dtype=dtypes.float64, name="l")
f = constant_op.constant([0.1, 0.2, 0.3, 0.4,
0.1, 0.4, 0.9, 1.6,
0.1, 0.8, 2.7, 6.4], shape=[12],
dtype=dtypes.float64, name="f")
x = nn_ops.softmax_cross_entropy_with_logits(labels=l, logits=f,
name="xent")
loss = math_ops.reduce_sum(x)
gradients = gradients_impl.gradients(loss, [f])[0]
err = gradient_checker.compute_gradient_error(f, [12], gradients, [12])
# Check that second derivative is calculated.
# (it is equivalent to being `BatchMatMul` op in the graph because of implementation of xentropy grad)
op_names = [op.op_def.name for op in sess.graph.get_operations() if op.op_def]
self.assertIn('BatchMatMul', op_names)
print("cross entropy hessian err = ", err)
self.assertLess(err, 5e-8)
def testWrapper(self):
features = 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)
labels = np.array([[[0., 0., 0., 1.], [0., 1., 0., 0.]],
[[0., 0.5, 0.5, 0.], [0.5, 0.5, 0., 0.]],
[[0., 1., 0., 0.], [0., 0., 1., 0.]]]).astype(np.float32)
self._testXentWrapper(features, labels, dim=0, use_gpu=False)
self._testXentWrapper(features, labels, dim=0, use_gpu=True)
self._testXentWrapper(features, labels, dim=1, use_gpu=False)
self._testXentWrapper(features, labels, dim=1, use_gpu=True)
self._testXentWrapper(features, labels, dim=-1, use_gpu=False)
self._testXentWrapper(features, labels, dim=-1, use_gpu=True)
if __name__ == "__main__":
test.main()
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