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Basic version of TensorFlow 1.0 Upgrade Script.
This script currently is minimally tested. It is a work in progress currently. Change: 144125570
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‎tensorflow/BUILD

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@@ -207,6 +207,7 @@ filegroup(
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"//tensorflow/tensorboard/lib/python:all_files",
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"//tensorflow/tensorboard/scripts:all_files",
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"//tensorflow/tools/common:all_files",
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"//tensorflow/tools/compatibility:all_files",
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"//tensorflow/tools/dist_test/server:all_files",
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"//tensorflow/tools/docker:all_files",
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"//tensorflow/tools/docker/notebooks:all_files",

‎tensorflow/tools/compatibility/BUILD

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licenses(["notice"]) # Apache 2.0
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package(default_visibility = ["//tensorflow:internal"])
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load(
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"//tensorflow:tensorflow.bzl",
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"tf_copts", # @unused
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"tf_cc_test", # @unused
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)
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py_binary(
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name = "tf_upgrade",
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srcs = ["tf_upgrade.py"],
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srcs_version = "PY2AND3",
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)
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py_test(
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name = "tf_upgrade_test",
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srcs = ["tf_upgrade_test.py"],
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srcs_version = "PY2AND3",
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deps = [
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"tf_upgrade",
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"//tensorflow:tensorflow_py",
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],
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)
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# Keep for reference, this test will succeed in 0.11 but fail in 1.0
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# py_test(
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# name = "test_file_v0_11",
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# size = "small",
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# srcs = ["testdata/test_file_v0_11.py"],
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# srcs_version = "PY2AND3",
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# deps = [
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# "//tensorflow:tensorflow_py",
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# ],
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# )
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genrule(
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name = "generate_upgraded_file",
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testonly = 1,
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srcs = ["testdata/test_file_v0_11.py"],
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outs = [
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"test_file_v1_0.py",
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"report.txt",
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],
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cmd = ("$(location tf_upgrade)" +
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" --infile $(location testdata/test_file_v0_11.py)" +
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" --outfile $(location test_file_v1_0.py)" +
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" --reportfile $(location report.txt)"),
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tools = ["tf_upgrade"],
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)
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py_test(
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name = "test_file_v1_0",
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size = "small",
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srcs = ["test_file_v1_0.py"],
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srcs_version = "PY2AND3",
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deps = [
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"//tensorflow:tensorflow_py",
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],
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)
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exports_files(
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[
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"tf_upgrade.py",
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"testdata/test_file_v0_11.py",
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],
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)
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# -----------------------------------------------------------------------------
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# Google-internal targets. These must be at the end for syncrepo.
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filegroup(
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name = "all_files",
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srcs = glob(
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["**/*"],
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exclude = [
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"**/METADATA",
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"**/OWNERS",
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],
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),
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visibility = ["//tensorflow:__subpackages__"],
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)

‎tensorflow/tools/compatibility/README.md

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# TensorFlow Python API Upgrade Utility
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This tool allows you to upgrade your existing TensorFlow Python scripts.
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This script can be run on a single Python file:
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```
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tf_upgrade.py --infile foo.py --outfile foo-upgraded.py
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```
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It will print a list of errors it finds that it can't fix. You can also run
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it on a directory tree:
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```
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tf_upgrade.py --intree coolcode -outtree coolcode-upgraded
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```
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In either case, it will also dump out a report e.g. which will detail changes
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e.g.:
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```
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third_party/tensorflow/tools/compatibility/test_file_v0.11.py Line 125
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Renamed keyword argument from `dim` to `axis`
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Renamed keyword argument from `squeeze_dims` to `axis`
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Old: [[1, 2, 3]], dim=1), squeeze_dims=[1]).eval(),
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~~~~ ~~~~~~~~~~~~~
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New: [[1, 2, 3]], axis=1), axis=[1]).eval(),
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~~~~~ ~~~~~
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```
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## Caveats
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- Don't update parts of your code manually before running this script. In
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particular, functions that have had reordered arguments like `tf.concat`,
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`tf.split` will cause the script to incorrectly add keyword arguments that
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mismap arguments.
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- This script is not able to upgrade all functions. One notable example is
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`tf.reverse()` which has been changed to take a list of indices rather than
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a tensor of bools. If the script detects this, it will report this to stdout
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(and in the report), and you can fix it manually. For example if you have
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`tf.reverse(a, [False, True, True])` you will need to manually change it to
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`tf.reverse(a, [1, 2])`.
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‎tensorflow/tools/compatibility/testdata/test_file_v0_11.py

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

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