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# Copyright 2016 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 tf upgrader."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import shutil
import tempfile
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import test_util
from tensorflow.python.platform import test as test_lib
class TestUpgrade(test_util.TensorFlowTestCase):
"""Test various APIs that have been changed in 1.0.
This test will not run in current TensorFlow, but did run in 0.11.
This file is intended to be converted by a genrule() that uses the converter
so that a 1.0 compatible version of this file is generated. That is run as
a unit test if the converter is successful.
"""
def testArgRenames(self):
with self.test_session():
a = [[1., 2., 3.], [4., 5., 6.]]
b = [[True, False, False], [False, True, True]]
dim0 = [1]
dim1 = [1]
self.assertAllEqual(
tf.reduce_any(
b, reduction_indices=dim0).eval(), [True, True])
self.assertAllEqual(
tf.reduce_all(
b, reduction_indices=[0]).eval(), [False, False, False])
self.assertAllEqual(
tf.reduce_all(
b, reduction_indices=dim1).eval(), [False, False])
self.assertAllEqual(
tf.reduce_sum(
a, reduction_indices=[1]).eval(), [6., 15.])
self.assertAllEqual(
tf.reduce_sum(
a, reduction_indices=[0, 1]).eval(), 21.0)
self.assertAllEqual(tf.reduce_sum(a, [0, 1]).eval(), 21.0)
self.assertAllEqual(
tf.reduce_prod(
a, reduction_indices=[1]).eval(), [6., 120.])
self.assertAllEqual(
tf.reduce_prod(
a, reduction_indices=[0, 1]).eval(), 720.0)
self.assertAllEqual(tf.reduce_prod(a, [0, 1]).eval(), 720.0)
self.assertAllEqual(
tf.reduce_mean(
a, reduction_indices=[1]).eval(), [2., 5.])
self.assertAllEqual(
tf.reduce_mean(
a, reduction_indices=[0, 1]).eval(), 3.5)
self.assertAllEqual(tf.reduce_mean(a, [0, 1]).eval(), 3.5)
self.assertAllEqual(
tf.reduce_min(
a, reduction_indices=[1]).eval(), [1., 4.])
self.assertAllEqual(
tf.reduce_min(
a, reduction_indices=[0, 1]).eval(), 1.0)
self.assertAllEqual(tf.reduce_min(a, [0, 1]).eval(), 1.0)
self.assertAllEqual(
tf.reduce_max(
a, reduction_indices=[1]).eval(), [3., 6.])
self.assertAllEqual(
tf.reduce_max(
a, reduction_indices=[0, 1]).eval(), 6.0)
self.assertAllEqual(tf.reduce_max(a, [0, 1]).eval(), 6.0)
self.assertAllClose(tf.reduce_logsumexp(a, reduction_indices=[1]).eval(),
[3.40760589, 6.40760612])
self.assertAllClose(
tf.reduce_logsumexp(a, reduction_indices=[0, 1]).eval(),
6.45619344711)
self.assertAllClose(
tf.reduce_logsumexp(a, [0, 1]).eval(), 6.45619344711)
self.assertAllEqual(
tf.expand_dims([[1, 2], [3, 4]], dim=1).eval(),
[[[1, 2]], [[3, 4]]])
def testArgMinMax(self):
with self.test_session():
self.assertAllEqual(
tf.argmin([[1, 2, 3], [4, 1, 0]], dimension=1).eval(),
[0, 2])
self.assertAllEqual(
tf.argmin([[1, 2, 3], [4, 1, 0]], dimension=0).eval(),
[0, 1, 1])
self.assertAllEqual(
tf.argmax([[1, 2, 3], [4, 1, 0]], dimension=1).eval(),
[2, 0])
self.assertAllEqual(
tf.argmax([[1, 2, 3], [4, 1, 0]], dimension=0).eval(),
[1, 0, 0])
def testExpandAndSqueeze(self):
with self.test_session():
# TODO(aselle): sparse_split, sparse_reduce_sum,
# sparse_reduce_sum_sparse, reduce_join
a = [[1, 2, 3]]
self.assertAllEqual(tf.expand_dims(tf.squeeze(a, [0]), 0).eval(),
a)
self.assertAllEqual(tf.squeeze(tf.expand_dims(a, 1), [1]).eval(),
a)
self.assertAllEqual(
tf.expand_dims(
tf.squeeze(
[[1, 2, 3]], squeeze_dims=[0]), dim=0).eval(),
a)
self.assertAllEqual(
tf.squeeze(
tf.expand_dims(
[[1, 2, 3]], dim=1), squeeze_dims=[1]).eval(),
a)
self.assertAllEqual(
tf.squeeze(
tf.expand_dims(
[[1, 2, 3]], dim=1), squeeze_dims=[1]).eval(),
a)
def testArithmeticRenames(self):
with self.test_session() as s:
stuff = tf.split(1, 2, [[1, 2, 3, 4], [4, 5, 6, 7]])
vals = s.run(stuff)
self.assertAllEqual(vals,
[[[1, 2], [4, 5]], [[3, 4], [6, 7]]])
self.assertAllEqual(
tf.neg(tf.mul(tf.add(1, 2), tf.sub(5, 3))).eval(),
-6)
self.assertAllEqual(
s.run(tf.listdiff([1, 2, 3], [3, 3, 4]))[0], [1, 2])
self.assertAllEqual(
tf.list_diff([1, 2, 3], [3, 3, 4])[0].eval(), [1, 2])
a = [[1., 2., 3.], [4., 5., 6.]]
foo = np.where(np.less(a, 2), np.negative(a), a)
self.assertAllEqual(
tf.select(tf.less(a, 2), tf.neg(a), a).eval(),
foo)
self.assertAllEqual(
tf.complex_abs(tf.constant(3 + 4.j)).eval(),
5)
# # TODO(aselle): (tf.batch_*)
# ]
def testBatchAndSvd(self):
with self.test_session():
mat = [[1., 2.], [2., 3.]]
batched_mat = tf.expand_dims(mat, [0])
result = tf.matmul(mat, mat).eval()
result_batched = tf.batch_matmul(batched_mat, batched_mat).eval()
self.assertAllEqual(result_batched, np.expand_dims(result, 0))
self.assertAllEqual(
tf.svd(mat, False, True).eval(),
tf.svd(mat, compute_uv=False, full_matrices=True).eval())
def testCrossEntropy(self):
# TODO(aselle): Test sparse_softmax_...
with self.test_session():
labels = [.8, .5, .2, .1]
logits = [.9, .1, .3, .1]
self.assertAllEqual(
tf.nn.softmax_cross_entropy_with_logits(
logits, labels).eval(),
tf.nn.softmax_cross_entropy_with_logits(
labels=labels, logits=logits).eval())
self.assertAllEqual(
tf.nn.sigmoid_cross_entropy_with_logits(
logits, labels).eval(),
tf.nn.sigmoid_cross_entropy_with_logits(
labels=labels, logits=logits).eval())
def testVariables(self):
with self.test_session() as s:
# make some variables
_ = [tf.Variable([1, 2, 3], dtype=tf.float32),
tf.Variable([1, 2, 3], dtype=tf.int32)]
s.run(tf.initialize_all_variables())
_ = [v.name for v in tf.all_variables()]
_ = [v.name for v in tf.local_variables()]
def testSummaries(self):
with self.test_session() as s:
var = tf.Variable([1, 2, 3], dtype=tf.float32)
s.run(tf.initialize_all_variables())
x, y = np.meshgrid(np.linspace(-10, 10, 256), np.linspace(-10, 10, 256))
image = np.sin(x**2 + y**2) / np.sqrt(x**2 + y**2) * .5 + .5
image = image[None, :, :, None]
# make a dummy sound
freq = 440 # A = 440Hz
sampling_frequency = 11000
audio = np.sin(2 * np.pi * np.linspace(0, 1, sampling_frequency) * freq)
audio = audio[None, :, None]
test_dir = tempfile.mkdtemp()
# test summaries
writer = tf.train.SummaryWriter(test_dir)
summaries = [
tf.scalar_summary("scalar_var", var[0]),
tf.scalar_summary("scalar_reduce_var", tf.reduce_sum(var)),
tf.histogram_summary("var_histogram", var),
tf.image_summary("sin_image", image),
tf.audio_summary("sin_wave", audio, sampling_frequency),
]
run_summaries = s.run(summaries)
writer.add_summary(s.run(tf.merge_summary(inputs=run_summaries)))
# This is redundant, but we want to be able to rewrite the command
writer.add_summary(s.run(tf.merge_all_summaries()))
writer.close()
shutil.rmtree(test_dir)
if __name__ == "__main__":
test_lib.main()
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