experiments/STT-tensorflow
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Change: 120185825
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Illia Polosukhin 2016-04-18 17:56:51 -08:00 committed by TensorFlower Gardener
parent fc432e37a7
commit 5c9bc51857
216 changed files with 5146 additions and 1769 deletions
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3
ISSUE_TEMPLATE.md
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@ -10,6 +10,9 @@ help and advice.
### Environment info
Operating System:
Installed version of CUDA and cuDNN:
(please attach the output of `ls -l /path/to/cuda/lib/libcud*`):
If installed from binary pip package, provide:
1. Which pip package you installed.

6
README.md
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@ -33,9 +33,9 @@ and discussion.**
People who are a little bit adventurous can also try our nightly binaries:
* Linux CPU only: [Python 2](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.7.1-cp27-none-linux_x86_64.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/)) / [Python 3](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.7.1-cp34-cp34m-linux_x86_64.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/))
* Linux GPU: [Python 2](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.7.1-cp27-none-linux_x86_64.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-slave/)) / [Python 3](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.7.1-cp34-cp34m-linux_x86_64.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-slave/))
* Mac CPU only: [Python 2](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.7.1-py2-none-any.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.7.1-py3-none-any.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
* Linux CPU only: [Python 2](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.8.0rc0-cp27-none-linux_x86_64.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/)) / [Python 3](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.8.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/))
* Linux GPU: [Python 2](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-working/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.8.0rc0-cp27-none-linux_x86_64.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-working/)) / [Python 3](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-working/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.8.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-working/))
* Mac CPU only: [Python 2](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.8.0rc0-py2-none-any.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.8.0rc0-py3-none-any.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
* [Android](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-android/TF_BUILD_CONTAINER_TYPE=ANDROID,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=NO_PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=android-slave/lastSuccessfulBuild/artifact/bazel-out/local_linux/bin/tensorflow/examples/android/tensorflow_demo.apk) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-android/TF_BUILD_CONTAINER_TYPE=ANDROID,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=NO_PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=android-slave/))
#### *Try your first TensorFlow program*

43
RELEASE.md
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@ -1,3 +1,46 @@
# Release 0.8.0
## Major Features and Improvements
* Added a distributed runtime using GRPC
* Move skflow to `contrib/learn`
* Better linear optimizer in `contrib/linear_optimizer`
* Random forest implementation in `contrib/tensor_forest`
* CTC loss and decoders in `contrib/ctc`
* Basic support for `half` data type
* Better support for loading user ops (see examples in `contrib/`)
* Allow use of (non-blocking) Eigen threadpool with `TENSORFLOW_USE_EIGEN_THREADPOOL` define
* Add an extension mechanism for adding network file system support
* TensorBoard displays metadata stats (running time, memory usage and device used) and tensor shapes
## Big Fixes and Other Changes
* Utility for inspecting checkpoints
* Basic tracing and timeline support
* Allow building against cuDNN 5 (not incl. RNN/LSTM support)
* Added instructions and binaries for ProtoBuf library with fast serialization and without 64MB limit
* Added special functions
* `bool`-strictness: Tensors have to be explictly compared to `None`
* Shape strictness: all fed values must have a shape that is compatible with the tensor they are replacing
* Exposed `tf.while_loop` (deprecated `control_flow_ops.While`)
* run() now takes RunOptions and RunMetadata, which enable timing stats
* Fixed lots of potential overflow problems in op kernels
* Various performance improvements, especially for RNNs and convolutions
* Many bugfixes
* Nightly builds, tutorial tests, many test improvements
* New examples: transfer learning and deepdream ipython notebook
* Added tutorials, many documentation fixes.
## Thanks to our Contributors
This release contains contributions from many people at Google, as well as:
Abhinav Upadhyay, Aggelos Avgerinos, Alan Wu, Alexander G. de G. Matthews, Aleksandr Yahnev, @amchercashin, Andy Kitchen, Aurelien Geron, Awni Hannun, @BanditCat, Bas Veeling, Cameron Chen, @cg31, Cheng-Lung Sung, Christopher Bonnett, Dan Becker, Dan Van Boxel, Daniel Golden, Danijar Hafner, Danny Goodman, Dave Decker, David Dao, David Kretch, Dongjoon Hyun, Dustin Dorroh, @e-lin, Eurico Doirado, Erik Erwitt, Fabrizio Milo, @gaohuazuo, Iblis Lin, Igor Babuschkin, Isaac Hodes, Isaac Turner, Iván Vallés, J Yegerlehner, Jack Zhang, James Wexler, Jan Zikes, Jay Young, Jeff Hodges, @jmtatsch, Johnny Lim, Jonas Meinertz Hansen, Kanit Wongsuphasawat, Kashif Rasul, Ken Shirriff, Kenneth Mitchner, Kenta Yonekura, Konrad Magnusson, Konstantin Lopuhin, @lahwran, @lekaha, @liyongsea, Lucas Adams, @makseq, Mandeep Singh, @manipopopo, Mark Amery, Memo Akten, Michael Heilman, Michael Peteuil, Nathan Daly, Nicolas Fauchereau, @ninotoshi, Olav Nymoen, @panmari, @papelita1234, Pedro Lopes, Pranav Sailesh Mani, RJ Ryan, Rob Culliton, Robert DiPietro, @ronrest, Sam Abrahams, Sarath Shekkizhar, Scott Graham, Sebastian Raschka, Sung Kim, Surya Bhupatiraju, Syed Ahmed, Till Hoffmann, @timsl, @urimend, @vesnica, Vlad Frolov, Vlad Zagorodniy, Wei-Ting Kuo, Wenjian Huang, William Dmitri Breaden Madden, Wladimir Schmidt, Yuwen Yan, Yuxin Wu, Yuya Kusakabe, @zhongzyd, @znah.
We are also grateful to all who filed issues or helped resolve them, asked and
answered questions, and were part of inspiring discussions.
# Release 0.7.1
## Bug Fixes and Other Changes

3
tensorflow/BUILD
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@ -69,12 +69,12 @@ filegroup(
"//tensorflow/contrib/distributions:all_files",
"//tensorflow/contrib/framework:all_files",
"//tensorflow/contrib/layers:all_files",
"//tensorflow/contrib/learn:all_files",
"//tensorflow/contrib/linear_optimizer:all_files",
"//tensorflow/contrib/linear_optimizer/kernels:all_files",
"//tensorflow/contrib/lookup:all_files",
"//tensorflow/contrib/losses:all_files",
"//tensorflow/contrib/metrics:all_files",
"//tensorflow/contrib/skflow:all_files",
"//tensorflow/contrib/tensor_forest:all_files",
"//tensorflow/contrib/testing:all_files",
"//tensorflow/contrib/util:all_files",
@ -89,6 +89,7 @@ filegroup(
"//tensorflow/examples/how_tos/reading_data:all_files",
"//tensorflow/examples/image_retraining:all_files",
"//tensorflow/examples/label_image:all_files",
"//tensorflow/examples/skflow:all_files",
"//tensorflow/examples/tutorials/mnist:all_files",
"//tensorflow/examples/tutorials/word2vec:all_files",
"//tensorflow/g3doc/how_tos/adding_an_op:all_files",

2
tensorflow/contrib/BUILD
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@ -16,7 +16,9 @@ py_library(
"//tensorflow/contrib/ctc:ctc_py",
"//tensorflow/contrib/distributions:distributions_py",
"//tensorflow/contrib/framework:framework_py",
"//tensorflow/contrib/grid_rnn:grid_rnn_py",
"//tensorflow/contrib/layers:layers_py",
"//tensorflow/contrib/learn",
"//tensorflow/contrib/linear_optimizer:sdca_ops_py",
"//tensorflow/contrib/lookup:lookup_py",
"//tensorflow/contrib/losses:losses_py",

3
tensorflow/contrib/__init__.py
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@ -22,10 +22,13 @@ from __future__ import print_function
from tensorflow.contrib import ctc
from tensorflow.contrib import distributions
from tensorflow.contrib import framework
from tensorflow.contrib import grid_rnn
from tensorflow.contrib import layers
from tensorflow.contrib import learn
from tensorflow.contrib import linear_optimizer
from tensorflow.contrib import lookup
from tensorflow.contrib import losses
from tensorflow.contrib import metrics
from tensorflow.contrib import skflow
from tensorflow.contrib import testing
from tensorflow.contrib import util

2
tensorflow/contrib/cmake/CMakeLists.txt
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@ -1,5 +1,5 @@
# Minimum CMake required
cmake_minimum_required(VERSION 2.8)
cmake_minimum_required(VERSION 3.1)
# Project
project(tensorflow C CXX)

3
tensorflow/contrib/cmake/tf_cc_ops.cmake
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@ -123,6 +123,7 @@ foreach(tf_cc_op_lib_name ${tf_cc_op_lib_names})
${jpeg_STATIC_LIBRARIES}
${png_STATIC_LIBRARIES}
${ZLIB_LIBRARIES}
${CMAKE_DL_LIBS}
)
target_compile_options(${tf_cc_op_lib_name}_gen_cc PRIVATE
@ -201,4 +202,4 @@ target_compile_features(tf_cc_ops PRIVATE
# "ops/const_op.cc",
# ] + glob(["ops/*_grad.cc"]),
# pkg = "//tensorflow/core",
#)
#)

1
tensorflow/contrib/cmake/tf_tutorials.cmake
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@ -41,6 +41,7 @@ target_link_libraries(tf_tutorials_example_trainer PUBLIC
${jpeg_STATIC_LIBRARIES}
${png_STATIC_LIBRARIES}
${ZLIB_LIBRARIES}
${CMAKE_DL_LIBS}
)
target_compile_options(tf_tutorials_example_trainer PRIVATE

9
tensorflow/contrib/ffmpeg/BUILD Normal file
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@ -0,0 +1,9 @@
# Description:
# Ops that process audio and/or video files using FFmpeg.
# (https://www.ffmpeg.org/)
licenses(["notice"]) # Apache 2.0
exports_files(["LICENSE"])
package(default_visibility = ["//tensorflow:__subpackages__"])

61
tensorflow/contrib/ffmpeg/kernels/BUILD Normal file
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@ -0,0 +1,61 @@
# Description:
# Libraries and kernels for manipulating audio and video using FFmpeg.
# (https://www.ffmpeg.org)
licenses(["notice"]) # Apache 2.0
exports_files(["LICENSE"])
package(default_visibility = ["//tensorflow:__subpackages__"])
cc_library(
name = "ffmpeg_lib",
srcs = ["ffmpeg_lib.cc"],
hdrs = ["ffmpeg_lib.h"],
deps = [
"//tensorflow/core:framework",
"//tensorflow/core:lib",
],
)
cc_test(
name = "ffmpeg_lib_installed_test",
srcs = ["ffmpeg_lib_test.cc"],
args = [
"--should_ffmpeg_be_installed=true",
],
data = [
":testdata/test_sound1.mp3",
],
tags = [
"local",
"manual",
],
deps = [
":ffmpeg_lib",
"//tensorflow/core:framework_internal",
"//tensorflow/core:lib",
"//tensorflow/core:test",
],
)
cc_test(
name = "ffmpeg_lib_uninstalled_test",
srcs = ["ffmpeg_lib_test.cc"],
args = [
"--should_ffmpeg_be_installed=false",
],
data = [
":testdata/test_sound1.mp3",
],
tags = [
"local",
"manual",
],
deps = [
":ffmpeg_lib",
"//tensorflow/core:framework_internal",
"//tensorflow/core:lib",
"//tensorflow/core:test",
],
)

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tensorflow/contrib/ffmpeg/kernels/ffmpeg_lib.cc Normal file
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@ -0,0 +1,119 @@
// Copyright 2016 Google Inc. 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.
// =============================================================================
#include "tensorflow/contrib/ffmpeg/kernels/ffmpeg_lib.h"
#include <errno.h>
#include <stdlib.h>
#include <sys/stat.h>
#include <sys/types.h>
#include <sys/wait.h>
#include <unistd.h>
#include <string>
#include <tuple>
#include <vector>
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/lib/strings/str_util.h"
using tensorflow::strings::StrCat;
namespace tensorflow {
namespace ffmpeg {
namespace {
const char kFfmpegExecutable[] = "ffmpeg";
const int32 kDefaultProbeSize = 5000000; // 5MB
string GetTempFilename(const string& extension) {
for (const char* dir : std::vector<const char*>(
{getenv("TEST_TMPDIR"), getenv("TMPDIR"), getenv("TMP"), "/tmp"})) {
if (!dir || !dir[0]) {
continue;
}
struct stat statbuf;
if (!stat(dir, &statbuf) && S_ISDIR(statbuf.st_mode)) {
return io::JoinPath(dir, StrCat("tmp_file_", getpid(), ".", extension));
}
}
LOG(FATAL) << "No temp directory found.";
}
std::vector<string> FfmpegCommandLine(const string& input_filename,
const string& output_filename,
const string& input_format_id,
int32 samples_per_second,
int32 channel_count) {
return {"-nostats", // No additional progress display.
"-nostdin", // No interactive commands accepted.
"-f", input_format_id, // eg: "mp3"
"-probesize", StrCat(kDefaultProbeSize), "-i", input_filename,
"-loglevel", "info", // Enable verbose logging to support debugging.
"-map_metadata", "-1", // Copy global metadata from input to output.
"-vn", // No video recording.
"-ac:a:0", StrCat(channel_count), "-ar:a:0",
StrCat(samples_per_second),
// Output set (in several ways) to signed 16-bit little-endian ints.
"-codec:a:0", "pcm_s16le", "-sample_fmt", "s16", "-f", "s16le",
"-sn", // No subtitle recording.
"-y", // Overwrite output file.
StrCat(output_filename)};
}
[[noreturn]] int ExecuteFfmpeg(const std::vector<string>& args) {
std::vector<char*> args_chars;
std::transform(args.begin(), args.end(), std::back_inserter(args_chars),
[](const string& s) { return const_cast<char*>(s.c_str()); });
args_chars.push_back(nullptr);
::execvp(kFfmpegExecutable, args_chars.data());
// exec only returns on error.
const int error = errno;
LOG(ERROR) << "FFmpeg could not be executed: " << error;
::_exit(error);
}
} // namespace
Status ReadAudioFile(const string& filename, const string& audio_format_id,
int32 samples_per_second, int32 channel_count,
std::vector<float>* output_samples) {
// Create an argument list.
string output_filename = GetTempFilename(audio_format_id);
const std::vector<string> args =
FfmpegCommandLine(filename, output_filename, audio_format_id,
samples_per_second, channel_count);
// Execute ffmpeg and report errors.
pid_t child_pid = ::fork();
if (child_pid < 0) {
return Status(error::Code::UNKNOWN, StrCat("fork failed: ", errno));
}
if (child_pid == 0) {
ExecuteFfmpeg(args);
} else {
int status_code;
::waitpid(child_pid, &status_code, 0);
if (!status_code) {
return Status::OK();
} else {
return Status(error::Code::NOT_FOUND,
StrCat("FFmpeg execution failed: ", status_code));
}
}
}
} // namespace ffmpeg
} // namespace tensorflow

33
tensorflow/contrib/ffmpeg/kernels/ffmpeg_lib.h Normal file
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@ -0,0 +1,33 @@
// Copyright 2016 Google Inc. 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.
// =============================================================================
#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_FFMPEG_KERNELS_FFMPEG_LIB_H_
#define THIRD_PARTY_TENSORFLOW_CONTRIB_FFMPEG_KERNELS_FFMPEG_LIB_H_
#include <string>
#include "tensorflow/core/lib/core/status.h"
namespace tensorflow {
namespace ffmpeg {
Status ReadAudioFile(const string& filename, const string& audio_format_id,
int32 samples_per_second, int32 channel_count,
std::vector<float>* output_samples);
} // namespace ffmpeg
} // namespace tensorflow
#endif // THIRD_PARTY_TENSORFLOW_CONTRIB_FFMPEG_KERNELS_FFMPEG_LIB_H_

86
tensorflow/contrib/ffmpeg/kernels/ffmpeg_lib_test.cc Normal file
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@ -0,0 +1,86 @@
// Copyright 2016 Google Inc. 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.
// =============================================================================
#include "tensorflow/contrib/ffmpeg/kernels/ffmpeg_lib.h"
#include <stdlib.h>
#include <vector>
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/thread_annotations.h"
#include "tensorflow/core/util/command_line_flags.h"
using tensorflow::testing::TensorFlowSrcRoot;
namespace tensorflow {
namespace ffmpeg {
namespace {
const char kTestSoundFilename[] =
"contrib/ffmpeg/kernels/testdata/test_sound1.mp3";
// Set to true via a command line flag iff the test is expected to have FFmpeg
// installed.
mutex mu;
bool should_ffmpeg_be_installed GUARDED_BY(mu) = false;
void ParseTestFlags(int* argc, char** argv) {
mutex_lock l(mu);
CHECK(ParseFlags(argc, argv, {Flag("should_ffmpeg_be_installed",
&should_ffmpeg_be_installed)}));
}
TEST(FfmpegLibTest, TestUninstalled) {
{
mutex_lock l(mu);
if (should_ffmpeg_be_installed) {
return;
}
LOG(INFO) << "Assuming FFmpeg is uninstalled.";
}
string filename = io::JoinPath(TensorFlowSrcRoot(), kTestSoundFilename);
std::vector<float> output_samples;
Status status = ReadAudioFile(filename, "mp3", 5000, 1, &output_samples);
ASSERT_EQ(status.code(), error::Code::NOT_FOUND);
}
TEST(FfmpegLibTest, TestInstalled) {
{
mutex_lock l(mu);
if (!should_ffmpeg_be_installed) {
return;
}
LOG(INFO) << "Assuming FFmpeg is installed.";
}
string filename = io::JoinPath(TensorFlowSrcRoot(), kTestSoundFilename);
std::vector<float> output_samples;
Status status = ReadAudioFile(filename, "mp3", 5000, 1, &output_samples);
ASSERT_TRUE(status.ok());
}
} // namespace
} // ffmpeg
} // tensorflow
int main(int argc, char** argv) {
tensorflow::ffmpeg::ParseTestFlags(&argc, argv);
testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}

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@ -0,0 +1,39 @@
# Description:
# Contains classes to construct GridRNN cells
# APIs here are meant to evolve over time.
licenses(["notice"]) # Apache 2.0
exports_files(["LICENSE"])
package(default_visibility = ["//tensorflow:__subpackages__"])
load("//tensorflow:tensorflow.bzl", "cuda_py_tests")
py_library(
name = "grid_rnn_py",
srcs = ["__init__.py"] + glob(["python/ops/*.py"]),
srcs_version = "PY2AND3",
)
cuda_py_tests(
name = "grid_rnn_test",
srcs = ["python/kernel_tests/grid_rnn_test.py"],
additional_deps = [
":grid_rnn_py",
"//tensorflow/python:framework_test_lib",
"//tensorflow/python:platform_test",
],
)
filegroup(
name = "all_files",
srcs = glob(
["**/*"],
exclude = [
"**/METADATA",
"**/OWNERS",
],
),
visibility = ["//tensorflow:__subpackages__"],
)

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# Copyright 2016 Google Inc. 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.
# ==============================================================================
"""GridRNN cells
## This package provides classes for GridRNN
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# pylint: disable=unused-import,wildcard-import, line-too-long
from tensorflow.contrib.grid_rnn.python.ops.grid_rnn_cell import *

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@ -12,8 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from skflow import *

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# Copyright 2016 Google Inc. 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 GridRNN cells."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
class GridRNNCellTest(tf.test.TestCase):
def testGrid2BasicLSTMCell(self):
with self.test_session() as sess:
with tf.variable_scope('root', initializer=tf.constant_initializer(0.2)) as root_scope:
x = tf.zeros([1, 3])
m = tf.zeros([1, 8])
cell = tf.contrib.grid_rnn.Grid2BasicLSTMCell(2)
self.assertEqual(cell.state_size, 8)
g, s = cell(x, m)
self.assertEqual(g.get_shape(), (1, 2))
self.assertEqual(s.get_shape(), (1, 8))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, s], {x: np.array([[1., 1., 1.]]),
m: np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]])})
self.assertEqual(res[0].shape, (1, 2))
self.assertEqual(res[1].shape, (1, 8))
self.assertAllClose(res[0], [[ 0.36617181, 0.36617181]])
self.assertAllClose(res[1], [[ 0.71053141, 0.71053141, 0.36617181, 0.36617181,
0.72320831, 0.80555487, 0.39102408, 0.42150158]])
# emulate a loop through the input sequence, where we call cell() multiple times
root_scope.reuse_variables()
g2, s2 = cell(x, m)
self.assertEqual(g2.get_shape(), (1, 2))
self.assertEqual(s2.get_shape(), (1, 8))
res = sess.run([g2, s2], {x: np.array([[2., 2., 2.]]), m: res[1]})
self.assertEqual(res[0].shape, (1, 2))
self.assertEqual(res[1].shape, (1, 8))
self.assertAllClose(res[0], [[0.58847463, 0.58847463]])
self.assertAllClose(res[1], [[1.40469193, 1.40469193, 0.58847463, 0.58847463,
0.97726452, 1.04626071, 0.4927212, 0.51137757]])
def testGrid2BasicLSTMCellTied(self):
with self.test_session() as sess:
with tf.variable_scope('root', initializer=tf.constant_initializer(0.2)):
x = tf.zeros([1, 3])
m = tf.zeros([1, 8])
cell = tf.contrib.grid_rnn.Grid2BasicLSTMCell(2, tied=True)
self.assertEqual(cell.state_size, 8)
g, s = cell(x, m)
self.assertEqual(g.get_shape(), (1, 2))
self.assertEqual(s.get_shape(), (1, 8))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, s], {x: np.array([[1., 1., 1.]]),
m: np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]])})
self.assertEqual(res[0].shape, (1, 2))
self.assertEqual(res[1].shape, (1, 8))
self.assertAllClose(res[0], [[ 0.36617181, 0.36617181]])
self.assertAllClose(res[1], [[ 0.71053141, 0.71053141, 0.36617181, 0.36617181,
0.72320831, 0.80555487, 0.39102408, 0.42150158]])
res = sess.run([g, s], {x: np.array([[1., 1., 1.]]), m: res[1]})
self.assertEqual(res[0].shape, (1, 2))
self.assertEqual(res[1].shape, (1, 8))
self.assertAllClose(res[0], [[0.36703536, 0.36703536]])
self.assertAllClose(res[1], [[0.71200621, 0.71200621, 0.36703536, 0.36703536,
0.80941606, 0.87550586, 0.40108523, 0.42199609]])
def testGrid2BasicLSTMCellWithRelu(self):
with self.test_session() as sess:
with tf.variable_scope('root', initializer=tf.constant_initializer(0.2)):
x = tf.zeros([1, 3])
m = tf.zeros([1, 4])
cell = tf.contrib.grid_rnn.Grid2BasicLSTMCell(2, tied=False, non_recurrent_fn=tf.nn.relu)
self.assertEqual(cell.state_size, 4)
g, s = cell(x, m)
self.assertEqual(g.get_shape(), (1, 2))
self.assertEqual(s.get_shape(), (1, 4))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, s], {x: np.array([[1., 1., 1.]]),
m: np.array([[0.1, 0.2, 0.3, 0.4]])})
self.assertEqual(res[0].shape, (1, 2))
self.assertEqual(res[1].shape, (1, 4))
self.assertAllClose(res[0], [[ 0.31667367, 0.31667367]])
self.assertAllClose(res[1], [[ 0.29530135, 0.37520045, 0.17044567, 0.21292259]])
"""
LSTMCell
"""
def testGrid2LSTMCell(self):
with self.test_session() as sess:
with tf.variable_scope('root', initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 3])
m = tf.zeros([1, 8])
cell = tf.contrib.grid_rnn.Grid2LSTMCell(2, use_peepholes=True)
self.assertEqual(cell.state_size, 8)
g, s = cell(x, m)
self.assertEqual(g.get_shape(), (1, 2))
self.assertEqual(s.get_shape(), (1, 8))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, s], {x: np.array([[1., 1., 1.]]),
m: np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]])})
self.assertEqual(res[0].shape, (1, 2))
self.assertEqual(res[1].shape, (1, 8))
self.assertAllClose(res[0], [[ 0.95686918, 0.95686918]])
self.assertAllClose(res[1], [[ 2.41515064, 2.41515064, 0.95686918, 0.95686918,
1.38917875, 1.49043763, 0.83884692, 0.86036491]])
def testGrid2LSTMCellTied(self):
with self.test_session() as sess:
with tf.variable_scope('root', initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 3])
m = tf.zeros([1, 8])
cell = tf.contrib.grid_rnn.Grid2LSTMCell(2, tied=True, use_peepholes=True)
self.assertEqual(cell.state_size, 8)
g, s = cell(x, m)
self.assertEqual(g.get_shape(), (1, 2))
self.assertEqual(s.get_shape(), (1, 8))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, s], {x: np.array([[1., 1., 1.]]),
m: np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]])})
self.assertEqual(res[0].shape, (1, 2))
self.assertEqual(res[1].shape, (1, 8))
self.assertAllClose(res[0], [[ 0.95686918, 0.95686918]])
self.assertAllClose(res[1], [[ 2.41515064, 2.41515064, 0.95686918, 0.95686918,
1.38917875, 1.49043763, 0.83884692, 0.86036491]])
def testGrid2LSTMCellWithRelu(self):
with self.test_session() as sess:
with tf.variable_scope('root', initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 3])
m = tf.zeros([1, 4])
cell = tf.contrib.grid_rnn.Grid2LSTMCell(2, use_peepholes=True, non_recurrent_fn=tf.nn.relu)
self.assertEqual(cell.state_size, 4)
g, s = cell(x, m)
self.assertEqual(g.get_shape(), (1, 2))
self.assertEqual(s.get_shape(), (1, 4))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, s], {x: np.array([[1., 1., 1.]]),
m: np.array([[0.1, 0.2, 0.3, 0.4]])})
self.assertEqual(res[0].shape, (1, 2))
self.assertEqual(res[1].shape, (1, 4))
self.assertAllClose(res[0], [[ 2.1831727, 2.1831727]])
self.assertAllClose(res[1], [[ 0.92270052, 1.02325559, 0.66159075, 0.70475441]])
"""
RNNCell
"""
def testGrid2BasicRNNCell(self):
with self.test_session() as sess:
with tf.variable_scope('root', initializer=tf.constant_initializer(0.5)):
x = tf.zeros([2, 2])
m = tf.zeros([2, 4])
cell = tf.contrib.grid_rnn.Grid2BasicRNNCell(2)
self.assertEqual(cell.state_size, 4)
g, s = cell(x, m)
self.assertEqual(g.get_shape(), (2, 2))
self.assertEqual(s.get_shape(), (2, 4))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, s], {x: np.array([[1., 1.], [2., 2.]]),
m: np.array([[0.1, 0.1, 0.1, 0.1], [0.2, 0.2, 0.2, 0.2]])})
self.assertEqual(res[0].shape, (2, 2))
self.assertEqual(res[1].shape, (2, 4))
self.assertAllClose(res[0], [[0.94685763, 0.94685763],
[0.99480951, 0.99480951]])
self.assertAllClose(res[1], [[0.94685763, 0.94685763, 0.80049908, 0.80049908],
[0.99480951, 0.99480951, 0.97574311, 0.97574311]])
def testGrid2BasicRNNCellTied(self):
with self.test_session() as sess:
with tf.variable_scope('root', initializer=tf.constant_initializer(0.5)):
x = tf.zeros([2, 2])
m = tf.zeros([2, 4])
cell = tf.contrib.grid_rnn.Grid2BasicRNNCell(2, tied=True)
self.assertEqual(cell.state_size, 4)
g, s = cell(x, m)
self.assertEqual(g.get_shape(), (2, 2))
self.assertEqual(s.get_shape(), (2, 4))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, s], {x: np.array([[1., 1.], [2., 2.]]),
m: np.array([[0.1, 0.1, 0.1, 0.1], [0.2, 0.2, 0.2, 0.2]])})
self.assertEqual(res[0].shape, (2, 2))
self.assertEqual(res[1].shape, (2, 4))
self.assertAllClose(res[0], [[0.94685763, 0.94685763],
[0.99480951, 0.99480951]])
self.assertAllClose(res[1], [[0.94685763, 0.94685763, 0.80049908, 0.80049908],
[0.99480951, 0.99480951, 0.97574311, 0.97574311]])
def testGrid2BasicRNNCellWithRelu(self):
with self.test_session() as sess:
with tf.variable_scope('root', initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 2])
m = tf.zeros([1, 2])
cell = tf.contrib.grid_rnn.Grid2BasicRNNCell(2, non_recurrent_fn=tf.nn.relu)
self.assertEqual(cell.state_size, 2)
g, s = cell(x, m)
self.assertEqual(g.get_shape(), (1, 2))
self.assertEqual(s.get_shape(), (1, 2))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, s], {x: np.array([[1., 1.]]), m: np.array([[0.1, 0.1]])})
self.assertEqual(res[0].shape, (1, 2))
self.assertEqual(res[1].shape, (1, 2))
self.assertAllClose(res[0], [[1.80049896, 1.80049896]])
self.assertAllClose(res[1], [[0.80049896, 0.80049896]])
"""
1-LSTM
"""
def testGrid1LSTMCell(self):
with self.test_session() as sess:
with tf.variable_scope('root', initializer=tf.constant_initializer(0.5)) as root_scope:
x = tf.zeros([1, 3])
m = tf.zeros([1, 4])
cell = tf.contrib.grid_rnn.Grid1LSTMCell(2, use_peepholes=True)
self.assertEqual(cell.state_size, 4)
g, s = cell(x, m)
self.assertEqual(g.get_shape(), (1, 2))
self.assertEqual(s.get_shape(), (1, 4))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, s], {x: np.array([[1., 1., 1.]]),
m: np.array([[0.1, 0.2, 0.3, 0.4]])})
self.assertEqual(res[0].shape, (1, 2))
self.assertEqual(res[1].shape, (1, 4))
self.assertAllClose(res[0], [[0.91287315, 0.91287315]])
self.assertAllClose(res[1], [[2.26285243, 2.26285243, 0.91287315, 0.91287315]])
root_scope.reuse_variables()
x2 = tf.zeros([0, 0])
g2, s2 = cell(x2, m)
self.assertEqual(g2.get_shape(), (1, 2))
self.assertEqual(s2.get_shape(), (1, 4))
sess.run([tf.initialize_all_variables()])
res = sess.run([g2, s2], {m: res[1]})
self.assertEqual(res[0].shape, (1, 2))
self.assertEqual(res[1].shape, (1, 4))
self.assertAllClose(res[0], [[0.9032144, 0.9032144]])
self.assertAllClose(res[1], [[2.79966092, 2.79966092, 0.9032144, 0.9032144]])
g3, s3 = cell(x2, m)
self.assertEqual(g3.get_shape(), (1, 2))
self.assertEqual(s3.get_shape(), (1, 4))
sess.run([tf.initialize_all_variables()])
res = sess.run([g3, s3], {m: res[1]})
self.assertEqual(res[0].shape, (1, 2))
self.assertEqual(res[1].shape, (1, 4))
self.assertAllClose(res[0], [[0.92727238, 0.92727238]])
self.assertAllClose(res[1], [[3.3529923, 3.3529923, 0.92727238, 0.92727238]])
"""
3-LSTM
"""
def testGrid3LSTMCell(self):
with self.test_session() as sess:
with tf.variable_scope('root', initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 3])
m = tf.zeros([1, 12])
cell = tf.contrib.grid_rnn.Grid3LSTMCell(2, use_peepholes=True)
self.assertEqual(cell.state_size, 12)
g, s = cell(x, m)
self.assertEqual(g.get_shape(), (1, 2))
self.assertEqual(s.get_shape(), (1, 12))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, s], {x: np.array([[1., 1., 1.]]),
m: np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, -0.1, -0.2, -0.3, -0.4]])})
self.assertEqual(res[0].shape, (1, 2))
self.assertEqual(res[1].shape, (1, 12))
self.assertAllClose(res[0], [[0.96892911, 0.96892911]])
self.assertAllClose(res[1], [[2.45227885, 2.45227885, 0.96892911, 0.96892911,
1.33592629, 1.4373529, 0.80867189, 0.83247656,
0.7317788, 0.63205892, 0.56548983, 0.50446129]])
"""
Edge cases
"""
def testGridRNNEdgeCasesLikeRelu(self):
with self.test_session() as sess:
with tf.variable_scope('root', initializer=tf.constant_initializer(0.5)):
x = tf.zeros([3, 2])
m = tf.zeros([0, 0])
# this is equivalent to relu
cell = tf.contrib.grid_rnn.GridRNNCell(num_units=2, num_dims=1, input_dims=0, output_dims=0,
non_recurrent_dims=0, non_recurrent_fn=tf.nn.relu)
g, s = cell(x, m)
self.assertEqual(g.get_shape(), (3, 2))
self.assertEqual(s.get_shape(), (0, 0))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, s], {x: np.array([[1., -1.], [-2, 1], [2, -1]])})
self.assertEqual(res[0].shape, (3, 2))
self.assertEqual(res[1].shape, (0, 0))
self.assertAllClose(res[0], [[0, 0], [0, 0], [0.5, 0.5]])
def testGridRNNEdgeCasesNoOutput(self):
with self.test_session() as sess:
with tf.variable_scope('root', initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 2])
m = tf.zeros([1, 4])
# This cell produces no output
cell = tf.contrib.grid_rnn.GridRNNCell(num_units=2, num_dims=2, input_dims=0, output_dims=None,
non_recurrent_dims=0, non_recurrent_fn=tf.nn.relu)
g, s = cell(x, m)
self.assertEqual(g.get_shape(), (0, 0))
self.assertEqual(s.get_shape(), (1, 4))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, s], {x: np.array([[1., 1.]]),
m: np.array([[0.1, 0.1, 0.1, 0.1]])})
self.assertEqual(res[0].shape, (0, 0))
self.assertEqual(res[1].shape, (1, 4))
"""
Test with tf.nn.rnn
"""
def testGrid2LSTMCellWithRNN(self):
batch_size = 3
input_size = 5
max_length = 6 # unrolled up to this length
num_units = 2
with tf.variable_scope('root', initializer=tf.constant_initializer(0.5)):
cell = tf.contrib.grid_rnn.Grid2LSTMCell(num_units=num_units)
inputs = max_length * [
tf.placeholder(tf.float32, shape=(batch_size, input_size))]
outputs, state = tf.nn.rnn(cell, inputs, dtype=tf.float32)
self.assertEqual(len(outputs), len(inputs))
self.assertEqual(state.get_shape(), (batch_size, 8))
for out, inp in zip(outputs, inputs):
self.assertEqual(out.get_shape()[0], inp.get_shape()[0])
self.assertEqual(out.get_shape()[1], num_units)
self.assertEqual(out.dtype, inp.dtype)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
input_value = np.ones((batch_size, input_size))
values = sess.run(outputs + [state],
feed_dict={inputs[0]: input_value})
for v in values:
self.assertTrue(np.all(np.isfinite(v)))
def testGrid2LSTMCellReLUWithRNN(self):
batch_size = 3
input_size = 5
max_length = 6 # unrolled up to this length
num_units = 2
with tf.variable_scope('root', initializer=tf.constant_initializer(0.5)):
cell = tf.contrib.grid_rnn.Grid2LSTMCell(num_units=num_units, non_recurrent_fn=tf.nn.relu)
inputs = max_length * [
tf.placeholder(tf.float32, shape=(batch_size, input_size))]
outputs, state = tf.nn.rnn(cell, inputs, dtype=tf.float32)
self.assertEqual(len(outputs), len(inputs))
self.assertEqual(state.get_shape(), (batch_size, 4))
for out, inp in zip(outputs, inputs):
self.assertEqual(out.get_shape()[0], inp.get_shape()[0])
self.assertEqual(out.get_shape()[1], num_units)
self.assertEqual(out.dtype, inp.dtype)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
input_value = np.ones((batch_size, input_size))
values = sess.run(outputs + [state],
feed_dict={inputs[0]: input_value})
for v in values:
self.assertTrue(np.all(np.isfinite(v)))
def testGrid3LSTMCellReLUWithRNN(self):
batch_size = 3
input_size = 5
max_length = 6 # unrolled up to this length
num_units = 2
with tf.variable_scope('root', initializer=tf.constant_initializer(0.5)):
cell = tf.contrib.grid_rnn.Grid3LSTMCell(num_units=num_units, non_recurrent_fn=tf.nn.relu)
inputs = max_length * [
tf.placeholder(tf.float32, shape=(batch_size, input_size))]
outputs, state = tf.nn.rnn(cell, inputs, dtype=tf.float32)
self.assertEqual(len(outputs), len(inputs))
self.assertEqual(state.get_shape(), (batch_size, 8))
for out, inp in zip(outputs, inputs):
self.assertEqual(out.get_shape()[0], inp.get_shape()[0])
self.assertEqual(out.get_shape()[1], num_units)
self.assertEqual(out.dtype, inp.dtype)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
input_value = np.ones((batch_size, input_size))
values = sess.run(outputs + [state],
feed_dict={inputs[0]: input_value})
for v in values:
self.assertTrue(np.all(np.isfinite(v)))
def testGrid1LSTMCellWithRNN(self):
batch_size = 3
input_size = 5
max_length = 6 # unrolled up to this length
num_units = 2
with tf.variable_scope('root', initializer=tf.constant_initializer(0.5)):
cell = tf.contrib.grid_rnn.Grid1LSTMCell(num_units=num_units)
# for 1-LSTM, we only feed the first step
inputs = [tf.placeholder(tf.float32, shape=(batch_size, input_size))] \
+ (max_length - 1) * [tf.zeros([0, 0])]
outputs, state = tf.nn.rnn(cell, inputs, dtype=tf.float32)
self.assertEqual(len(outputs), len(inputs))
self.assertEqual(state.get_shape(), (batch_size, 4))
for out, inp in zip(outputs, inputs):
self.assertEqual(out.get_shape(), (3, num_units))
self.assertEqual(out.dtype, inp.dtype)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
input_value = np.ones((batch_size, input_size))
values = sess.run(outputs + [state],
feed_dict={inputs[0]: input_value})
for v in values:
self.assertTrue(np.all(np.isfinite(v)))
if __name__ == "__main__":
tf.test.main()

18
tensorflow/contrib/grid_rnn/python/ops/__init__.py Normal file
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@ -0,0 +1,18 @@
# Copyright 2016 Google Inc. 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

352
tensorflow/contrib/grid_rnn/python/ops/grid_rnn_cell.py Normal file
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@ -0,0 +1,352 @@
# Copyright 2016 Google Inc. 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.
# ==============================================================================
"""Module for constructing GridRNN cells"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import namedtuple
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops import rnn_cell
from tensorflow.contrib import layers
class GridRNNCell(rnn_cell.RNNCell):
"""Grid recurrent cell.
This implementation is based on:
http://arxiv.org/pdf/1507.01526v3.pdf
This is the generic implementation of GridRNN. Users can specify arbitrary number of dimensions,
set some of them to be priority (section 3.2), non-recurrent (section 3.3)
and input/output dimensions (section 3.4).
Weight sharing can also be specified using the `tied` parameter.
Type of recurrent units can be specified via `cell_fn`.
"""
def __init__(self, num_units, num_dims=1, input_dims=None, output_dims=None, priority_dims=None,
non_recurrent_dims=None, tied=False, cell_fn=None, non_recurrent_fn=None):
"""Initialize the parameters of a Grid RNN cell
Args:
num_units: int, The number of units in all dimensions of this GridRNN cell
num_dims: int, Number of dimensions of this grid.
input_dims: int or list, List of dimensions which will receive input data.
output_dims: int or list, List of dimensions from which the output will be recorded.
priority_dims: int or list, List of dimensions to be considered as priority dimensions.
If None, no dimension is prioritized.
non_recurrent_dims: int or list, List of dimensions that are not recurrent.
The transfer function for non-recurrent dimensions is specified via `non_recurrent_fn`,
which is default to be `tensorflow.nn.relu`.
tied: bool, Whether to share the weights among the dimensions of this GridRNN cell.
If there are non-recurrent dimensions in the grid, weights are shared between each
group of recurrent and non-recurrent dimensions.
cell_fn: function, a function which returns the recurrent cell object. Has to be in the following signature:
def cell_func(num_units, input_size):
# ...
and returns an object of type `RNNCell`. If None, LSTMCell with default parameters will be used.
non_recurrent_fn: a tensorflow Op that will be the transfer function of the non-recurrent dimensions
"""
if num_dims < 1:
raise ValueError('dims must be >= 1: {}'.format(num_dims))
self._config = _parse_rnn_config(num_dims, input_dims, output_dims, priority_dims,
non_recurrent_dims, non_recurrent_fn or nn.relu, tied, num_units)
cell_input_size = (self._config.num_dims - 1) * num_units
if cell_fn is None:
self._cell = rnn_cell.LSTMCell(num_units=num_units, input_size=cell_input_size)
else:
self._cell = cell_fn(num_units, cell_input_size)
if not isinstance(self._cell, rnn_cell.RNNCell):
raise ValueError('cell_fn must return an object of type RNNCell')
@property
def input_size(self):
# temporarily using num_units as the input_size of each dimension.
# The actual input size only determined when this cell get invoked,
# so this information can be considered unreliable.
return self._config.num_units * len(self._config.inputs)
@property
def output_size(self):
return self._cell.output_size * len(self._config.outputs)
@property
def state_size(self):
return self._cell.state_size * len(self._config.recurrents)
def __call__(self, inputs, state, scope=None):
"""Run one step of GridRNN.
Args:
inputs: input Tensor, 2D, batch x input_size. Or None
state: state Tensor, 2D, batch x state_size. Note that state_size = cell_state_size * recurrent_dims
scope: VariableScope for the created subgraph; defaults to "GridRNNCell".
Returns:
A tuple containing:
- A 2D, batch x output_size, Tensor representing the output of the cell
after reading "inputs" when previous state was "state".
- A 2D, batch x state_size, Tensor representing the new state of the cell
after reading "inputs" when previous state was "state".
"""
state_sz = state.get_shape().as_list()[1]
if self.state_size != state_sz:
raise ValueError('Actual state size not same as specified: {} vs {}.'.format(state_sz, self.state_size))
conf = self._config
dtype = inputs.dtype if inputs is not None else state.dtype
# c_prev is `m`, and m_prev is `h` in the paper. Keep c and m here for consistency with the codebase
c_prev = [None] * self._config.num_dims
m_prev = [None] * self._config.num_dims
cell_output_size = self._cell.state_size - conf.num_units
# for LSTM : state = memory cell + output, hence cell_output_size > 0
# for GRU/RNN: state = output (whose size is equal to _num_units), hence cell_output_size = 0
for recurrent_dim, start_idx in zip(self._config.recurrents, range(0, self.state_size, self._cell.state_size)):
if cell_output_size > 0:
c_prev[recurrent_dim] = array_ops.slice(state, [0, start_idx], [-1, conf.num_units])
m_prev[recurrent_dim] = array_ops.slice(state, [0, start_idx + conf.num_units], [-1, cell_output_size])
else:
m_prev[recurrent_dim] = array_ops.slice(state, [0, start_idx], [-1, conf.num_units])
new_output = [None] * conf.num_dims
new_state = [None] * conf.num_dims
with vs.variable_scope(scope or type(self).__name__): # GridRNNCell
# project input
if inputs is not None and sum(inputs.get_shape().as_list()) > 0 and len(conf.inputs) > 0:
input_splits = array_ops.split(1, len(conf.inputs), inputs)
input_sz = input_splits[0].get_shape().as_list()[1]
for i, j in enumerate(conf.inputs):
input_project_m = vs.get_variable('project_m_{}'.format(j), [input_sz, conf.num_units], dtype=dtype)
m_prev[j] = math_ops.matmul(input_splits[i], input_project_m)
if cell_output_size > 0:
input_project_c = vs.get_variable('project_c_{}'.format(j), [input_sz, conf.num_units], dtype=dtype)
c_prev[j] = math_ops.matmul(input_splits[i], input_project_c)
_propagate(conf.non_priority, conf, self._cell, c_prev, m_prev, new_output, new_state, True)
_propagate(conf.priority, conf, self._cell, c_prev, m_prev, new_output, new_state, False)
output_tensors = [new_output[i] for i in self._config.outputs]
output = array_ops.zeros([0, 0], dtype) if len(output_tensors) == 0 else array_ops.concat(1,
output_tensors)
state_tensors = [new_state[i] for i in self._config.recurrents]
states = array_ops.zeros([0, 0], dtype) if len(state_tensors) == 0 else array_ops.concat(1, state_tensors)
return output, states
"""
Specialized cells, for convenience
"""
class Grid1BasicRNNCell(GridRNNCell):
"""1D BasicRNN cell"""
def __init__(self, num_units):
super(Grid1BasicRNNCell, self).__init__(num_units=num_units, num_dims=1,
input_dims=0, output_dims=0, priority_dims=0, tied=False,
cell_fn=lambda n, i: rnn_cell.BasicRNNCell(num_units=n, input_size=i))
class Grid2BasicRNNCell(GridRNNCell):
"""2D BasicRNN cell
This creates a 2D cell which receives input and gives output in the first dimension.
The first dimension can optionally be non-recurrent if `non_recurrent_fn` is specified.
"""
def __init__(self, num_units, tied=False, non_recurrent_fn=None):
super(Grid2BasicRNNCell, self).__init__(num_units=num_units, num_dims=2,
input_dims=0, output_dims=0, priority_dims=0, tied=tied,
non_recurrent_dims=None if non_recurrent_fn is None else 0,
cell_fn=lambda n, i: rnn_cell.BasicRNNCell(num_units=n, input_size=i),
non_recurrent_fn=non_recurrent_fn)
class Grid1BasicLSTMCell(GridRNNCell):
"""1D BasicLSTM cell"""
def __init__(self, num_units, forget_bias=1):
super(Grid1BasicLSTMCell, self).__init__(num_units=num_units, num_dims=1,
input_dims=0, output_dims=0, priority_dims=0, tied=False,
cell_fn=lambda n, i: rnn_cell.BasicLSTMCell(num_units=n,
forget_bias=forget_bias, input_size=i))
class Grid2BasicLSTMCell(GridRNNCell):
"""2D BasicLSTM cell
This creates a 2D cell which receives input and gives output in the first dimension.
The first dimension can optionally be non-recurrent if `non_recurrent_fn` is specified.
"""
def __init__(self, num_units, tied=False, non_recurrent_fn=None, forget_bias=1):
super(Grid2BasicLSTMCell, self).__init__(num_units=num_units, num_dims=2,
input_dims=0, output_dims=0, priority_dims=0, tied=tied,
non_recurrent_dims=None if non_recurrent_fn is None else 0,
cell_fn=lambda n, i: rnn_cell.BasicLSTMCell(
num_units=n, forget_bias=forget_bias, input_size=i),
non_recurrent_fn=non_recurrent_fn)
class Grid1LSTMCell(GridRNNCell):
"""1D LSTM cell
This is different from Grid1BasicLSTMCell because it gives options to specify the forget bias and enabling peepholes
"""
def __init__(self, num_units, use_peepholes=False, forget_bias=1.0):
super(Grid1LSTMCell, self).__init__(num_units=num_units, num_dims=1,
input_dims=0, output_dims=0, priority_dims=0,
cell_fn=lambda n, i: rnn_cell.LSTMCell(
num_units=n, input_size=i, use_peepholes=use_peepholes,
forget_bias=forget_bias))
class Grid2LSTMCell(GridRNNCell):
"""2D LSTM cell
This creates a 2D cell which receives input and gives output in the first dimension.
The first dimension can optionally be non-recurrent if `non_recurrent_fn` is specified.
"""
def __init__(self, num_units, tied=False, non_recurrent_fn=None,
use_peepholes=False, forget_bias=1.0):
super(Grid2LSTMCell, self).__init__(num_units=num_units, num_dims=2,
input_dims=0, output_dims=0, priority_dims=0, tied=tied,
non_recurrent_dims=None if non_recurrent_fn is None else 0,
cell_fn=lambda n, i: rnn_cell.LSTMCell(
num_units=n, input_size=i, forget_bias=forget_bias,
use_peepholes=use_peepholes),
non_recurrent_fn=non_recurrent_fn)
class Grid3LSTMCell(GridRNNCell):
"""3D BasicLSTM cell
This creates a 2D cell which receives input and gives output in the first dimension.
The first dimension can optionally be non-recurrent if `non_recurrent_fn` is specified.
The second and third dimensions are LSTM.
"""
def __init__(self, num_units, tied=False, non_recurrent_fn=None,
use_peepholes=False, forget_bias=1.0):
super(Grid3LSTMCell, self).__init__(num_units=num_units, num_dims=3,
input_dims=0, output_dims=0, priority_dims=0, tied=tied,
non_recurrent_dims=None if non_recurrent_fn is None else 0,
cell_fn=lambda n, i: rnn_cell.LSTMCell(
num_units=n, input_size=i, forget_bias=forget_bias,
use_peepholes=use_peepholes),
non_recurrent_fn=non_recurrent_fn)
class Grid2GRUCell(GridRNNCell):
"""2D LSTM cell
This creates a 2D cell which receives input and gives output in the first dimension.
The first dimension can optionally be non-recurrent if `non_recurrent_fn` is specified.
"""
def __init__(self, num_units, tied=False, non_recurrent_fn=None):
super(Grid2GRUCell, self).__init__(num_units=num_units, num_dims=2,
input_dims=0, output_dims=0, priority_dims=0, tied=tied,
non_recurrent_dims=None if non_recurrent_fn is None else 0,
cell_fn=lambda n, i: rnn_cell.GRUCell(num_units=n, input_size=i),
non_recurrent_fn=non_recurrent_fn)
"""
Helpers
"""
_GridRNNDimension = namedtuple('_GridRNNDimension', ['idx', 'is_input', 'is_output', 'is_priority', 'non_recurrent_fn'])
_GridRNNConfig = namedtuple('_GridRNNConfig', ['num_dims', 'dims',
'inputs', 'outputs', 'recurrents',
'priority', 'non_priority', 'tied', 'num_units'])
def _parse_rnn_config(num_dims, ls_input_dims, ls_output_dims, ls_priority_dims, ls_non_recurrent_dims,
non_recurrent_fn, tied, num_units):
def check_dim_list(ls):
if ls is None:
ls = []
if not isinstance(ls, (list, tuple)):
ls = [ls]
ls = sorted(set(ls))
if any(_ < 0 or _ >= num_dims for _ in ls):
raise ValueError('Invalid dims: {}. Must be in [0, {})'.format(ls, num_dims))
return ls
input_dims = check_dim_list(ls_input_dims)
output_dims = check_dim_list(ls_output_dims)
priority_dims = check_dim_list(ls_priority_dims)
non_recurrent_dims = check_dim_list(ls_non_recurrent_dims)
rnn_dims = []
for i in range(num_dims):
rnn_dims.append(_GridRNNDimension(idx=i, is_input=(i in input_dims), is_output=(i in output_dims),
is_priority=(i in priority_dims),
non_recurrent_fn=non_recurrent_fn if i in non_recurrent_dims else None))
return _GridRNNConfig(num_dims=num_dims, dims=rnn_dims, inputs=input_dims, outputs=output_dims,
recurrents=[x for x in range(num_dims) if x not in non_recurrent_dims],
priority=priority_dims,
non_priority=[x for x in range(num_dims) if x not in priority_dims],
tied=tied, num_units=num_units)
def _propagate(dim_indices, conf, cell, c_prev, m_prev, new_output, new_state, first_call):
"""
Propagates through all the cells in dim_indices dimensions.
"""
if len(dim_indices) == 0:
return
# Because of the way RNNCells are implemented, we take the last dimension (H_{N-1}) out
# and feed it as the state of the RNN cell (in `last_dim_output`)
# The input of the cell (H_0 to H_{N-2}) are concatenated into `cell_inputs`
if conf.num_dims > 1:
ls_cell_inputs = [None] * (conf.num_dims - 1)
for d in conf.dims[:-1]:
ls_cell_inputs[d.idx] = new_output[d.idx] if new_output[d.idx] is not None else m_prev[d.idx]
cell_inputs = array_ops.concat(1, ls_cell_inputs)
else:
cell_inputs = array_ops.zeros([m_prev[0].get_shape().as_list()[0], 0], m_prev[0].dtype)
last_dim_output = new_output[-1] if new_output[-1] is not None else m_prev[-1]
for i in dim_indices:
d = conf.dims[i]
if d.non_recurrent_fn:
linear_args = array_ops.concat(1, [cell_inputs, last_dim_output]) if conf.num_dims > 1 else last_dim_output
with vs.variable_scope('non_recurrent' if conf.tied else 'non_recurrent/cell_{}'.format(i)):
if conf.tied and not(first_call and i == dim_indices[0]):
vs.get_variable_scope().reuse_variables()
new_output[d.idx] = layers.fully_connected(linear_args, num_output_units=conf.num_units,
activation_fn=d.non_recurrent_fn,
weight_init=vs.get_variable_scope().initializer or
layers.initializers.xavier_initializer)
else:
if c_prev[i] is not None:
cell_state = array_ops.concat(1, [c_prev[i], last_dim_output])
else:
# for GRU/RNN, the state is just the previous output
cell_state = last_dim_output
with vs.variable_scope('recurrent' if conf.tied else 'recurrent/cell_{}'.format(i)):
if conf.tied and not (first_call and i == dim_indices[0]):
vs.get_variable_scope().reuse_variables()
new_output[d.idx], new_state[d.idx] = cell(cell_inputs, cell_state)

15
tensorflow/contrib/layers/python/layers/optimizers.py
View File

@ -87,10 +87,16 @@ def optimize_loss(loss,
loss = control_flow_ops.with_dependencies([loss_averages_op], loss)
# Learning rate variable, with possible decay.
lr = vs.get_variable("learning_rate",
[],
trainable=False,
initializer=init_ops.constant_initializer(learning_rate))
if isinstance(learning_rate, ops.Tensor) and len(learning_rate.get_shape()) == 0:
lr = learning_rate
elif isinstance(learning_rate, float):
lr = vs.get_variable("learning_rate",
[],
trainable=False,
initializer=init_ops.constant_initializer(learning_rate))
else:
raise ValueError("Learning rate should be 0d Tensor or float. Got %s" %
str(learning_rate))
if learning_rate_decay_fn is not None:
lr = learning_rate_decay_fn(lr, global_step)
@ -149,3 +155,4 @@ def optimize_loss(loss,
train_tensor = control_flow_ops.with_dependencies([grad_updates], final_loss)
return train_tensor

1
tensorflow/contrib/layers/python/layers/optimizers_test.py
View File

@ -88,3 +88,4 @@ class OptimizersTest(tf.test.TestCase):
if __name__ == "__main__":
tf.test.main()

259
tensorflow/contrib/learn/BUILD Normal file
View File

@ -0,0 +1,259 @@
# Description:
# Contains TF Learn (aka Scikit Flow) sub-project with high level tensorflow API.
licenses(["notice"]) # Apache 2.0
exports_files(["LICENSE"])
package(default_visibility = ["//tensorflow:__subpackages__"])
filegroup(
name = "datasets",
srcs = glob(["**/*.csv"]),
)
py_library(
name = "learn",
srcs = glob([
"python/learn/**/*.py",
]) + [
"__init__.py",
"python/__init__.py",
],
data = [":datasets"],
srcs_version = "PY2AND3",
deps = ["//tensorflow/python:framework"],
)
py_test(
name = "test_base",
size = "medium",
srcs = ["python/learn/tests/test_base.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_custom_decay",
size = "small",
srcs = ["python/learn/tests/test_custom_decay.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_data_feeder",
size = "small",
srcs = ["python/learn/tests/test_data_feeder.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_early_stopping",
size = "medium",
srcs = ["python/learn/tests/test_early_stopping.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_estimators",
size = "small",
srcs = ["python/learn/tests/test_estimators.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_grid_search",
size = "small",
srcs = ["python/learn/tests/test_grid_search.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_io",
size = "small",
srcs = ["python/learn/tests/test_io.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_multioutput",
size = "small",
srcs = ["python/learn/tests/test_multioutput.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_nonlinear",
size = "medium",
srcs = ["python/learn/tests/test_nonlinear.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_regression",
size = "small",
srcs = ["python/learn/tests/test_regression.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_saver",
size = "small",
srcs = ["python/learn/tests/test_saver.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_ops",
size = "small",
srcs = ["python/learn/ops/tests/test_ops.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_dropout_ops",
size = "small",
srcs = ["python/learn/ops/tests/test_dropout_ops.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_seq2seq_ops",
size = "small",
srcs = ["python/learn/ops/tests/test_seq2seq_ops.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_categorical",
size = "small",
srcs = ["python/learn/preprocessing/tests/test_categorical.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_categorical_vocabulary",
size = "small",
srcs = ["python/learn/preprocessing/tests/test_categorical_vocabulary.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_text",
size = "small",
srcs = ["python/learn/preprocessing/tests/test_text.py"],
srcs_version = "PY2AND3",
deps = [
":learn",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework",
"//tensorflow/python:framework_test_lib",
],
)
filegroup(
name = "all_files",
srcs = glob(
["**/*"],
exclude = [
"**/METADATA",
"**/OWNERS",
],
),
visibility = ["//tensorflow:__subpackages__"],
)

21
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@ -0,0 +1,21 @@
# Copyright 2016 Google Inc. 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.learn.python.learn import *

0
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0
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0
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0
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0
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0
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0
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0
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0
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0
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0
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20
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@ -0,0 +1,20 @@
# Copyright 2016 Google Inc. 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.learn.python.learn import *

60
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View File

@ -1,7 +1,6 @@
|Travis-CI Build Status| |Codecov Status| |License| |PyPI version| |Join the chat at
https://gitter.im/tensorflow/skflow|
|License| |Join the chat at [https://gitter.im/tensorflow/skflow](https://gitter.im/tensorflow/skflow)|
Scikit Flow
TF Learn (aka Scikit Flow)
===========
This is a simplified interface for TensorFlow, to get people started on predictive analytics and data mining.
@ -13,7 +12,7 @@ Why *TensorFlow*?
- TensorFlow provides a good backbone for building different shapes of machine learning applications.
- It will continue to evolve both in the distributed direction and as general pipelinining machinery.
Why *Scikit Flow*?
Why *TensorFlow Learn* (Scikit Flow)?
-----------------
- To smooth the transition from the Scikit Learn world of one-liner machine learning into the more open world of building different shapes of ML models. You can start by using fit/predict and slide into TensorFlow APIs as you are getting comfortable.
- To provide a set of reference models that would be easy to integrate with existing code.
@ -21,25 +20,10 @@ Why *Scikit Flow*?
Installation
============
Dependencies
-----------
- Python: 2.7, 3.4+
- Scikit learn: 0.16, 0.17, 0.18+
- Tensorflow: 0.7+
Optionally you can install Scikit Learn and Pandas for additional functionality.
First, you need to make sure you have `TensorFlow <https://github.com/tensorflow/tensorflow#installation>`__ and `Scikit Learn <http://scikit-learn.org/stable/install.html>`__ installed.
Then you can simply import `learn` via `from tensorflow.contrib.learn` or use `tf.contrib.learn`.
Run the following to install the stable version from PyPI:
.. code:: bash
pip install skflow
Or run the following to install from the development version from Github:
.. code:: bash
pip install git+git://github.com/tensorflow/skflow.git
Tutorial
--------
@ -57,13 +41,13 @@ Community
---------
- Twitter `#skflow <https://twitter.com/search?q=skflow&src=typd>`__.
- StackOverflow with `skflow tag <http://stackoverflow.com/questions/tagged/skflow>`__ for questions and struggles.
- Github `issues <https://github.com/tensorflow/skflow/issues>`__ for technical discussions and feature requests.
- Github `issues <https://github.com/tensorflow/tensorflow/issues>`__ for technical discussions and feature requests.
- `Gitter channel <https://gitter.im/tensorflow/skflow>`__ for non-trivial discussions.
Usage
-----
Below are few simple examples of the API. For more examples, please see `examples <https://github.com/tensorflow/skflow/tree/master/examples>`__.
Below are few simple examples of the API. For more examples, please see `examples <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/skflow>`__.
General tips
~~~~~~~~~~~~
@ -79,11 +63,10 @@ Simple linear classification:
.. code:: python
import skflow
from sklearn import datasets, metrics
iris = datasets.load_iris()
classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
classifier = learn.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(iris.target, classifier.predict(iris.data))
print("Accuracy: %f" % score)
@ -95,12 +78,11 @@ Simple linear regression:
.. code:: python
import skflow
from sklearn import datasets, metrics, preprocessing
boston = datasets.load_boston()
X = preprocessing.StandardScaler().fit_transform(boston.data)
regressor = skflow.TensorFlowLinearRegressor()
regressor = learn.TensorFlowLinearRegressor()
regressor.fit(X, boston.target)
score = metrics.mean_squared_error(regressor.predict(X), boston.target)
print ("MSE: %f" % score)
@ -112,11 +94,10 @@ Example of 3 layer network with 10, 20 and 10 hidden units respectively:
.. code:: python
import skflow
from sklearn import datasets, metrics
iris = datasets.load_iris()
classifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(iris.target, classifier.predict(iris.data))
print("Accuracy: %f" % score)
@ -128,17 +109,16 @@ Example of how to pass a custom model to the TensorFlowEstimator:
.. code:: python
import skflow
from sklearn import datasets, metrics
iris = datasets.load_iris()
def my_model(X, y):
"""This is DNN with 10, 20, 10 hidden layers, and dropout of 0.5 probability."""
layers = skflow.ops.dnn(X, [10, 20, 10], keep_prob=0.5)
return skflow.models.logistic_regression(layers, y)
layers = learn.ops.dnn(X, [10, 20, 10], keep_prob=0.5)
return learn.models.logistic_regression(layers, y)
classifier = skflow.TensorFlowEstimator(model_fn=my_model, n_classes=3)
classifier = learn.TensorFlowEstimator(model_fn=my_model, n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(iris.target, classifier.predict(iris.data))
print("Accuracy: %f" % score)
@ -146,15 +126,13 @@ Example of how to pass a custom model to the TensorFlowEstimator:
Saving / Restoring models
~~~~~~~~~~~~~~~~~~~~~~~~~
Each estimator has a ``save`` method which takes folder path where all model information will be saved. For restoring you can just call ``skflow.TensorFlowEstimator.restore(path)`` and it will return object of your class.
Each estimator has a ``save`` method which takes folder path where all model information will be saved. For restoring you can just call ``learn.TensorFlowEstimator.restore(path)`` and it will return object of your class.
Some example code:
.. code:: python
import skflow
classifier = skflow.TensorFlowLinearRegression()
classifier = learn.TensorFlowLinearRegression()
classifier.fit(...)
classifier.save('/tmp/tf_examples/my_model_1/')
@ -168,7 +146,7 @@ To get nice visualizations and summaries you can use ``logdir`` parameter on ``f
.. code:: python
classifier = skflow.TensorFlowLinearRegression()
classifier = learn.TensorFlowLinearRegression()
classifier.fit(X, y, logdir='/tmp/tf_examples/my_model_1/')
Then run next command in command line:
@ -186,7 +164,7 @@ Loss visualization: |Text classification RNN Loss|
More examples
-------------
See examples folder for:
See `examples folder <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/skflow>`__ for:
- Easy way to handle categorical variables - words are just an example of categorical variable.
- Text Classification - see examples for RNN, CNN on word and characters.
@ -194,10 +172,6 @@ See examples folder for:
- Images (CNNs) - see example for digit recognition.
- More & deeper - different examples showing DNNs and CNNs
.. |Travis-CI Build Status| image:: https://travis-ci.org/tensorflow/skflow.svg?branch=master
:target: https://travis-ci.org/tensorflow/skflow
.. |Codecov Status| image:: https://codecov.io/github/tensorflow/skflow/coverage.svg?precision=2
:target: https://codecov.io/github/tensorflow/skflow
.. |License| image:: https://img.shields.io/badge/license-Apache%202.0-blue.svg
:target: http://www.apache.org/licenses/LICENSE-2.0.html
.. |Join the chat at https://gitter.im/tensorflow/skflow| image:: https://badges.gitter.im/Join%20Chat.svg

11
tensorflow/contrib/skflow/python/skflow/addons/__init__.py → tensorflow/contrib/learn/python/learn/__init__.py
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@ -1,4 +1,4 @@
"""Scikit Flow Addons."""
"""Main Scikit Flow module."""
# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
@ -12,8 +12,15 @@
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.skflow.python.skflow.addons.config_addon import ConfigAddon
import numpy as np
from tensorflow.contrib.learn.python.learn.io import *
from tensorflow.contrib.learn.python.learn.estimators import *
from tensorflow.contrib.learn.python.learn import ops
from tensorflow.contrib.learn.python.learn import preprocessing
from tensorflow.contrib.learn.python.learn import models

56
tensorflow/contrib/learn/python/learn/datasets/__init__.py Normal file
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@ -0,0 +1,56 @@
"""Module inclues reference datasets and utilities to load datasets."""
# Copyright 2015-present The Scikit Flow 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import collections
from os import path
import numpy as np
from tensorflow.contrib.learn.python.learn.datasets import base
from tensorflow.contrib.learn.python.learn.datasets import mnist
# Export load_iris and load_boston.
load_iris = base.load_iris
load_boston = base.load_boston
# List of all available datasets.
# Note, currently they may return different types.
DATASETS = {
# Returns base.Dataset.
'iris': base.load_iris,
'boston': base.load_boston,
# Returns mnist.Dataset.
'mnist': mnist.load_mnist,
}
def load_dataset(name):
"""Loads dataset by name.
Args:
name: Name of the dataset to load.
Returns:
Features and targets for given dataset. Can be numpy or iterator.
"""
if name not in DATASETS:
raise ValueError("Name of dataset is not found: %s" % name)
return DATASETS[name]()

87
tensorflow/contrib/learn/python/learn/datasets/base.py Normal file
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@ -0,0 +1,87 @@
"""Base utilities for loading datasets."""
# Copyright 2015-present The Scikit Flow 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import collections
import os
from os import path
import tempfile
from six.moves import urllib
import numpy as np
from tensorflow.python.platform.default import _gfile as gfile
Dataset = collections.namedtuple('Dataset', ['data', 'target'])
def load_csv(filename, target_dtype):
with gfile.Open(filename) as csv_file:
data_file = csv.reader(csv_file)
header = next(data_file)
n_samples = int(header[0])
n_features = int(header[1])
target_names = np.array(header[2:])
data = np.empty((n_samples, n_features))
target = np.empty((n_samples,), dtype=np.int)
for i, ir in enumerate(data_file):
data[i] = np.asarray(ir[:-1], dtype=np.float64)
target[i] = np.asarray(ir[-1], dtype=target_dtype)
return Dataset(data=data, target=target)
def load_iris():
"""Load Iris dataset.
Returns:
Dataset object containing data in-memory.
"""
module_path = path.dirname(__file__)
return load_csv(path.join(module_path, 'data', 'iris.csv'),
target_dtype=np.int)
def load_boston():
"""Load Boston housing dataset.
Returns:
Dataset object containing data in-memory.
"""
module_path = path.dirname(__file__)
return load_csv(path.join(module_path, 'data', 'boston_house_prices.csv'),
target_dtype=np.float)
def maybe_download(filename, work_directory, source_url):
"""Download the data from source url, unless it's already here."""
if not gfile.Exists(work_directory):
gfile.MakeDirs(work_directory)
filepath = os.path.join(work_directory, filename)
if not gfile.Exists(filepath):
with tempfile.NamedTemporaryFile() as tmpfile:
temp_file_name = tmpfile.name
urllib.request.urlretrieve(source_url, temp_file_name)
gfile.Copy(temp_file_name, filepath)
with gfile.GFile(filepath) as f:
size = f.Size()
print('Successfully downloaded', filename, size, 'bytes.')
return filepath

507
tensorflow/contrib/learn/python/learn/datasets/data/boston_house_prices.csv Normal file
View File

@ -0,0 +1,507 @@
506,13,"CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS","RAD","TAX","PTRATIO","B","LSTAT","MEDV"
0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24
0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6
0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7
0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4
0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33,36.2
0.02985,0,2.18,0,0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7
0.08829,12.5,7.87,0,0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43,22.9
0.14455,12.5,7.87,0,0.524,6.172,96.1,5.9505,5,311,15.2,396.9,19.15,27.1
0.21124,12.5,7.87,0,0.524,5.631,100,6.0821,5,311,15.2,386.63,29.93,16.5
0.17004,12.5,7.87,0,0.524,6.004,85.9,6.5921,5,311,15.2,386.71,17.1,18.9
0.22489,12.5,7.87,0,0.524,6.377,94.3,6.3467,5,311,15.2,392.52,20.45,15
0.11747,12.5,7.87,0,0.524,6.009,82.9,6.2267,5,311,15.2,396.9,13.27,18.9
0.09378,12.5,7.87,0,0.524,5.889,39,5.4509,5,311,15.2,390.5,15.71,21.7
0.62976,0,8.14,0,0.538,5.949,61.8,4.7075,4,307,21,396.9,8.26,20.4
0.63796,0,8.14,0,0.538,6.096,84.5,4.4619,4,307,21,380.02,10.26,18.2
0.62739,0,8.14,0,0.538,5.834,56.5,4.4986,4,307,21,395.62,8.47,19.9
1.05393,0,8.14,0,0.538,5.935,29.3,4.4986,4,307,21,386.85,6.58,23.1
0.7842,0,8.14,0,0.538,5.99,81.7,4.2579,4,307,21,386.75,14.67,17.5
0.80271,0,8.14,0,0.538,5.456,36.6,3.7965,4,307,21,288.99,11.69,20.2
0.7258,0,8.14,0,0.538,5.727,69.5,3.7965,4,307,21,390.95,11.28,18.2
1.25179,0,8.14,0,0.538,5.57,98.1,3.7979,4,307,21,376.57,21.02,13.6
0.85204,0,8.14,0,0.538,5.965,89.2,4.0123,4,307,21,392.53,13.83,19.6
1.23247,0,8.14,0,0.538,6.142,91.7,3.9769,4,307,21,396.9,18.72,15.2
0.98843,0,8.14,0,0.538,5.813,100,4.0952,4,307,21,394.54,19.88,14.5
0.75026,0,8.14,0,0.538,5.924,94.1,4.3996,4,307,21,394.33,16.3,15.6
0.84054,0,8.14,0,0.538,5.599,85.7,4.4546,4,307,21,303.42,16.51,13.9
0.67191,0,8.14,0,0.538,5.813,90.3,4.682,4,307,21,376.88,14.81,16.6
0.95577,0,8.14,0,0.538,6.047,88.8,4.4534,4,307,21,306.38,17.28,14.8
0.77299,0,8.14,0,0.538,6.495,94.4,4.4547,4,307,21,387.94,12.8,18.4
1.00245,0,8.14,0,0.538,6.674,87.3,4.239,4,307,21,380.23,11.98,21
1.13081,0,8.14,0,0.538,5.713,94.1,4.233,4,307,21,360.17,22.6,12.7
1.35472,0,8.14,0,0.538,6.072,100,4.175,4,307,21,376.73,13.04,14.5
1.38799,0,8.14,0,0.538,5.95,82,3.99,4,307,21,232.6,27.71,13.2
1.15172,0,8.14,0,0.538,5.701,95,3.7872,4,307,21,358.77,18.35,13.1
1.61282,0,8.14,0,0.538,6.096,96.9,3.7598,4,307,21,248.31,20.34,13.5
0.06417,0,5.96,0,0.499,5.933,68.2,3.3603,5,279,19.2,396.9,9.68,18.9
0.09744,0,5.96,0,0.499,5.841,61.4,3.3779,5,279,19.2,377.56,11.41,20
0.08014,0,5.96,0,0.499,5.85,41.5,3.9342,5,279,19.2,396.9,8.77,21
0.17505,0,5.96,0,0.499,5.966,30.2,3.8473,5,279,19.2,393.43,10.13,24.7
0.02763,75,2.95,0,0.428,6.595,21.8,5.4011,3,252,18.3,395.63,4.32,30.8
0.03359,75,2.95,0,0.428,7.024,15.8,5.4011,3,252,18.3,395.62,1.98,34.9
0.12744,0,6.91,0,0.448,6.77,2.9,5.7209,3,233,17.9,385.41,4.84,26.6
0.1415,0,6.91,0,0.448,6.169,6.6,5.7209,3,233,17.9,383.37,5.81,25.3
0.15936,0,6.91,0,0.448,6.211,6.5,5.7209,3,233,17.9,394.46,7.44,24.7
0.12269,0,6.91,0,0.448,6.069,40,5.7209,3,233,17.9,389.39,9.55,21.2
0.17142,0,6.91,0,0.448,5.682,33.8,5.1004,3,233,17.9,396.9,10.21,19.3
0.18836,0,6.91,0,0.448,5.786,33.3,5.1004,3,233,17.9,396.9,14.15,20
0.22927,0,6.91,0,0.448,6.03,85.5,5.6894,3,233,17.9,392.74,18.8,16.6
0.25387,0,6.91,0,0.448,5.399,95.3,5.87,3,233,17.9,396.9,30.81,14.4
0.21977,0,6.91,0,0.448,5.602,62,6.0877,3,233,17.9,396.9,16.2,19.4
0.08873,21,5.64,0,0.439,5.963,45.7,6.8147,4,243,16.8,395.56,13.45,19.7
0.04337,21,5.64,0,0.439,6.115,63,6.8147,4,243,16.8,393.97,9.43,20.5
0.0536,21,5.64,0,0.439,6.511,21.1,6.8147,4,243,16.8,396.9,5.28,25
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0.08387,0,12.83,0,0.437,5.874,36.6,4.5026,5,398,18.7,396.06,9.1,20.3
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0.88125,0,21.89,0,0.624,5.637,94.7,1.9799,4,437,21.2,396.9,18.34,14.3
0.34006,0,21.89,0,0.624,6.458,98.9,2.1185,4,437,21.2,395.04,12.6,19.2
1.19294,0,21.89,0,0.624,6.326,97.7,2.271,4,437,21.2,396.9,12.26,19.6
0.59005,0,21.89,0,0.624,6.372,97.9,2.3274,4,437,21.2,385.76,11.12,23
0.32982,0,21.89,0,0.624,5.822,95.4,2.4699,4,437,21.2,388.69,15.03,18.4
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0.2909,0,21.89,0,0.624,6.174,93.6,1.6119,4,437,21.2,388.08,24.16,14
1.62864,0,21.89,0,0.624,5.019,100,1.4394,4,437,21.2,396.9,34.41,14.4
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4.0974,0,19.58,0,0.871,5.468,100,1.4118,5,403,14.7,396.9,26.42,15.6
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1.6566,0,19.58,0,0.871,6.122,97.3,1.618,5,403,14.7,372.8,14.1,21.5
1.49632,0,19.58,0,0.871,5.404,100,1.5916,5,403,14.7,341.6,13.28,19.6
1.12658,0,19.58,1,0.871,5.012,88,1.6102,5,403,14.7,343.28,12.12,15.3
2.14918,0,19.58,0,0.871,5.709,98.5,1.6232,5,403,14.7,261.95,15.79,19.4
1.41385,0,19.58,1,0.871,6.129,96,1.7494,5,403,14.7,321.02,15.12,17
3.53501,0,19.58,1,0.871,6.152,82.6,1.7455,5,403,14.7,88.01,15.02,15.6
2.44668,0,19.58,0,0.871,5.272,94,1.7364,5,403,14.7,88.63,16.14,13.1
1.22358,0,19.58,0,0.605,6.943,97.4,1.8773,5,403,14.7,363.43,4.59,41.3
1.34284,0,19.58,0,0.605,6.066,100,1.7573,5,403,14.7,353.89,6.43,24.3
1.42502,0,19.58,0,0.871,6.51,100,1.7659,5,403,14.7,364.31,7.39,23.3
1.27346,0,19.58,1,0.605,6.25,92.6,1.7984,5,403,14.7,338.92,5.5,27
1.46336,0,19.58,0,0.605,7.489,90.8,1.9709,5,403,14.7,374.43,1.73,50
1.83377,0,19.58,1,0.605,7.802,98.2,2.0407,5,403,14.7,389.61,1.92,50
1.51902,0,19.58,1,0.605,8.375,93.9,2.162,5,403,14.7,388.45,3.32,50
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2.924,0,19.58,0,0.605,6.101,93,2.2834,5,403,14.7,240.16,9.81,25
2.01019,0,19.58,0,0.605,7.929,96.2,2.0459,5,403,14.7,369.3,3.7,50
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2.44953,0,19.58,0,0.605,6.402,95.2,2.2625,5,403,14.7,330.04,11.32,22.3
1.20742,0,19.58,0,0.605,5.875,94.6,2.4259,5,403,14.7,292.29,14.43,17.4
2.3139,0,19.58,0,0.605,5.88,97.3,2.3887,5,403,14.7,348.13,12.03,19.1
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0.01778,95,1.47,0,0.403,7.135,13.9,7.6534,3,402,17,384.3,4.45,32.9
0.03445,82.5,2.03,0,0.415,6.162,38.4,6.27,2,348,14.7,393.77,7.43,24.1
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0.13587,0,10.59,1,0.489,6.064,59.1,4.2392,4,277,18.6,381.32,14.66,24.4
0.43571,0,10.59,1,0.489,5.344,100,3.875,4,277,18.6,396.9,23.09,20
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0.01709,90,2.02,0,0.41,6.728,36.1,12.1265,5,187,17,384.46,4.5,30.1
0.04301,80,1.91,0,0.413,5.663,21.9,10.5857,4,334,22,382.8,8.05,18.2
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13.5222,0,18.1,0,0.631,3.863,100,1.5106,24,666,20.2,131.42,13.33,23.1
4.89822,0,18.1,0,0.631,4.97,100,1.3325,24,666,20.2,375.52,3.26,50
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tensorflow/contrib/learn/python/learn/datasets/data/iris.csv Normal file
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@ -0,0 +1,151 @@
150,4,setosa,versicolor,virginica
5.1,3.5,1.4,0.2,0
4.9,3.0,1.4,0.2,0
4.7,3.2,1.3,0.2,0
4.6,3.1,1.5,0.2,0
5.0,3.6,1.4,0.2,0
5.4,3.9,1.7,0.4,0
4.6,3.4,1.4,0.3,0
5.0,3.4,1.5,0.2,0
4.4,2.9,1.4,0.2,0
4.9,3.1,1.5,0.1,0
5.4,3.7,1.5,0.2,0
4.8,3.4,1.6,0.2,0
4.8,3.0,1.4,0.1,0
4.3,3.0,1.1,0.1,0
5.8,4.0,1.2,0.2,0
5.7,4.4,1.5,0.4,0
5.4,3.9,1.3,0.4,0
5.1,3.5,1.4,0.3,0
5.7,3.8,1.7,0.3,0
5.1,3.8,1.5,0.3,0
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5.1,3.7,1.5,0.4,0
4.6,3.6,1.0,0.2,0
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5.2,3.4,1.4,0.2,0
4.7,3.2,1.6,0.2,0
4.8,3.1,1.6,0.2,0
5.4,3.4,1.5,0.4,0
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5.5,4.2,1.4,0.2,0
4.9,3.1,1.5,0.1,0
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5.0,3.3,1.4,0.2,0
7.0,3.2,4.7,1.4,1
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5.7,2.8,4.5,1.3,1
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5.6,3.0,4.5,1.5,1
5.8,2.7,4.1,1.0,1
6.2,2.2,4.5,1.5,1
5.6,2.5,3.9,1.1,1
5.9,3.2,4.8,1.8,1
6.1,2.8,4.0,1.3,1
6.3,2.5,4.9,1.5,1
6.1,2.8,4.7,1.2,1
6.4,2.9,4.3,1.3,1
6.6,3.0,4.4,1.4,1
6.8,2.8,4.8,1.4,1
6.7,3.0,5.0,1.7,1
6.0,2.9,4.5,1.5,1
5.7,2.6,3.5,1.0,1
5.5,2.4,3.8,1.1,1
5.5,2.4,3.7,1.0,1
5.8,2.7,3.9,1.2,1
6.0,2.7,5.1,1.6,1
5.4,3.0,4.5,1.5,1
6.0,3.4,4.5,1.6,1
6.7,3.1,4.7,1.5,1
6.3,2.3,4.4,1.3,1
5.6,3.0,4.1,1.3,1
5.5,2.5,4.0,1.3,1
5.5,2.6,4.4,1.2,1
6.1,3.0,4.6,1.4,1
5.8,2.6,4.0,1.2,1
5.0,2.3,3.3,1.0,1
5.6,2.7,4.2,1.3,1
5.7,3.0,4.2,1.2,1
5.7,2.9,4.2,1.3,1
6.2,2.9,4.3,1.3,1
5.1,2.5,3.0,1.1,1
5.7,2.8,4.1,1.3,1
6.3,3.3,6.0,2.5,2
5.8,2.7,5.1,1.9,2
7.1,3.0,5.9,2.1,2
6.3,2.9,5.6,1.8,2
6.5,3.0,5.8,2.2,2
7.6,3.0,6.6,2.1,2
4.9,2.5,4.5,1.7,2
7.3,2.9,6.3,1.8,2
6.7,2.5,5.8,1.8,2
7.2,3.6,6.1,2.5,2
6.5,3.2,5.1,2.0,2
6.4,2.7,5.3,1.9,2
6.8,3.0,5.5,2.1,2
5.7,2.5,5.0,2.0,2
5.8,2.8,5.1,2.4,2
6.4,3.2,5.3,2.3,2
6.5,3.0,5.5,1.8,2
7.7,3.8,6.7,2.2,2
7.7,2.6,6.9,2.3,2
6.0,2.2,5.0,1.5,2
6.9,3.2,5.7,2.3,2
5.6,2.8,4.9,2.0,2
7.7,2.8,6.7,2.0,2
6.3,2.7,4.9,1.8,2
6.7,3.3,5.7,2.1,2
7.2,3.2,6.0,1.8,2
6.2,2.8,4.8,1.8,2
6.1,3.0,4.9,1.8,2
6.4,2.8,5.6,2.1,2
7.2,3.0,5.8,1.6,2
7.4,2.8,6.1,1.9,2
7.9,3.8,6.4,2.0,2
6.4,2.8,5.6,2.2,2
6.3,2.8,5.1,1.5,2
6.1,2.6,5.6,1.4,2
7.7,3.0,6.1,2.3,2
6.3,3.4,5.6,2.4,2
6.4,3.1,5.5,1.8,2
6.0,3.0,4.8,1.8,2
6.9,3.1,5.4,2.1,2
6.7,3.1,5.6,2.4,2
6.9,3.1,5.1,2.3,2
5.8,2.7,5.1,1.9,2
6.8,3.2,5.9,2.3,2
6.7,3.3,5.7,2.5,2
6.7,3.0,5.2,2.3,2
6.3,2.5,5.0,1.9,2
6.5,3.0,5.2,2.0,2
6.2,3.4,5.4,2.3,2
5.9,3.0,5.1,1.8,2
1 150 4 setosa versicolor virginica
2 5.1 3.5 1.4 0.2 0
3 4.9 3.0 1.4 0.2 0
4 4.7 3.2 1.3 0.2 0
5 4.6 3.1 1.5 0.2 0
6 5.0 3.6 1.4 0.2 0
7 5.4 3.9 1.7 0.4 0
8 4.6 3.4 1.4 0.3 0
9 5.0 3.4 1.5 0.2 0
10 4.4 2.9 1.4 0.2 0
11 4.9 3.1 1.5 0.1 0
12 5.4 3.7 1.5 0.2 0
13 4.8 3.4 1.6 0.2 0
14 4.8 3.0 1.4 0.1 0
15 4.3 3.0 1.1 0.1 0
16 5.8 4.0 1.2 0.2 0
17 5.7 4.4 1.5 0.4 0
18 5.4 3.9 1.3 0.4 0
19 5.1 3.5 1.4 0.3 0
20 5.7 3.8 1.7 0.3 0
21 5.1 3.8 1.5 0.3 0
22 5.4 3.4 1.7 0.2 0
23 5.1 3.7 1.5 0.4 0
24 4.6 3.6 1.0 0.2 0
25 5.1 3.3 1.7 0.5 0
26 4.8 3.4 1.9 0.2 0
27 5.0 3.0 1.6 0.2 0
28 5.0 3.4 1.6 0.4 0
29 5.2 3.5 1.5 0.2 0
30 5.2 3.4 1.4 0.2 0
31 4.7 3.2 1.6 0.2 0
32 4.8 3.1 1.6 0.2 0
33 5.4 3.4 1.5 0.4 0
34 5.2 4.1 1.5 0.1 0
35 5.5 4.2 1.4 0.2 0
36 4.9 3.1 1.5 0.1 0
37 5.0 3.2 1.2 0.2 0
38 5.5 3.5 1.3 0.2 0
39 4.9 3.1 1.5 0.1 0
40 4.4 3.0 1.3 0.2 0
41 5.1 3.4 1.5 0.2 0
42 5.0 3.5 1.3 0.3 0
43 4.5 2.3 1.3 0.3 0
44 4.4 3.2 1.3 0.2 0
45 5.0 3.5 1.6 0.6 0
46 5.1 3.8 1.9 0.4 0
47 4.8 3.0 1.4 0.3 0
48 5.1 3.8 1.6 0.2 0
49 4.6 3.2 1.4 0.2 0
50 5.3 3.7 1.5 0.2 0
51 5.0 3.3 1.4 0.2 0
52 7.0 3.2 4.7 1.4 1
53 6.4 3.2 4.5 1.5 1
54 6.9 3.1 4.9 1.5 1
55 5.5 2.3 4.0 1.3 1
56 6.5 2.8 4.6 1.5 1
57 5.7 2.8 4.5 1.3 1
58 6.3 3.3 4.7 1.6 1
59 4.9 2.4 3.3 1.0 1
60 6.6 2.9 4.6 1.3 1
61 5.2 2.7 3.9 1.4 1
62 5.0 2.0 3.5 1.0 1
63 5.9 3.0 4.2 1.5 1
64 6.0 2.2 4.0 1.0 1
65 6.1 2.9 4.7 1.4 1
66 5.6 2.9 3.6 1.3 1
67 6.7 3.1 4.4 1.4 1
68 5.6 3.0 4.5 1.5 1
69 5.8 2.7 4.1 1.0 1
70 6.2 2.2 4.5 1.5 1
71 5.6 2.5 3.9 1.1 1
72 5.9 3.2 4.8 1.8 1
73 6.1 2.8 4.0 1.3 1
74 6.3 2.5 4.9 1.5 1
75 6.1 2.8 4.7 1.2 1
76 6.4 2.9 4.3 1.3 1
77 6.6 3.0 4.4 1.4 1
78 6.8 2.8 4.8 1.4 1
79 6.7 3.0 5.0 1.7 1
80 6.0 2.9 4.5 1.5 1
81 5.7 2.6 3.5 1.0 1
82 5.5 2.4 3.8 1.1 1
83 5.5 2.4 3.7 1.0 1
84 5.8 2.7 3.9 1.2 1
85 6.0 2.7 5.1 1.6 1
86 5.4 3.0 4.5 1.5 1
87 6.0 3.4 4.5 1.6 1
88 6.7 3.1 4.7 1.5 1
89 6.3 2.3 4.4 1.3 1
90 5.6 3.0 4.1 1.3 1
91 5.5 2.5 4.0 1.3 1
92 5.5 2.6 4.4 1.2 1
93 6.1 3.0 4.6 1.4 1
94 5.8 2.6 4.0 1.2 1
95 5.0 2.3 3.3 1.0 1
96 5.6 2.7 4.2 1.3 1
97 5.7 3.0 4.2 1.2 1
98 5.7 2.9 4.2 1.3 1
99 6.2 2.9 4.3 1.3 1
100 5.1 2.5 3.0 1.1 1
101 5.7 2.8 4.1 1.3 1
102 6.3 3.3 6.0 2.5 2
103 5.8 2.7 5.1 1.9 2
104 7.1 3.0 5.9 2.1 2
105 6.3 2.9 5.6 1.8 2
106 6.5 3.0 5.8 2.2 2
107 7.6 3.0 6.6 2.1 2
108 4.9 2.5 4.5 1.7 2
109 7.3 2.9 6.3 1.8 2
110 6.7 2.5 5.8 1.8 2
111 7.2 3.6 6.1 2.5 2
112 6.5 3.2 5.1 2.0 2
113 6.4 2.7 5.3 1.9 2
114 6.8 3.0 5.5 2.1 2
115 5.7 2.5 5.0 2.0 2
116 5.8 2.8 5.1 2.4 2
117 6.4 3.2 5.3 2.3 2
118 6.5 3.0 5.5 1.8 2
119 7.7 3.8 6.7 2.2 2
120 7.7 2.6 6.9 2.3 2
121 6.0 2.2 5.0 1.5 2
122 6.9 3.2 5.7 2.3 2
123 5.6 2.8 4.9 2.0 2
124 7.7 2.8 6.7 2.0 2
125 6.3 2.7 4.9 1.8 2
126 6.7 3.3 5.7 2.1 2
127 7.2 3.2 6.0 1.8 2
128 6.2 2.8 4.8 1.8 2
129 6.1 3.0 4.9 1.8 2
130 6.4 2.8 5.6 2.1 2
131 7.2 3.0 5.8 1.6 2
132 7.4 2.8 6.1 1.9 2
133 7.9 3.8 6.4 2.0 2
134 6.4 2.8 5.6 2.2 2
135 6.3 2.8 5.1 1.5 2
136 6.1 2.6 5.6 1.4 2
137 7.7 3.0 6.1 2.3 2
138 6.3 3.4 5.6 2.4 2
139 6.4 3.1 5.5 1.8 2
140 6.0 3.0 4.8 1.8 2
141 6.9 3.1 5.4 2.1 2
142 6.7 3.1 5.6 2.4 2
143 6.9 3.1 5.1 2.3 2
144 5.8 2.7 5.1 1.9 2
145 6.8 3.2 5.9 2.3 2
146 6.7 3.3 5.7 2.5 2
147 6.7 3.0 5.2 2.3 2
148 6.3 2.5 5.0 1.9 2
149 6.5 3.0 5.2 2.0 2
150 6.2 3.4 5.4 2.3 2
151 5.9 3.0 5.1 1.8 2

210
tensorflow/contrib/learn/python/learn/datasets/mnist.py Normal file
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@ -0,0 +1,210 @@
"""Functions for downloading and reading MNIST data."""
# Copyright 2015 Google Inc. 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gzip
import os
import tempfile
import numpy
from six.moves import urllib
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from tensorflow.contrib.learn.python.learn.datasets.base import maybe_download
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
def _read32(bytestream):
dt = numpy.dtype(numpy.uint32).newbyteorder('>')
return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
def extract_images(filename):
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
print('Extracting', filename)
with tf.gfile.Open(filename, 'rb') as f, gzip.GzipFile(fileobj=f) as bytestream:
magic = _read32(bytestream)
if magic != 2051:
raise ValueError(
'Invalid magic number %d in MNIST image file: %s' %
(magic, filename))
num_images = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(rows * cols * num_images)
data = numpy.frombuffer(buf, dtype=numpy.uint8)
data = data.reshape(num_images, rows, cols, 1)
return data
def dense_to_one_hot(labels_dense, num_classes):
"""Convert class labels from scalars to one-hot vectors."""
num_labels = labels_dense.shape[0]
index_offset = numpy.arange(num_labels) * num_classes
labels_one_hot = numpy.zeros((num_labels, num_classes))
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
return labels_one_hot
def extract_labels(filename, one_hot=False, num_classes=10):
"""Extract the labels into a 1D uint8 numpy array [index]."""
print('Extracting', filename)
with tf.gfile.Open(filename, 'rb') as f, gzip.GzipFile(fileobj=f) as bytestream:
magic = _read32(bytestream)
if magic != 2049:
raise ValueError(
'Invalid magic number %d in MNIST label file: %s' %
(magic, filename))
num_items = _read32(bytestream)
buf = bytestream.read(num_items)
labels = numpy.frombuffer(buf, dtype=numpy.uint8)
if one_hot:
return dense_to_one_hot(labels, num_classes)
return labels
class DataSet(object):
def __init__(self, images, labels, fake_data=False, one_hot=False,
dtype=tf.float32):
"""Construct a DataSet.
one_hot arg is used only if fake_data is true. `dtype` can be either
`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
`[0, 1]`.
"""
dtype = tf.as_dtype(dtype).base_dtype
if dtype not in (tf.uint8, tf.float32):
raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
dtype)
if fake_data:
self._num_examples = 10000
self.one_hot = one_hot
else:
assert images.shape[0] == labels.shape[0], (
'images.shape: %s labels.shape: %s' % (images.shape,
labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
assert images.shape[3] == 1
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2])
if dtype == tf.float32:
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(numpy.float32)
images = numpy.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0
@property
def images(self):
return self._images
@property
def labels(self):
return self._labels
@property
def num_examples(self):
return self._num_examples
@property
def epochs_completed(self):
return self._epochs_completed
def next_batch(self, batch_size, fake_data=False):
"""Return the next `batch_size` examples from this data set."""
if fake_data:
fake_image = [1] * 784
if self.one_hot:
fake_label = [1] + [0] * 9
else:
fake_label = 0
return [fake_image for _ in xrange(batch_size)], [
fake_label for _ in xrange(batch_size)]
start = self._index_in_epoch
self._index_in_epoch += batch_size
if self._index_in_epoch > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Shuffle the data
perm = numpy.arange(self._num_examples)
numpy.random.shuffle(perm)
self._images = self._images[perm]
self._labels = self._labels[perm]
# Start next epoch
start = 0
self._index_in_epoch = batch_size
assert batch_size <= self._num_examples
end = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
def read_data_sets(train_dir, fake_data=False, one_hot=False, dtype=tf.float32):
class DataSets(object):
pass
data_sets = DataSets()
if fake_data:
def fake():
return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype)
data_sets.train = fake()
data_sets.validation = fake()
data_sets.test = fake()
return data_sets
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
VALIDATION_SIZE = 5000
local_file = maybe_download(TRAIN_IMAGES, train_dir, SOURCE_URL + TRAIN_IMAGES)
train_images = extract_images(local_file)
local_file = maybe_download(TRAIN_LABELS, train_dir, SOURCE_URL + TRAIN_LABELS)
train_labels = extract_labels(local_file, one_hot=one_hot)
local_file = maybe_download(TEST_IMAGES, train_dir, SOURCE_URL + TEST_IMAGES)
test_images = extract_images(local_file)
local_file = maybe_download(TEST_LABELS, train_dir, SOURCE_URL + TEST_LABELS)
test_labels = extract_labels(local_file, one_hot=one_hot)
validation_images = train_images[:VALIDATION_SIZE]
validation_labels = train_labels[:VALIDATION_SIZE]
train_images = train_images[VALIDATION_SIZE:]
train_labels = train_labels[VALIDATION_SIZE:]
data_sets.train = DataSet(train_images, train_labels, dtype=dtype)
data_sets.validation = DataSet(validation_images, validation_labels,
dtype=dtype)
data_sets.test = DataSet(test_images, test_labels, dtype=dtype)
return data_sets
def load_mnist():
return read_data_sets("MNIST_data")

28
tensorflow/contrib/learn/python/learn/estimators/__init__.py Normal file
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@ -0,0 +1,28 @@
"""Scikit Flow Estimators."""
# Copyright 2015-present The Scikit Flow 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowEstimator
from tensorflow.contrib.learn.python.learn.estimators.linear import TensorFlowLinearClassifier
from tensorflow.contrib.learn.python.learn.estimators.linear import TensorFlowClassifier
from tensorflow.contrib.learn.python.learn.estimators.linear import TensorFlowLinearRegressor
from tensorflow.contrib.learn.python.learn.estimators.linear import TensorFlowRegressor
from tensorflow.contrib.learn.python.learn.estimators.dnn import TensorFlowDNNClassifier
from tensorflow.contrib.learn.python.learn.estimators.dnn import TensorFlowDNNRegressor
from tensorflow.contrib.learn.python.learn.estimators.rnn import TensorFlowRNNClassifier
from tensorflow.contrib.learn.python.learn.estimators.rnn import TensorFlowRNNRegressor
from tensorflow.contrib.learn.python.learn.estimators.run_config import RunConfig

186
tensorflow/contrib/learn/python/learn/estimators/_sklearn.py Normal file
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@ -0,0 +1,186 @@
"""sklearn cross-support."""
# Copyright 2015-present The Scikit Flow 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
class _BaseEstimator(object):
"""This is a cross-import when sklearn is not available.
Adopted from sklearn.BaseEstimator implementation.
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py
"""
def get_params(self, deep=True):
"""Get parameters for this estimator.
Parameters
----------
deep: boolean, optional
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
params : mapping of string to any
Parameter names mapped to their values.
"""
out = dict()
param_names = [name for name in self.__dict__ if not name.startswith('_')]
for key in param_names:
value = getattr(self, key, None)
# XXX: should we rather test if instance of estimator?
if deep and hasattr(value, 'get_params'):
deep_items = value.get_params().items()
out.update((key + '__' + k, val) for k, val in deep_items)
out[key] = value
return out
def set_params(self, **params):
"""Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The former have parameters of the form
``<component>__<parameter>`` so that it's possible to update each
component of a nested object.
Returns
-------
self
"""
if not params:
# Simple optimisation to gain speed (inspect is slow)
return self
valid_params = self.get_params(deep=True)
for key, value in six.iteritems(params):
split = key.split('__', 1)
if len(split) > 1:
# nested objects case
name, sub_name = split
if name not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(name, self))
sub_object = valid_params[name]
sub_object.set_params(**{sub_name: value})
else:
# simple objects case
if key not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(key, self.__class__.__name__))
setattr(self, key, value)
return self
def __repr__(self):
class_name = self.__class__.__name__
return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False),
offset=len(class_name),),)
class _ClassifierMixin():
"""Mixin class for all classifiers."""
pass
class _RegressorMixin():
"""Mixin class for all regression estimators."""
pass
class _NotFittedError(ValueError, AttributeError):
"""Exception class to raise if estimator is used before fitting.
This class inherits from both ValueError and AttributeError to help with
exception handling and backward compatibility.
Examples
--------
>>> from sklearn.svm import LinearSVC
>>> from sklearn.exceptions import NotFittedError
>>> try:
... LinearSVC().predict([[1, 2], [2, 3], [3, 4]])
... except NotFittedError as e:
... print(repr(e))
... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
NotFittedError('This LinearSVC instance is not fitted yet',)
Copied from https://github.com/scikit-learn/scikit-learn/master/sklearn/exceptions.py
"""
def _accuracy_score(y_true, y_pred):
score = y_true == y_pred
return np.average(score)
def _mean_squared_error(y_true, y_pred):
if len(y_true.shape) > 1:
y_true = np.squeeze(y_true)
if len(y_pred.shape) > 1:
y_pred = np.squeeze(y_pred)
return np.average((y_true - y_pred) ** 2)
def _train_test_split(*args, **options):
n_array = len(args)
test_size = options.pop('test_size', None)
train_size = options.pop('train_size', None)
random_state = options.pop('random_state', None)
if test_size is None and train_size is None:
train_size = 0.75
elif train_size is None:
train_size = 1 - test_size
train_size = train_size * args[0].shape[0]
indices = np.random.permutation(args[0].shape[0])
train_idx, test_idx = indices[:train_size], indices[:train_size]
result = []
for x in args:
result += [x.take(train_idx, axis=0), x.take(test_idx, axis=0)]
return tuple(result)
# Try to import sklearn, if fail - use _BaseEstimator.
try:
from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
except ImportError:
BaseEstimator = _BaseEstimator
ClassifierMixin = _ClassifierMixin
RegressorMixin = _RegressorMixin
# Try to import exception for not fitted error.
try:
from sklearn.exceptions import NotFittedError
except ImportError:
NotFittedError = _NotFittedError
# Try to import metrics
try:
from sklearn.metrics import accuracy_score, log_loss, mean_squared_error
except ImportError:
accuracy_score = _accuracy_score
log_loss = None
mean_squared_error = _mean_squared_error
# Try to import train_test_split
try:
from sklearn.cross_validation import train_test_split
except ImportError:
train_test_split = _train_test_split

224
tensorflow/contrib/skflow/python/skflow/estimators/base.py → tensorflow/contrib/learn/python/learn/estimators/base.py
View File

@ -23,40 +23,53 @@ import shutil
from six import string_types
import numpy as np
import tensorflow as tf
from google.protobuf import text_format
from tensorflow.python.platform.default import _gfile as gfile
from sklearn.base import BaseEstimator
try:
from sklearn.exceptions import NotFittedError
except ImportError:
from sklearn.utils.validation import NotFittedError # pylint: disable=ungrouped-imports
from tensorflow.python.client import session
from tensorflow.core.framework import graph_pb2
from tensorflow.python.framework import ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import importer
from tensorflow.python.framework import random_seed
from tensorflow.python.ops import array_ops as array_ops_
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import constant_op
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import logging_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import variables
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.training import training as train
from ..trainer import TensorFlowTrainer, RestoredTrainer
from ..io.data_feeder import setup_train_data_feeder
from ..io.data_feeder import setup_predict_data_feeder
from ..ops.dropout_ops import DROPOUTS
from .. import monitors
from tensorflow.contrib.layers import optimizers
from tensorflow.contrib.learn.python.learn import trainer
from tensorflow.contrib.learn.python.learn.io.data_feeder import setup_train_data_feeder
from tensorflow.contrib.learn.python.learn.io.data_feeder import setup_predict_data_feeder
from tensorflow.contrib.learn.python.learn.ops.dropout_ops import DROPOUTS
from tensorflow.contrib.learn.python.learn import monitors
from ..addons.config_addon import ConfigAddon
from tensorflow.contrib.learn.python.learn.estimators import _sklearn
from tensorflow.contrib.learn.python.learn.estimators._sklearn import NotFittedError
from tensorflow.contrib.learn.python.learn.estimators.run_config import RunConfig
def _write_with_backup(filename, content):
if os.path.exists(filename):
shutil.move(filename, filename + '.old')
with open(filename, 'w') as f:
if gfile.Exists(filename):
gfile.Rename(filename, filename + '.old', overwrite=True)
with gfile.Open(filename, 'w') as f:
f.write(content)
class TensorFlowEstimator(BaseEstimator):
class TensorFlowEstimator(_sklearn.BaseEstimator):
"""Base class for all TensorFlow estimators.
Parameters:
model_fn: Model function, that takes input X, y tensors and outputs
prediction and loss tensors.
n_classes: Number of classes in the target.
tf_master: TensorFlow master. Empty string is default for local.
batch_size: Mini batch size.
steps: Number of steps to run over data.
optimizer: Optimizer name (or class), for example "SGD", "Adam",
@ -69,115 +82,111 @@ class TensorFlowEstimator(BaseEstimator):
return tf.train.exponential_decay(
learning_rate=0.1, global_step,
decay_steps=2, decay_rate=0.001)
clip_gradients: Clip norm of the gradients to this value to stop
gradient explosion.
class_weight: None or list of n_classes floats. Weight associated with
classes for loss computation. If not given, all classes are suppose to have
weight one.
tf_random_seed: Random seed for TensorFlow initializers.
Setting this value, allows consistency between reruns.
continue_training: when continue_training is True, once initialized
model will be continuely trained on every call of fit.
config_addon: ConfigAddon object that controls the configurations of the session,
config: RunConfig object that controls the configurations of the session,
e.g. num_cores, gpu_memory_fraction, etc.
verbose: Controls the verbosity, possible values:
0: the algorithm and debug information is muted.
1: trainer prints the progress.
2: log device placement is printed.
max_to_keep: The maximum number of recent checkpoint files to keep.
As new files are created, older files are deleted.
If None or 0, all checkpoint files are kept.
Defaults to 5 (that is, the 5 most recent checkpoint files are kept.)
keep_checkpoint_every_n_hours: Number of hours between each checkpoint
to be saved. The default value of 10,000 hours effectively disables the feature.
"""
def __init__(self, model_fn, n_classes, tf_master="", batch_size=32,
def __init__(self, model_fn, n_classes, batch_size=32,
steps=200, optimizer="Adagrad",
learning_rate=0.1, class_weight=None,
tf_random_seed=42, continue_training=False,
config_addon=None, verbose=1,
max_to_keep=5, keep_checkpoint_every_n_hours=10000):
learning_rate=0.1, clip_gradients=5.0, class_weight=None,
continue_training=False,
config=None, verbose=1):
self.model_fn = model_fn
self.n_classes = n_classes
self.tf_master = tf_master
self.batch_size = batch_size
self.steps = steps
self.verbose = verbose
self.optimizer = optimizer
self.learning_rate = learning_rate
self.tf_random_seed = tf_random_seed
self.model_fn = model_fn
self.clip_gradients = clip_gradients
self.continue_training = continue_training
self._initialized = False
self.max_to_keep = max_to_keep
self.keep_checkpoint_every_n_hours = keep_checkpoint_every_n_hours
self.class_weight = class_weight
self.config_addon = config_addon
self._config = config
def _setup_training(self):
"""Sets up graph, model and trainer."""
self._graph = tf.Graph()
# Create config if not given.
if self._config is None:
self._config = RunConfig(verbose=self.verbose)
# Create new graph.
self._graph = ops.Graph()
self._graph.add_to_collection("IS_TRAINING", True)
with self._graph.as_default():
tf.set_random_seed(self.tf_random_seed)
self._global_step = tf.Variable(
random_seed.set_random_seed(self._config.tf_random_seed)
self._global_step = variables.Variable(
0, name="global_step", trainable=False)
# Setting up input and output placeholders.
input_shape = [None] + self._data_feeder.input_shape[1:]
output_shape = [None] + self._data_feeder.output_shape[1:]
self._inp = tf.placeholder(
tf.as_dtype(self._data_feeder.input_dtype), input_shape,
name="input")
self._out = tf.placeholder(
tf.as_dtype(self._data_feeder.output_dtype), output_shape,
name="output")
# Setting up inputs and outputs.
self._inp, self._out = self._data_feeder.input_builder()
# If class weights are provided, add them to the graph.
# Different loss functions can use this tensor by name.
if self.class_weight:
self._class_weight_node = tf.constant(
self._class_weight_node = constant_op.constant(
self.class_weight, name='class_weight')
# Add histograms for X and y if they are floats.
if self._data_feeder.input_dtype in (np.float32, np.float64):
tf.histogram_summary("X", self._inp)
logging_ops.histogram_summary("X", self._inp)
if self._data_feeder.output_dtype in (np.float32, np.float64):
tf.histogram_summary("y", self._out)
logging_ops.histogram_summary("y", self._out)
# Create model's graph.
self._model_predictions, self._model_loss = self.model_fn(
self._inp, self._out)
# Create summary to monitor loss
tf.scalar_summary("loss", self._model_loss)
# Set up a single operator to merge all the summaries
self._summaries = tf.merge_all_summaries()
self._summaries = logging_ops.merge_all_summaries()
# Create trainer and augment graph with gradients and optimizer.
# Additionally creates initialization ops.
self._trainer = TensorFlowTrainer(
loss=self._model_loss, global_step=self._global_step,
optimizer=self.optimizer, learning_rate=self.learning_rate)
learning_rate = self.learning_rate
optimizer = self.optimizer
if callable(learning_rate):
learning_rate = learning_rate(self._global_step)
if callable(optimizer):
optimizer = optimizer(learning_rate)
self._train = optimizers.optimize_loss(self._model_loss, self._global_step,
learning_rate=learning_rate,
optimizer=optimizer, clip_gradients=self.clip_gradients)
# Update ops during training, e.g. batch_norm_ops
self._train = control_flow_ops.group(self._train, *ops.get_collection('update_ops'))
# Get all initializers for all trainable variables.
self._initializers = variables.initialize_all_variables()
# Create model's saver capturing all the nodes created up until now.
self._saver = tf.train.Saver(
max_to_keep=self.max_to_keep,
keep_checkpoint_every_n_hours=self.keep_checkpoint_every_n_hours)
self._saver = train.Saver(
max_to_keep=self._config.keep_checkpoint_max,
keep_checkpoint_every_n_hours=self._config.keep_checkpoint_every_n_hours)
# Enable monitor to create validation data dict with appropriate tf placeholders
self._monitor.create_val_feed_dict(self._inp, self._out)
# Create session to run model with.
if self.config_addon is None:
self.config_addon = ConfigAddon(verbose=self.verbose)
self._session = tf.Session(self.tf_master, config=self.config_addon.config)
self._session = session.Session(self._config.tf_master, config=self._config.tf_config)
# Run parameter initializers.
self._session.run(self._initializers)
def _setup_summary_writer(self, logdir):
"""Sets up the summary writer to prepare for later optional visualization."""
self._summary_writer = tf.train.SummaryWriter(
self._summary_writer = train.SummaryWriter(
os.path.join(logdir, datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')),
graph_def=self._session.graph_def)
graph=self._session.graph)
def fit(self, X, y, monitor=None, logdir=None):
"""Builds a neural network model given provided `model_fn` and training
@ -216,9 +225,9 @@ class TensorFlowEstimator(BaseEstimator):
if not self.continue_training or not self._initialized:
# Sets up model and trainer.
self._setup_training()
# Initialize model parameters.
self._trainer.initialize(self._session)
self._initialized = True
else:
self._data_feeder.set_placeholders(self._inp, self._out)
# Sets up summary writer for later optional visualization.
# Due to not able to setup _summary_writer in __init__ as it's not a
@ -234,14 +243,15 @@ class TensorFlowEstimator(BaseEstimator):
self._summary_writer = None
# Train model for given number of steps.
self._trainer.train(self._session,
self._data_feeder.get_feed_dict_fn(
self._inp, self._out),
self.steps,
self._monitor,
self._summary_writer,
self._summaries,
feed_params_fn=self._data_feeder.get_feed_params)
trainer.train(
self._session, self._train,
self._model_loss, self._global_step,
self._data_feeder.get_feed_dict_fn(),
steps=self.steps,
monitor=self._monitor,
summary_writer=self._summary_writer,
summaries=self._summaries,
feed_params_fn=self._data_feeder.get_feed_params)
return self
def partial_fit(self, X, y):
@ -270,7 +280,7 @@ class TensorFlowEstimator(BaseEstimator):
def _predict(self, X, axis=-1, batch_size=None):
if not self._initialized:
raise NotFittedError()
raise _sklearn.NotFittedError()
# Use the batch size for fitting if the user did not specify one.
if batch_size is None:
@ -361,7 +371,7 @@ class TensorFlowEstimator(BaseEstimator):
path: Folder to save model to.
"""
if not self._initialized:
raise NotFittedError()
raise _sklearn.NotFittedError()
# Currently Saver requires absolute path to work correctly.
path = os.path.abspath(path)
@ -394,7 +404,7 @@ class TensorFlowEstimator(BaseEstimator):
# Save graph definition.
_write_with_backup(os.path.join(path, 'graph.pbtxt'), str(self._graph.as_graph_def()))
# Save saver defintion.
# Save saver definition.
_write_with_backup(os.path.join(path, 'saver.pbtxt'), str(self._saver.as_saver_def()))
# Save checkpoints.
@ -413,46 +423,44 @@ class TensorFlowEstimator(BaseEstimator):
# Currently Saver requires absolute path to work correctly.
path = os.path.abspath(path)
self._graph = tf.Graph()
self._graph = ops.Graph()
with self._graph.as_default():
endpoints_filename = os.path.join(path, 'endpoints')
if not os.path.exists(endpoints_filename):
raise ValueError("Restore folder doesn't contain endpoints.")
with open(endpoints_filename) as foutputs:
with gfile.Open(endpoints_filename) as foutputs:
endpoints = foutputs.read().split('\n')
graph_filename = os.path.join(path, 'graph.pbtxt')
if not os.path.exists(graph_filename):
raise ValueError("Restore folder doesn't contain graph definition.")
with open(graph_filename) as fgraph:
graph_def = tf.GraphDef()
with gfile.Open(graph_filename) as fgraph:
graph_def = graph_pb2.GraphDef()
text_format.Merge(fgraph.read(), graph_def)
(self._inp, self._out,
self._model_predictions, self._model_loss) = tf.import_graph_def(
self._model_predictions, self._model_loss) = importer.import_graph_def(
graph_def, name='', return_elements=endpoints)
saver_filename = os.path.join(path, 'saver.pbtxt')
if not os.path.exists(saver_filename):
raise ValueError("Restore folder doesn't contain saver defintion.")
with open(saver_filename) as fsaver:
saver_def = tf.train.SaverDef()
raise ValueError("Restore folder doesn't contain saver definition.")
with gfile.Open(saver_filename) as fsaver:
saver_def = train.SaverDef()
text_format.Merge(fsaver.read(), saver_def)
self._saver = tf.train.Saver(saver_def=saver_def)
self._saver = train.Saver(saver_def=saver_def)
# Restore trainer
self._global_step = self._graph.get_tensor_by_name('global_step:0')
trainer_op = self._graph.get_operation_by_name('train')
self._trainer = RestoredTrainer(
self._model_loss, self._global_step, trainer_op)
self._train = self._graph.get_operation_by_name('train')
# Restore summaries.
self._summaries = self._graph.get_operation_by_name('MergeSummary/MergeSummary')
# Restore session.
if not isinstance(self.config_addon, ConfigAddon):
self.config_addon = ConfigAddon(verbose=self.verbose)
self._session = tf.Session(
self.tf_master,
config=self.config_addon.config)
checkpoint_path = tf.train.latest_checkpoint(path)
if not isinstance(self._config, RunConfig):
self._config = RunConfig(verbose=self.verbose)
self._session = session.Session(
self._config.tf_master,
config=self._config.tf_config)
checkpoint_path = train.latest_checkpoint(path)
if checkpoint_path is None:
raise ValueError("Missing checkpoint files in the %s. Please "
"make sure you are you have checkpoint file that describes "
@ -465,12 +473,12 @@ class TensorFlowEstimator(BaseEstimator):
# pylint: disable=unused-argument
@classmethod
def restore(cls, path, config_addon=None):
def restore(cls, path, config=None):
"""Restores model from give path.
Args:
path: Path to the checkpoints and other model information.
config_addon: ConfigAddon object that controls the configurations of the session,
config: RunConfig object that controls the configurations of the session,
e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be reconfigured.
Returns:
@ -480,8 +488,9 @@ class TensorFlowEstimator(BaseEstimator):
if not os.path.exists(model_def_filename):
raise ValueError("Restore folder doesn't contain model definition.")
# list of parameters that are allowed to be reconfigured
reconfigurable_params = ['config_addon']
with open(model_def_filename) as fmodel:
reconfigurable_params = ['_config']
_config = config
with gfile.Open(model_def_filename) as fmodel:
model_def = json.loads(fmodel.read())
# TensorFlow binding requires parameters to be strings not unicode.
# Only issue in Python2.
@ -490,9 +499,9 @@ class TensorFlowEstimator(BaseEstimator):
not isinstance(value, str)):
model_def[key] = str(value)
if key in reconfigurable_params:
newValue = locals()[key]
if newValue is not None:
model_def[key] = newValue
new_value = locals()[key]
if new_value is not None:
model_def[key] = new_value
class_name = model_def.pop('class_name')
if class_name == 'TensorFlowEstimator':
custom_estimator = TensorFlowEstimator(model_fn=None, **model_def)
@ -501,8 +510,9 @@ class TensorFlowEstimator(BaseEstimator):
# To avoid cyclical dependencies, import inside the function instead of
# the beginning of the file.
from tensorflow.contrib.skflow.python.skflow import estimators
from tensorflow.contrib.learn.python.learn import estimators
# Estimator must be one of the defined estimators in the __init__ file.
estimator = getattr(estimators, class_name)(**model_def)
estimator._restore(path)
return estimator

72
tensorflow/contrib/skflow/python/skflow/estimators/dnn.py → tensorflow/contrib/learn/python/learn/estimators/dnn.py
View File

@ -16,19 +16,17 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn.base import ClassifierMixin, RegressorMixin
from .base import TensorFlowEstimator
from .. import models
from tensorflow.contrib.learn.python.learn.estimators import _sklearn
from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowEstimator
from tensorflow.contrib.learn.python.learn import models
class TensorFlowDNNClassifier(TensorFlowEstimator, ClassifierMixin):
class TensorFlowDNNClassifier(TensorFlowEstimator, _sklearn.ClassifierMixin):
"""TensorFlow DNN Classifier model.
Parameters:
hidden_units: List of hidden units per layer.
n_classes: Number of classes in the target.
tf_master: TensorFlow master. Empty string is default for local.
batch_size: Mini batch size.
steps: Number of steps to run over data.
optimizer: Optimizer name (or class), for example "SGD", "Adam",
@ -44,41 +42,29 @@ class TensorFlowDNNClassifier(TensorFlowEstimator, ClassifierMixin):
class_weight: None or list of n_classes floats. Weight associated with
classes for loss computation. If not given, all classes are suppose to have
weight one.
tf_random_seed: Random seed for TensorFlow initializers.
Setting this value, allows consistency between reruns.
continue_training: when continue_training is True, once initialized
model will be continuely trained on every call of fit.
config_addon: ConfigAddon object that controls the configurations of the session,
config: RunConfig object that controls the configurations of the session,
e.g. num_cores, gpu_memory_fraction, etc.
max_to_keep: The maximum number of recent checkpoint files to keep.
As new files are created, older files are deleted.
If None or 0, all checkpoint files are kept.
Defaults to 5 (that is, the 5 most recent checkpoint files are kept.)
keep_checkpoint_every_n_hours: Number of hours between each checkpoint
to be saved. The default value of 10,000 hours effectively disables the feature.
dropout: When not None, the probability we will drop out a given
coordinate.
"""
"""
def __init__(self, hidden_units, n_classes, tf_master="", batch_size=32,
def __init__(self, hidden_units, n_classes, batch_size=32,
steps=200, optimizer="Adagrad", learning_rate=0.1,
class_weight=None,
tf_random_seed=42, continue_training=False, config_addon=None,
verbose=1, max_to_keep=5, keep_checkpoint_every_n_hours=10000,
dropout=None):
class_weight=None, clip_gradients=5.0,
continue_training=False, config=None,
verbose=1, dropout=None):
self.hidden_units = hidden_units
self.dropout = dropout
super(TensorFlowDNNClassifier, self).__init__(
model_fn=self._model_fn,
n_classes=n_classes, tf_master=tf_master,
n_classes=n_classes,
batch_size=batch_size, steps=steps, optimizer=optimizer,
learning_rate=learning_rate, class_weight=class_weight,
tf_random_seed=tf_random_seed,
clip_gradients=clip_gradients,
continue_training=continue_training,
config_addon=config_addon, verbose=verbose,
max_to_keep=max_to_keep,
keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)
config=config, verbose=verbose)
def _model_fn(self, X, y):
return models.get_dnn_model(self.hidden_units,
@ -104,12 +90,11 @@ class TensorFlowDNNClassifier(TensorFlowEstimator, ClassifierMixin):
return biases
class TensorFlowDNNRegressor(TensorFlowEstimator, RegressorMixin):
class TensorFlowDNNRegressor(TensorFlowEstimator, _sklearn.RegressorMixin):
"""TensorFlow DNN Regressor model.
Parameters:
hidden_units: List of hidden units per layer.
tf_master: TensorFlow master. Empty string is default for local.
batch_size: Mini batch size.
steps: Number of steps to run over data.
optimizer: Optimizer name (or class), for example "SGD", "Adam",
@ -122,43 +107,32 @@ class TensorFlowDNNRegressor(TensorFlowEstimator, RegressorMixin):
return tf.train.exponential_decay(
learning_rate=0.1, global_step,
decay_steps=2, decay_rate=0.001)
tf_random_seed: Random seed for TensorFlow initializers.
Setting this value, allows consistency between reruns.
continue_training: when continue_training is True, once initialized
model will be continuely trained on every call of fit.
config_addon: ConfigAddon object that controls the configurations of the session,
config: RunConfig object that controls the configurations of the session,
e.g. num_cores, gpu_memory_fraction, etc.
verbose: Controls the verbosity, possible values:
0: the algorithm and debug information is muted.
1: trainer prints the progress.
2: log device placement is printed.
max_to_keep: The maximum number of recent checkpoint files to keep.
As new files are created, older files are deleted.
If None or 0, all checkpoint files are kept.
Defaults to 5 (that is, the 5 most recent checkpoint files are kept.)
keep_checkpoint_every_n_hours: Number of hours between each checkpoint
to be saved. The default value of 10,000 hours effectively disables the feature.
dropout: When not None, the probability we will drop out a given
coordinate.
"""
"""
def __init__(self, hidden_units, n_classes=0, tf_master="", batch_size=32,
def __init__(self, hidden_units, n_classes=0, batch_size=32,
steps=200, optimizer="Adagrad", learning_rate=0.1,
tf_random_seed=42, continue_training=False, config_addon=None,
verbose=1, max_to_keep=5, keep_checkpoint_every_n_hours=10000,
dropout=None):
clip_gradients=5.0,
continue_training=False, config=None,
verbose=1, dropout=None):
self.hidden_units = hidden_units
self.dropout = dropout
super(TensorFlowDNNRegressor, self).__init__(
model_fn=self._model_fn,
n_classes=n_classes, tf_master=tf_master,
n_classes=n_classes,
batch_size=batch_size, steps=steps, optimizer=optimizer,
learning_rate=learning_rate, tf_random_seed=tf_random_seed,
learning_rate=learning_rate, clip_gradients=clip_gradients,
continue_training=continue_training,
config_addon=config_addon, verbose=verbose,
max_to_keep=max_to_keep,
keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)
config=config, verbose=verbose)
def _model_fn(self, X, y):
return models.get_dnn_model(self.hidden_units,

42
tensorflow/contrib/skflow/python/skflow/estimators/linear.py → tensorflow/contrib/learn/python/learn/estimators/linear.py
View File

@ -16,28 +16,24 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn.base import ClassifierMixin, RegressorMixin
from .base import TensorFlowEstimator
from .. import models
from tensorflow.contrib.learn.python.learn.estimators import _sklearn
from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowEstimator
from tensorflow.contrib.learn.python.learn import models
class TensorFlowLinearRegressor(TensorFlowEstimator, RegressorMixin):
class TensorFlowLinearRegressor(TensorFlowEstimator, _sklearn.RegressorMixin):
"""TensorFlow Linear Regression model."""
def __init__(self, n_classes=0, tf_master="", batch_size=32, steps=200, optimizer="Adagrad",
learning_rate=0.1, tf_random_seed=42, continue_training=False,
config_addon=None, verbose=1,
max_to_keep=5, keep_checkpoint_every_n_hours=10000):
def __init__(self, n_classes=0, batch_size=32, steps=200, optimizer="Adagrad",
learning_rate=0.1, clip_gradients=5.0, continue_training=False,
config=None, verbose=1):
super(TensorFlowLinearRegressor, self).__init__(
model_fn=models.linear_regression_zero_init, n_classes=n_classes,
tf_master=tf_master,
batch_size=batch_size, steps=steps, optimizer=optimizer,
learning_rate=learning_rate, tf_random_seed=tf_random_seed,
continue_training=continue_training, config_addon=config_addon,
verbose=verbose, max_to_keep=max_to_keep,
keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)
learning_rate=learning_rate, clip_gradients=clip_gradients,
continue_training=continue_training, config=config,
verbose=verbose)
@property
def weights_(self):
@ -50,23 +46,21 @@ class TensorFlowLinearRegressor(TensorFlowEstimator, RegressorMixin):
return self.get_tensor_value('linear_regression/bias:0')
class TensorFlowLinearClassifier(TensorFlowEstimator, ClassifierMixin):
class TensorFlowLinearClassifier(TensorFlowEstimator, _sklearn.ClassifierMixin):
"""TensorFlow Linear Classifier model."""
def __init__(self, n_classes, tf_master="", batch_size=32, steps=200, optimizer="Adagrad",
learning_rate=0.1, class_weight=None,
tf_random_seed=42, continue_training=False, config_addon=None,
verbose=1, max_to_keep=5, keep_checkpoint_every_n_hours=10000):
def __init__(self, n_classes, batch_size=32, steps=200, optimizer="Adagrad",
learning_rate=0.1, class_weight=None, clip_gradients=5.0,
continue_training=False, config=None,
verbose=1):
super(TensorFlowLinearClassifier, self).__init__(
model_fn=models.logistic_regression_zero_init, n_classes=n_classes,
tf_master=tf_master,
batch_size=batch_size, steps=steps, optimizer=optimizer,
learning_rate=learning_rate, class_weight=class_weight,
tf_random_seed=tf_random_seed,
continue_training=continue_training, config_addon=config_addon,
verbose=verbose, max_to_keep=max_to_keep,
keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)
clip_gradients=clip_gradients,
continue_training=continue_training, config=config,
verbose=verbose)
@property
def weights_(self):

72
tensorflow/contrib/skflow/python/skflow/estimators/rnn.py → tensorflow/contrib/learn/python/learn/estimators/rnn.py
View File

@ -16,10 +16,9 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn.base import ClassifierMixin, RegressorMixin
from .base import TensorFlowEstimator
from .. import models
from tensorflow.contrib.learn.python.learn.estimators import _sklearn
from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowEstimator
from tensorflow.contrib.learn.python.learn import models
def null_input_op_fn(X):
@ -27,7 +26,7 @@ def null_input_op_fn(X):
return X
class TensorFlowRNNClassifier(TensorFlowEstimator, ClassifierMixin):
class TensorFlowRNNClassifier(TensorFlowEstimator, _sklearn.ClassifierMixin):
"""TensorFlow RNN Classifier model.
Parameters:
@ -43,7 +42,6 @@ class TensorFlowRNNClassifier(TensorFlowEstimator, ClassifierMixin):
initial_state: An initial state for the RNN. This must be a tensor of appropriate type
and shape [batch_size x cell.state_size].
n_classes: Number of classes in the target.
tf_master: TensorFlow master. Empty string is default for local.
batch_size: Mini batch size.
steps: Number of steps to run over data.
optimizer: Optimizer name (or class), for example "SGD", "Adam",
@ -59,28 +57,20 @@ class TensorFlowRNNClassifier(TensorFlowEstimator, ClassifierMixin):
class_weight: None or list of n_classes floats. Weight associated with
classes for loss computation. If not given, all classes are suppose to have
weight one.
tf_random_seed: Random seed for TensorFlow initializers.
Setting this value, allows consistency between reruns.
continue_training: when continue_training is True, once initialized
model will be continuely trained on every call of fit.
num_cores: Number of cores to be used. (default: 4)
max_to_keep: The maximum number of recent checkpoint files to keep.
As new files are created, older files are deleted.
If None or 0, all checkpoint files are kept.
Defaults to 5 (that is, the 5 most recent checkpoint files are kept.)
keep_checkpoint_every_n_hours: Number of hours between each checkpoint
to be saved. The default value of 10,000 hours effectively disables the feature.
config: RunConfig object that controls the configurations of the session,
e.g. num_cores, gpu_memory_fraction, etc.
"""
def __init__(self, rnn_size, n_classes, cell_type='gru', num_layers=1,
input_op_fn=null_input_op_fn,
initial_state=None, bidirectional=False,
sequence_length=None, tf_master="", batch_size=32,
sequence_length=None, batch_size=32,
steps=50, optimizer="Adagrad", learning_rate=0.1,
class_weight=None,
tf_random_seed=42, continue_training=False,
config_addon=None, verbose=1,
max_to_keep=5, keep_checkpoint_every_n_hours=10000):
class_weight=None, clip_gradients=5.0,
continue_training=False,
config=None, verbose=1):
self.rnn_size = rnn_size
self.cell_type = cell_type
@ -91,14 +81,12 @@ class TensorFlowRNNClassifier(TensorFlowEstimator, ClassifierMixin):
self.initial_state = initial_state
super(TensorFlowRNNClassifier, self).__init__(
model_fn=self._model_fn,
n_classes=n_classes, tf_master=tf_master,
n_classes=n_classes,
batch_size=batch_size, steps=steps, optimizer=optimizer,
learning_rate=learning_rate, class_weight=class_weight,
tf_random_seed=tf_random_seed,
continue_training=continue_training, config_addon=config_addon,
verbose=verbose,
max_to_keep=max_to_keep,
keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)
clip_gradients=clip_gradients,
continue_training=continue_training, config=config,
verbose=verbose)
def _model_fn(self, X, y):
return models.get_rnn_model(self.rnn_size, self.cell_type,
@ -119,7 +107,7 @@ class TensorFlowRNNClassifier(TensorFlowEstimator, ClassifierMixin):
return self.get_tensor_value('logistic_regression/weights:0')
class TensorFlowRNNRegressor(TensorFlowEstimator, RegressorMixin):
class TensorFlowRNNRegressor(TensorFlowEstimator, _sklearn.RegressorMixin):
"""TensorFlow RNN Regressor model.
Parameters:
@ -134,7 +122,6 @@ class TensorFlowRNNRegressor(TensorFlowEstimator, RegressorMixin):
This saves computational time when unrolling past max sequence length.
initial_state: An initial state for the RNN. This must be a tensor of appropriate type
and shape [batch_size x cell.state_size].
tf_master: TensorFlow master. Empty string is default for local.
batch_size: Mini batch size.
steps: Number of steps to run over data.
optimizer: Optimizer name (or class), for example "SGD", "Adam",
@ -147,32 +134,24 @@ class TensorFlowRNNRegressor(TensorFlowEstimator, RegressorMixin):
return tf.train.exponential_decay(
learning_rate=0.1, global_step,
decay_steps=2, decay_rate=0.001)
tf_random_seed: Random seed for TensorFlow initializers.
Setting this value, allows consistency between reruns.
continue_training: when continue_training is True, once initialized
model will be continuely trained on every call of fit.
num_cores: Number of cores to be used. (default: 4)
config: RunConfig object that controls the configurations of the session,
e.g. num_cores, gpu_memory_fraction, etc.
verbose: Controls the verbosity, possible values:
0: the algorithm and debug information is muted.
1: trainer prints the progress.
2: log device placement is printed.
max_to_keep: The maximum number of recent checkpoint files to keep.
As new files are created, older files are deleted.
If None or 0, all checkpoint files are kept.
Defaults to 5 (that is, the 5 most recent checkpoint files are kept.)
keep_checkpoint_every_n_hours: Number of hours between each checkpoint
to be saved. The default value of 10,000 hours effectively disables the feature.
"""
def __init__(self, rnn_size, cell_type='gru', num_layers=1,
input_op_fn=null_input_op_fn, initial_state=None,
bidirectional=False, sequence_length=None,
n_classes=0, tf_master="", batch_size=32,
n_classes=0, batch_size=32,
steps=50, optimizer="Adagrad", learning_rate=0.1,
tf_random_seed=42, continue_training=False,
config_addon=None, verbose=1,
max_to_keep=5, keep_checkpoint_every_n_hours=10000):
clip_gradients=5.0,
continue_training=False,
config=None, verbose=1):
self.rnn_size = rnn_size
self.cell_type = cell_type
self.input_op_fn = input_op_fn
@ -182,12 +161,11 @@ class TensorFlowRNNRegressor(TensorFlowEstimator, RegressorMixin):
self.initial_state = initial_state
super(TensorFlowRNNRegressor, self).__init__(
model_fn=self._model_fn,
n_classes=n_classes, tf_master=tf_master,
n_classes=n_classes,
batch_size=batch_size, steps=steps, optimizer=optimizer,
learning_rate=learning_rate, tf_random_seed=tf_random_seed,
continue_training=continue_training, config_addon=config_addon,
verbose=verbose, max_to_keep=max_to_keep,
keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)
learning_rate=learning_rate, clip_gradients=clip_gradients,
continue_training=continue_training, config=config,
verbose=verbose)
def _model_fn(self, X, y):
return models.get_rnn_model(self.rnn_size, self.cell_type,

64
tensorflow/contrib/learn/python/learn/estimators/run_config.py Normal file
View File

@ -0,0 +1,64 @@
"""Run Config."""
# Copyright 2015-present The Scikit Flow 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python import GPUOptions, ConfigProto
class RunConfig(object):
"""This class specifies the specific configurations for the run.
Parameters:
tf_master: TensorFlow master. Empty string is default for local.
num_cores: Number of cores to be used. (default: 4)
verbose: Controls the verbosity, possible values:
0: the algorithm and debug information is muted.
1: trainer prints the progress.
2: log device placement is printed.
gpu_memory_fraction: Fraction of GPU memory used by the process on
each GPU uniformly on the same machine.
tf_random_seed: Random seed for TensorFlow initializers.
Setting this value, allows consistency between reruns.
keep_checkpoint_max: The maximum number of recent checkpoint files to keep.
As new files are created, older files are deleted.
If None or 0, all checkpoint files are kept.
Defaults to 5 (that is, the 5 most recent checkpoint files are kept.)
keep_checkpoint_every_n_hours: Number of hours between each checkpoint
to be saved. The default value of 10,000 hours effectively disables the feature.
Attributes:
tf_master: Tensorflow master.
tf_config: Tensorflow Session Config proto.
tf_random_seed: Tensorflow random seed.
keep_checkpoint_max: Maximum number of checkpoints to keep.
keep_checkpoint_every_n_hours: Number of hours between each checkpoint.
"""
def __init__(self, tf_master='', num_cores=4, verbose=1,
gpu_memory_fraction=1, tf_random_seed=42,
keep_checkpoint_max=5,
keep_checkpoint_every_n_hours=10000):
self.tf_master = tf_master
gpu_options = GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
self.tf_config = ConfigProto(log_device_placement=(verbose > 1),
inter_op_parallelism_threads=num_cores,
intra_op_parallelism_threads=num_cores,
gpu_options=gpu_options)
self.tf_random_seed = tf_random_seed
self.keep_checkpoint_max = keep_checkpoint_max
self.keep_checkpoint_every_n_hours = keep_checkpoint_every_n_hours

4
tensorflow/contrib/skflow/python/skflow/io/__init__.py → tensorflow/contrib/learn/python/learn/io/__init__.py
View File

@ -16,5 +16,5 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.skflow.python.skflow.io.pandas_io import *
from tensorflow.contrib.skflow.python.skflow.io.dask_io import *
from tensorflow.contrib.learn.python.learn.io.pandas_io import *
from tensorflow.contrib.learn.python.learn.io.dask_io import *

0
tensorflow/contrib/skflow/python/skflow/io/dask_io.py → tensorflow/contrib/learn/python/learn/io/dask_io.py
View File

128
tensorflow/contrib/skflow/python/skflow/io/data_feeder.py → tensorflow/contrib/learn/python/learn/io/data_feeder.py
View File

@ -24,7 +24,9 @@ import six
from six.moves import xrange # pylint: disable=redefined-builtin
import numpy as np
from sklearn.utils import check_array
from tensorflow.python.ops import array_ops
from tensorflow.python.framework import dtypes
from .pandas_io import HAS_PANDAS, extract_pandas_data, extract_pandas_matrix, extract_pandas_labels
from .dask_io import HAS_DASK, extract_dask_data, extract_dask_labels
@ -36,16 +38,15 @@ def _get_in_out_shape(x_shape, y_shape, n_classes, batch_size):
input_shape = [batch_size] + x_shape
if y_shape is None:
return input_shape, None
y_shape = list(y_shape[1:]) if len(y_shape) > 1 else []
# Skip first dimension if it is 1.
if y_shape and y_shape[0] == 1:
y_shape = y_shape[1:]
if n_classes > 1:
output_shape = [batch_size] + y_shape + [n_classes]
else:
y_shape = list(y_shape[1:]) if len(y_shape) > 1 else []
# Skip first dimention if it is 1.
if y_shape and y_shape[0] == 1:
y_shape = y_shape[1:]
if n_classes > 1:
output_shape = [batch_size] + y_shape + [n_classes]
else:
output_shape = [batch_size] + y_shape
return input_shape, output_shape
output_shape = [batch_size] + y_shape
return input_shape, output_shape
def _data_type_filter(X, y):
@ -147,6 +148,20 @@ def setup_processor_data_feeder(X):
return X
def check_array(array, dtype):
"""Checks array on dtype and convers it if different.
Args:
array: Input array.
dtype: Expected dtype.
Returns:
Original array or converted.
"""
array = np.array(array, dtype=dtype, order=None, copy=False)
return array
class DataFeeder(object):
"""Data feeder is an example class to sample data for TF trainer.
@ -173,10 +188,8 @@ class DataFeeder(object):
def __init__(self, X, y, n_classes, batch_size, random_state=None):
x_dtype = np.int64 if X.dtype == np.int64 else np.float32
y_dtype = np.int64 if n_classes > 1 else np.float32
self.X = check_array(X, ensure_2d=False,
allow_nd=True, dtype=x_dtype)
self.y = (None if y is None
else check_array(y, ensure_2d=False, dtype=y_dtype))
self.X = check_array(X, dtype=x_dtype)
self.y = (None if y is None else check_array(y, dtype=y_dtype))
self.n_classes = n_classes
self.batch_size = batch_size
self.input_shape, self.output_shape = _get_in_out_shape(
@ -194,6 +207,36 @@ class DataFeeder(object):
self.offset = 0
self.epoch = 0
def input_builder(self):
"""Builds inputs in the graph.
Returns:
Two placeholders for inputs and outputs.
"""
input_shape = [None] + self.input_shape[1:]
self._input_placeholder = array_ops.placeholder(dtypes.as_dtype(self.input_dtype), input_shape,
name="input")
if self.output_shape is None:
self._output_placeholder = None
else:
output_shape = [None] + self.output_shape[1:]
self._output_placeholder = array_ops.placeholder(dtypes.as_dtype(self.output_dtype), output_shape,
name="output")
return self._input_placeholder, self._output_placeholder
def set_placeholders(self, input_placeholder, output_placeholder):
"""Sets placeholders for this data feeder.
Args:
input_placeholder: Placeholder for `X` variable. Should match shape
of the examples in the X dataset.
output_placeholder: Placeholder for `y` variable. Should match
shape of the examples in the y dataset. Can be
None.
"""
self._input_placeholder = input_placeholder
self._output_placeholder = output_placeholder
def get_feed_params(self):
"""Function returns a dict with data feed params while training.
Returns:
@ -205,7 +248,7 @@ class DataFeeder(object):
'batch_size': self.batch_size
}
def get_feed_dict_fn(self, input_placeholder, output_placeholder=None):
def get_feed_dict_fn(self):
"""Returns a function, that will sample data and provide it to given
placeholders.
@ -216,6 +259,7 @@ class DataFeeder(object):
A function that when called samples a random subset of batch size
from X and y.
"""
assert self._input_placeholder != None
def _feed_dict_fn():
# take random indices
batch_indices = self.indices[self.offset: self.offset+self.batch_size]
@ -224,38 +268,36 @@ class DataFeeder(object):
inp = np.array(self.X[batch_indices]).reshape((batch_indices.shape[0], 1)) \
if len(self.X.shape) == 1 else self.X[batch_indices]
if output_placeholder is None:
return {input_placeholder.name: inp}
else:
assert self.y is not None
assert self.output_shape is not None
if self._output_placeholder is None:
return {self._input_placeholder.name: inp}
# assign labels from random indices
self.output_shape[0] = batch_indices.shape[0]
out = np.zeros(self.output_shape, dtype=self.output_dtype)
for i in xrange(out.shape[0]):
sample = batch_indices[i]
if self.n_classes > 1:
if len(self.output_shape) == 2:
out.itemset((i, self.y[sample]), 1.0)
else:
for idx, value in enumerate(self.y[sample]):
out.itemset(tuple([i, idx, value]), 1.0)
else:
out[i] = self.y[sample]
# assign labels from random indices
self.output_shape[0] = batch_indices.shape[0]
out = np.zeros(self.output_shape, dtype=self.output_dtype)
for i in xrange(out.shape[0]):
sample = batch_indices[i]
if self.n_classes > 1:
if len(self.output_shape) == 2:
out.itemset((i, self.y[sample]), 1.0)
else:
for idx, value in enumerate(self.y[sample]):
out.itemset(tuple([i, idx, value]), 1.0)
else:
out[i] = self.y[sample]
# move offset and reset it if necessary
self.offset += self.batch_size
if self.offset >= self.X.shape[0]:
self.indices = self.random_state.permutation(self.X.shape[0])
self.offset = 0
self.epoch += 1
# move offset and reset it if necessary
self.offset += self.batch_size
if self.offset >= self.X.shape[0]:
self.indices = self.random_state.permutation(self.X.shape[0])
self.offset = 0
self.epoch += 1
return {input_placeholder.name: inp, output_placeholder.name: out}
return {self._input_placeholder.name: inp,
self._output_placeholder.name: out}
return _feed_dict_fn
class StreamingDataFeeder(object):
class StreamingDataFeeder(DataFeeder):
"""Data feeder for TF trainer that reads data from iterator.
Streaming data feeder allows to read data as it comes it from disk or
@ -305,7 +347,7 @@ class StreamingDataFeeder(object):
"""
return {'batch_size': self.batch_size}
def get_feed_dict_fn(self, input_placeholder, output_placeholder):
def get_feed_dict_fn(self):
"""Returns a function, that will sample data and provide it to given
placeholders.
@ -331,7 +373,7 @@ class StreamingDataFeeder(object):
out.itemset(tuple([i, idx, value]), 1.0)
else:
out[i] = y
return {input_placeholder.name: inp, output_placeholder.name: out}
return {self._input_placeholder.name: inp, self._output_placeholder.name: out}
return _feed_dict_fn

0
tensorflow/contrib/skflow/python/skflow/io/pandas_io.py → tensorflow/contrib/learn/python/learn/io/pandas_io.py
View File

99
tensorflow/contrib/skflow/python/skflow/models.py → tensorflow/contrib/learn/python/learn/models.py
View File

@ -16,9 +16,16 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops as array_ops_
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import logging_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import variable_scope as vs
from .ops import mean_squared_error_regressor, softmax_classifier, dnn
from tensorflow.contrib.learn.python.learn.ops import mean_squared_error_regressor, softmax_classifier, dnn
def linear_regression_zero_init(X, y):
@ -71,9 +78,9 @@ def linear_regression(X, y, init_mean=None, init_stddev=1.0):
is desirable for convex use cases.) If init_mean is None, then the
uniform_unit_scaling_initialzer will be used.
"""
with tf.variable_scope('linear_regression'):
tf.histogram_summary('linear_regression.X', X)
tf.histogram_summary('linear_regression.y', y)
with vs.variable_scope('linear_regression'):
logging_ops.histogram_summary('linear_regression.X', X)
logging_ops.histogram_summary('linear_regression.y', y)
y_shape = y.get_shape()
if len(y_shape) == 1:
output_shape = 1
@ -81,21 +88,21 @@ def linear_regression(X, y, init_mean=None, init_stddev=1.0):
output_shape = y_shape[1]
# Set up the requested initialization.
if (init_mean is None):
weights = tf.get_variable('weights',
weights = vs.get_variable('weights',
[X.get_shape()[1], output_shape])
bias = tf.get_variable('bias',
bias = vs.get_variable('bias',
[output_shape])
else:
weights = tf.get_variable('weights',
weights = vs.get_variable('weights',
[X.get_shape()[1], output_shape],
initializer=tf.random_normal_initializer(
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev))
bias = tf.get_variable('bias',
bias = vs.get_variable('bias',
[output_shape],
initializer=tf.random_normal_initializer(
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev))
tf.histogram_summary('linear_regression.weights', weights)
tf.histogram_summary('linear_regression.bias', bias)
logging_ops.histogram_summary('linear_regression.weights', weights)
logging_ops.histogram_summary('linear_regression.bias', bias)
return mean_squared_error_regressor(X, y, weights, bias)
@ -126,31 +133,31 @@ def logistic_regression(X, y, class_weight=None, init_mean=None,
is desirable for convex use cases.) If init_mean is None, then the
uniform_unit_scaling_initialzer will be used.
"""
with tf.variable_scope('logistic_regression'):
tf.histogram_summary('logistic_regression.X', X)
tf.histogram_summary('logistic_regression.y', y)
with vs.variable_scope('logistic_regression'):
logging_ops.histogram_summary('logistic_regression.X', X)
logging_ops.histogram_summary('logistic_regression.y', y)
# Set up the requested initialization.
if (init_mean is None):
weights = tf.get_variable('weights',
weights = vs.get_variable('weights',
[X.get_shape()[1], y.get_shape()[-1]])
bias = tf.get_variable('bias',
bias = vs.get_variable('bias',
[y.get_shape()[-1]])
else:
weights = tf.get_variable('weights',
weights = vs.get_variable('weights',
[X.get_shape()[1], y.get_shape()[-1]],
initializer=tf.random_normal_initializer(
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev))
bias = tf.get_variable('bias',
bias = vs.get_variable('bias',
[y.get_shape()[-1]],
initializer=tf.random_normal_initializer(
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev))
tf.histogram_summary('logistic_regression.weights', weights)
tf.histogram_summary('logistic_regression.bias', bias)
logging_ops.histogram_summary('logistic_regression.weights', weights)
logging_ops.histogram_summary('logistic_regression.bias', bias)
# If no class weight provided, try to retrieve one from pre-defined
# tensor name in the graph.
if not class_weight:
try:
class_weight = tf.get_default_graph().get_tensor_by_name('class_weight:0')
class_weight = ops.get_default_graph().get_tensor_by_name('class_weight:0')
except KeyError:
pass
@ -200,12 +207,12 @@ def _reverse_seq(input_seq, lengths):
input_.set_shape(input_.get_shape().with_rank(2))
# Join into (time, batch_size, depth)
s_joined = tf.pack(input_seq)
s_joined = array_ops_.pack(input_seq)
# Reverse along dimension 0
s_reversed = tf.reverse_sequence(s_joined, lengths, 0, 1)
s_reversed = array_ops_.reverse_sequence(s_joined, lengths, 0, 1)
# Split again into list
result = tf.unpack(s_reversed)
result = array_ops_.unpack(s_reversed)
return result
@ -245,9 +252,9 @@ def bidirectional_rnn(cell_fw, cell_bw, inputs,
ValueError: If inputs is None or an empty list.
"""
if not isinstance(cell_fw, tf.nn.rnn_cell.RNNCell):
if not isinstance(cell_fw, nn.rnn_cell.RNNCell):
raise TypeError("cell_fw must be an instance of RNNCell")
if not isinstance(cell_bw, tf.nn.rnn_cell.RNNCell):
if not isinstance(cell_bw, nn.rnn_cell.RNNCell):
raise TypeError("cell_bw must be an instance of RNNCell")
if not isinstance(inputs, list):
raise TypeError("inputs must be a list")
@ -256,20 +263,20 @@ def bidirectional_rnn(cell_fw, cell_bw, inputs,
name = scope or "BiRNN"
# Forward direction
with tf.variable_scope(name + "_FW"):
output_fw, state_fw = tf.nn.rnn(cell_fw, inputs, initial_state_fw, dtype,
sequence_length)
with vs.variable_scope(name + "_FW"):
output_fw, state_fw = nn.rnn(cell_fw, inputs, initial_state_fw, dtype,
sequence_length)
# Backward direction
with tf.variable_scope(name + "_BW"):
tmp, state_bw = tf.nn.rnn(cell_bw, _reverse_seq(inputs, sequence_length),
initial_state_bw, dtype, sequence_length)
with vs.variable_scope(name + "_BW"):
tmp, state_bw = nn.rnn(cell_bw, _reverse_seq(inputs, sequence_length),
initial_state_bw, dtype, sequence_length)
output_bw = _reverse_seq(tmp, sequence_length)
# Concat each of the forward/backward outputs
outputs = [tf.concat(1, [fw, bw])
outputs = [array_ops_.concat(1, [fw, bw])
for fw, bw in zip(output_fw, output_bw)]
return outputs, tf.concat(1, [state_fw, state_bw])
return outputs, array_ops_.concat(1, [state_fw, state_bw])
# End of Tensorflow 0.7
@ -305,27 +312,27 @@ def get_rnn_model(rnn_size, cell_type, num_layers, input_op_fn,
"""RNN estimator with target predictor function on top."""
X = input_op_fn(X)
if cell_type == 'rnn':
cell_fn = tf.nn.rnn_cell.BasicRNNCell
cell_fn = nn.rnn_cell.BasicRNNCell
elif cell_type == 'gru':
cell_fn = tf.nn.rnn_cell.GRUCell
cell_fn = nn.rnn_cell.GRUCell
elif cell_type == 'lstm':
cell_fn = tf.nn.rnn_cell.BasicLSTMCell
cell_fn = nn.rnn_cell.BasicLSTMCell
else:
raise ValueError("cell_type {} is not supported. ".format(cell_type))
if bidirectional:
# forward direction cell
rnn_fw_cell = tf.nn.rnn_cell.MultiRNNCell([cell_fn(rnn_size)] * num_layers)
rnn_fw_cell = nn.rnn_cell.MultiRNNCell([cell_fn(rnn_size)] * num_layers)
# backward direction cell
rnn_bw_cell = tf.nn.rnn_cell.MultiRNNCell([cell_fn(rnn_size)] * num_layers)
rnn_bw_cell = nn.rnn_cell.MultiRNNCell([cell_fn(rnn_size)] * num_layers)
# pylint: disable=unexpected-keyword-arg, no-value-for-parameter
_, encoding = bidirectional_rnn(rnn_fw_cell, rnn_bw_cell, X,
dtype=tf.float32,
dtype=dtypes.float32,
sequence_length=sequence_length,
initial_state_fw=initial_state,
initial_state_bw=initial_state)
else:
cell = tf.nn.rnn_cell.MultiRNNCell([cell_fn(rnn_size)] * num_layers)
_, encoding = tf.nn.rnn(cell, X, dtype=tf.float32,
cell = nn.rnn_cell.MultiRNNCell([cell_fn(rnn_size)] * num_layers)
_, encoding = nn.rnn(cell, X, dtype=dtypes.float32,
sequence_length=sequence_length,
initial_state=initial_state)
return target_predictor_fn(encoding, y)

5
tensorflow/contrib/skflow/python/skflow/monitors.py → tensorflow/contrib/learn/python/learn/monitors.py
View File

@ -20,7 +20,7 @@ from __future__ import print_function
import sys
import numpy as np
from tensorflow.contrib.skflow.python.skflow.io.data_feeder import setup_train_data_feeder
from tensorflow.contrib.learn.python.learn.io.data_feeder import setup_train_data_feeder
# pylint: disable=too-many-instance-attributes
@ -162,7 +162,8 @@ class ValidationMonitor(BaseMonitor):
def create_val_feed_dict(self, inp, out):
"""Set tensorflow placeholders and create validation data feed"""
self.val_dict = self.val_feeder.get_feed_dict_fn(inp, out)()
self.val_feeder.set_placeholders(inp, out)
self.val_dict = self.val_feeder.get_feed_dict_fn()()
def _set_last_loss_seen(self):
"""Sets self.last_loss_seen to most recent validation loss

16
tensorflow/contrib/skflow/python/skflow/ops/__init__.py → tensorflow/contrib/learn/python/learn/ops/__init__.py
View File

@ -16,11 +16,11 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.skflow.python.skflow.ops.array_ops import *
from tensorflow.contrib.skflow.python.skflow.ops.conv_ops import *
from tensorflow.contrib.skflow.python.skflow.ops.dnn_ops import *
from tensorflow.contrib.skflow.python.skflow.ops.dropout_ops import *
from tensorflow.contrib.skflow.python.skflow.ops.embeddings_ops import *
from tensorflow.contrib.skflow.python.skflow.ops.losses_ops import *
from tensorflow.contrib.skflow.python.skflow.ops.seq2seq_ops import *
from tensorflow.contrib.skflow.python.skflow.ops.batch_norm_ops import *
from tensorflow.contrib.learn.python.learn.ops.array_ops import *
from tensorflow.contrib.learn.python.learn.ops.conv_ops import *
from tensorflow.contrib.learn.python.learn.ops.dnn_ops import *
from tensorflow.contrib.learn.python.learn.ops.dropout_ops import *
from tensorflow.contrib.learn.python.learn.ops.embeddings_ops import *
from tensorflow.contrib.learn.python.learn.ops.losses_ops import *
from tensorflow.contrib.learn.python.learn.ops.seq2seq_ops import *
from tensorflow.contrib.learn.python.learn.ops.batch_norm_ops import *

22
tensorflow/contrib/skflow/python/skflow/ops/array_ops.py → tensorflow/contrib/learn/python/learn/ops/array_ops.py
View File

@ -16,7 +16,9 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops as array_ops_
from tensorflow.python.ops import math_ops
def split_squeeze(dim, num_split, tensor_in):
@ -30,7 +32,8 @@ def split_squeeze(dim, num_split, tensor_in):
Returns:
List of tensors [N1, N2, .. Ndim-1, Ndim+1, .. Nx].
"""
return [tf.squeeze(t, squeeze_dims=[dim]) for t in tf.split(dim, num_split, tensor_in)]
return [array_ops_.squeeze(t, squeeze_dims=[dim])
for t in array_ops_.split(dim, num_split, tensor_in)]
def expand_concat(dim, inputs):
@ -43,7 +46,7 @@ def expand_concat(dim, inputs):
Returns:
A tensor of shape [N1, .. Ndim, ... Nx]
"""
return tf.concat(dim, [tf.expand_dims(t, dim) for t in inputs])
return array_ops_.concat(dim, [array_ops_.expand_dims(t, dim) for t in inputs])
def one_hot_matrix(tensor_in, num_classes, on_value=1.0, off_value=0.0):
@ -61,14 +64,5 @@ def one_hot_matrix(tensor_in, num_classes, on_value=1.0, off_value=0.0):
Tensor of shape [N1, N2, num_classes] with 1.0 for each id in original
tensor.
"""
tensor_in = tf.convert_to_tensor(tensor_in)
sparse_values = tf.to_int64(tf.reshape(tensor_in, [-1, 1]))
size = tf.shape(sparse_values)[0]
dims = tf.shape(tensor_in)
indices = tf.to_int64(tf.reshape(tf.range(0, size), [-1, 1]))
indices_values = tf.concat(1, [indices, sparse_values])
outshape = tf.to_int64(expand_concat(0, [size, num_classes]))
one_hot_vector = tf.sparse_to_dense(indices_values, outshape, on_value, off_value)
ret = tf.reshape(one_hot_vector, tf.concat(0, [dims, [num_classes]]))
ret.set_shape(tensor_in.get_shape().concatenate(num_classes))
return ret
return array_ops_.one_hot(math_ops.cast(tensor_in, dtypes.int64), num_classes, on_value, off_value)

36
tensorflow/contrib/skflow/python/skflow/ops/batch_norm_ops.py → tensorflow/contrib/learn/python/learn/ops/batch_norm_ops.py
View File

@ -16,10 +16,16 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops as array_ops_
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.training import moving_averages
def batch_normalize(tensor_in, epsilon=1e-5, convnet=True, decay=0.9,
def batch_normalize(tensor_in, epsilon=1e-5, convnet=False, decay=0.9,
scale_after_normalization=True):
"""Batch Normalization
@ -34,25 +40,25 @@ def batch_normalize(tensor_in, epsilon=1e-5, convnet=True, decay=0.9,
"""
shape = tensor_in.get_shape().as_list()
with tf.variable_scope("batch_norm"):
gamma = tf.get_variable("gamma", [shape[-1]],
initializer=tf.random_normal_initializer(1., 0.02))
beta = tf.get_variable("beta", [shape[-1]],
initializer=tf.constant_initializer(0.))
ema = tf.train.ExponentialMovingAverage(decay=decay)
with vs.variable_scope("batch_norm"):
gamma = vs.get_variable("gamma", [shape[-1]],
initializer=init_ops.random_normal_initializer(1., 0.02))
beta = vs.get_variable("beta", [shape[-1]],
initializer=init_ops.constant_initializer(0.))
ema = moving_averages.ExponentialMovingAverage(decay=decay)
if convnet:
assign_mean, assign_var = tf.nn.moments(tensor_in, [0, 1, 2])
assign_mean, assign_var = nn.moments(tensor_in, [0, 1, 2])
else:
assign_mean, assign_var = tf.nn.moments(tensor_in, [0])
assign_mean, assign_var = nn.moments(tensor_in, [0])
ema_assign_op = ema.apply([assign_mean, assign_var])
ema_mean, ema_var = ema.average(assign_mean), ema.average(assign_var)
def update_mean_var():
"""Internal function that updates mean and variance during training"""
with tf.control_dependencies([ema_assign_op]):
return tf.identity(assign_mean), tf.identity(assign_var)
is_training = tf.squeeze(tf.get_collection("IS_TRAINING"))
mean, variance = tf.cond(
with ops.control_dependencies([ema_assign_op]):
return array_ops_.identity(assign_mean), array_ops_.identity(assign_var)
is_training = array_ops_.squeeze(ops.get_collection("IS_TRAINING"))
mean, variance = control_flow_ops.cond(
is_training, update_mean_var, lambda: (ema_mean, ema_var))
return tf.nn.batch_norm_with_global_normalization(
return nn.batch_norm_with_global_normalization(
tensor_in, mean, variance, beta, gamma, epsilon,
scale_after_normalization=scale_after_normalization)

18
tensorflow/contrib/skflow/python/skflow/ops/conv_ops.py → tensorflow/contrib/learn/python/learn/ops/conv_ops.py
View File

@ -16,8 +16,10 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from .batch_norm_ops import batch_normalize
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import nn
from tensorflow.python.ops import variable_scope as vs
from tensorflow.contrib.learn.python.learn.ops.batch_norm_ops import batch_normalize
def conv2d(tensor_in, n_filters, filter_shape, strides=None, padding='SAME',
@ -45,19 +47,19 @@ def conv2d(tensor_in, n_filters, filter_shape, strides=None, padding='SAME',
Returns:
A Tensor with resulting convolution.
"""
with tf.variable_scope('convolution'):
with vs.variable_scope('convolution'):
if strides is None:
strides = [1, 1, 1, 1]
input_shape = tensor_in.get_shape()
filter_shape = list(filter_shape) + [input_shape[3], n_filters]
filters = tf.get_variable('filters', filter_shape, tf.float32)
output = tf.nn.conv2d(tensor_in, filters, strides, padding)
filters = vs.get_variable('filters', filter_shape, dtypes.float32)
output = nn.conv2d(tensor_in, filters, strides, padding)
if bias:
bias_var = tf.get_variable('bias', [1, 1, 1, n_filters],
tf.float32)
bias_var = vs.get_variable('bias', [1, 1, 1, n_filters],
dtypes.float32)
output = output + bias_var
if batch_norm:
output = batch_normalize(output)
output = batch_normalize(output, convnet=True)
if activation:
output = activation(output)
return output

15
tensorflow/contrib/skflow/python/skflow/ops/dnn_ops.py → tensorflow/contrib/learn/python/learn/ops/dnn_ops.py
View File

@ -16,12 +16,13 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib.skflow.python.skflow.ops import dropout_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import rnn_cell
from tensorflow.python.ops import variable_scope as vs
from tensorflow.contrib.learn.python.learn.ops import dropout_ops
def dnn(tensor_in, hidden_units, activation=tf.nn.relu, dropout=None):
def dnn(tensor_in, hidden_units, activation=nn.relu, dropout=None):
"""Creates fully connected deep neural network subgraph.
Args:
@ -34,10 +35,10 @@ def dnn(tensor_in, hidden_units, activation=tf.nn.relu, dropout=None):
Returns:
A tensor which would be a deep neural network.
"""
with tf.variable_scope('dnn'):
with vs.variable_scope('dnn'):
for i, n_units in enumerate(hidden_units):
with tf.variable_scope('layer%d' % i):
tensor_in = tf.nn.rnn_cell.linear(tensor_in, n_units, True)
with vs.variable_scope('layer%d' % i):
tensor_in = rnn_cell.linear(tensor_in, n_units, True)
if activation is not None:
tensor_in = activation(tensor_in)
if dropout is not None:

15
tensorflow/contrib/skflow/python/skflow/ops/dropout_ops.py → tensorflow/contrib/learn/python/learn/ops/dropout_ops.py
View File

@ -16,7 +16,10 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import variable_scope as vs
# Key to collect dropout probabilities.
@ -36,11 +39,11 @@ def dropout(tensor_in, prob, name=None):
Raises:
ValueError: If `keep_prob` is not in `(0, 1]`.
"""
with tf.op_scope([tensor_in], name, "dropout") as name:
with ops.op_scope([tensor_in], name, "dropout") as name:
if isinstance(prob, float):
prob = tf.get_variable("prob", [],
initializer=tf.constant_initializer(prob),
prob = vs.get_variable("prob", [],
initializer=init_ops.constant_initializer(prob),
trainable=False)
tf.add_to_collection(DROPOUTS, prob)
return tf.nn.dropout(tensor_in, prob)
ops.add_to_collection(DROPOUTS, prob)
return nn.dropout(tensor_in, prob)

26
tensorflow/contrib/skflow/python/skflow/ops/embeddings_ops.py → tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py
View File

@ -20,7 +20,11 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops as array_ops_
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import variable_scope as vs
def embedding_lookup(params, ids, name="embedding_lookup"):
@ -43,14 +47,14 @@ def embedding_lookup(params, ids, name="embedding_lookup"):
Raises:
ValueError: if some parameters are invalid.
"""
with tf.op_scope([params, ids], name, "embedding_lookup"):
params = tf.convert_to_tensor(params)
ids = tf.convert_to_tensor(ids)
shape = tf.shape(ids)
ids_flat = tf.reshape(ids, tf.reduce_prod(shape, keep_dims=True))
embeds_flat = tf.nn.embedding_lookup(params, ids_flat, name)
embed_shape = tf.concat(0, [shape, [-1]])
embeds = tf.reshape(embeds_flat, embed_shape)
with ops.op_scope([params, ids], name, "embedding_lookup"):
params = ops.convert_to_tensor(params)
ids = ops.convert_to_tensor(ids)
shape = array_ops_.shape(ids)
ids_flat = array_ops_.reshape(ids, math_ops.reduce_prod(shape, keep_dims=True))
embeds_flat = nn.embedding_lookup(params, ids_flat, name)
embed_shape = array_ops_.concat(0, [shape, [-1]])
embeds = array_ops_.reshape(embeds_flat, embed_shape)
embeds.set_shape(ids.get_shape().concatenate(params.get_shape()[1:]))
return embeds
@ -72,7 +76,7 @@ def categorical_variable(tensor_in, n_classes, embedding_size, name):
Calling categorical_variable([1, 2], 5, 10, "my_cat"), will return 2 x 10
tensor, where each row is representation of the class.
"""
with tf.variable_scope(name):
embeddings = tf.get_variable(
with vs.variable_scope(name):
embeddings = vs.get_variable(
name + "_embeddings", [n_classes, embedding_size])
return embedding_lookup(embeddings, tensor_in)

30
tensorflow/contrib/skflow/python/skflow/ops/losses_ops.py → tensorflow/contrib/learn/python/learn/ops/losses_ops.py
View File

@ -16,18 +16,20 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops as array_ops_
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.contrib.losses.python.losses import loss_ops
def mean_squared_error_regressor(tensor_in, labels, weights, biases, name=None):
"""Returns prediction and loss for mean squared error regression."""
with tf.op_scope([tensor_in, labels], name, "mean_squared_error_regressor"):
predictions = tf.nn.xw_plus_b(tensor_in, weights, biases)
with ops.op_scope([tensor_in, labels], name, "mean_squared_error_regressor"):
predictions = nn.xw_plus_b(tensor_in, weights, biases)
if len(labels.get_shape()) == 1:
labels = tf.reshape(labels, [-1, 1])
diff = labels - predictions
loss = tf.reduce_mean(tf.mul(diff, diff))
return predictions, loss
labels = array_ops_.reshape(labels, [-1, 1])
return predictions, loss_ops.sum_of_squares(predictions, labels)
def softmax_classifier(tensor_in, labels, weights, biases, class_weight=None, name=None):
@ -45,13 +47,9 @@ def softmax_classifier(tensor_in, labels, weights, biases, class_weight=None, na
Returns:
Prediction and loss tensors.
"""
with tf.op_scope([tensor_in, labels], name, "softmax_classifier"):
logits = tf.nn.xw_plus_b(tensor_in, weights, biases)
with ops.op_scope([tensor_in, labels], name, "softmax_classifier"):
logits = nn.xw_plus_b(tensor_in, weights, biases)
if class_weight is not None:
logits = tf.mul(logits, class_weight)
xent = tf.nn.softmax_cross_entropy_with_logits(logits,
labels,
name="xent_raw")
loss = tf.reduce_mean(xent, name="xent")
predictions = tf.nn.softmax(logits, name=name)
return predictions, loss
logits = math_ops.mul(logits, class_weight)
return nn.softmax(logits), loss_ops.softmax_cross_entropy(logits, labels)

42
tensorflow/contrib/skflow/python/skflow/ops/seq2seq_ops.py → tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py
View File

@ -16,9 +16,14 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops as array_ops_
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import variable_scope as vs
from . import array_ops
from tensorflow.contrib.learn.python.learn.ops import array_ops
def sequence_classifier(decoding, labels, sampling_decoding=None, name=None):
@ -34,18 +39,18 @@ def sequence_classifier(decoding, labels, sampling_decoding=None, name=None):
Returns:
Predictions and losses tensors.
"""
with tf.op_scope([decoding, labels], name, "sequence_classifier"):
with ops.op_scope([decoding, labels], name, "sequence_classifier"):
predictions, xent_list = [], []
for i, pred in enumerate(decoding):
xent_list.append(
tf.nn.softmax_cross_entropy_with_logits(
nn.softmax_cross_entropy_with_logits(
pred, labels[i], name="sequence_loss/xent_raw{0}".format(i)))
if sampling_decoding:
predictions.append(tf.nn.softmax(sampling_decoding[i]))
predictions.append(nn.softmax(sampling_decoding[i]))
else:
predictions.append(tf.nn.softmax(pred))
xent = tf.add_n(xent_list, name="sequence_loss/xent")
loss = tf.reduce_sum(xent, name="sequence_loss")
predictions.append(nn.softmax(pred))
xent = math_ops.add_n(xent_list, name="sequence_loss/xent")
loss = math_ops.reduce_sum(xent, name="sequence_loss")
return array_ops.expand_concat(1, predictions), loss
@ -65,13 +70,13 @@ def seq2seq_inputs(X, y, input_length, output_length, sentinel=None, name=None):
Returns:
Encoder input from X, and decoder inputs and outputs from y.
"""
with tf.op_scope([X, y], name, "seq2seq_inputs"):
with ops.op_scope([X, y], name, "seq2seq_inputs"):
in_X = array_ops.split_squeeze(1, input_length, X)
y = array_ops.split_squeeze(1, output_length, y)
if not sentinel:
# Set to zeros of shape of y[0], using X for batch size.
sentinel_shape = tf.pack([tf.shape(X)[0], y[0].get_shape()[1]])
sentinel = tf.zeros(sentinel_shape)
sentinel_shape = array_ops_.pack([array_ops_.shape(X)[0], y[0].get_shape()[1]])
sentinel = array_ops_.zeros(sentinel_shape)
sentinel.set_shape(y[0].get_shape())
in_y = [sentinel] + y
out_y = y + [sentinel]
@ -91,17 +96,17 @@ def rnn_decoder(decoder_inputs, initial_state, cell, scope=None):
Returns:
List of tensors for outputs and states for training and sampling sub-graphs.
"""
with tf.variable_scope(scope or "dnn_decoder"):
with vs.variable_scope(scope or "dnn_decoder"):
states, sampling_states = [initial_state], [initial_state]
outputs, sampling_outputs = [], []
with tf.op_scope([decoder_inputs, initial_state], "training"):
with ops.op_scope([decoder_inputs, initial_state], "training"):
for i, inp in enumerate(decoder_inputs):
if i > 0:
tf.get_variable_scope().reuse_variables()
vs.get_variable_scope().reuse_variables()
output, new_state = cell(inp, states[-1])
outputs.append(output)
states.append(new_state)
with tf.op_scope([initial_state], "sampling"):
with ops.op_scope([initial_state], "sampling"):
for i, _ in enumerate(decoder_inputs):
if i == 0:
sampling_outputs.append(outputs[i])
@ -115,7 +120,7 @@ def rnn_decoder(decoder_inputs, initial_state, cell, scope=None):
def rnn_seq2seq(encoder_inputs, decoder_inputs, encoder_cell, decoder_cell=None,
dtype=tf.float32, scope=None):
dtype=dtypes.float32, scope=None):
"""RNN Sequence to Sequence model.
Args:
@ -129,6 +134,7 @@ def rnn_seq2seq(encoder_inputs, decoder_inputs, encoder_cell, decoder_cell=None,
Returns:
List of tensors for outputs and states for trianing and sampling sub-graphs.
"""
with tf.variable_scope(scope or "rnn_seq2seq"):
_, last_enc_state = tf.nn.rnn(encoder_cell, encoder_inputs, dtype=dtype)
with vs.variable_scope(scope or "rnn_seq2seq"):
_, last_enc_state = nn.rnn(encoder_cell, encoder_inputs, dtype=dtype)
return rnn_decoder(decoder_inputs, last_enc_state, decoder_cell or encoder_cell)

0
tensorflow/contrib/skflow/python/skflow/ops/tests/__init__.py → tensorflow/contrib/learn/python/learn/ops/tests/__init__.py
View File

2
tensorflow/contrib/skflow/python/skflow/ops/tests/test_dropout_ops.py → tensorflow/contrib/learn/python/learn/ops/tests/test_dropout_ops.py
View File

@ -18,7 +18,7 @@ from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.contrib.skflow.python.skflow import ops
from tensorflow.contrib.learn.python.learn import ops
class DropoutTest(tf.test.TestCase):

2
tensorflow/contrib/skflow/python/skflow/ops/tests/test_ops.py → tensorflow/contrib/learn/python/learn/ops/tests/test_ops.py
View File

@ -18,7 +18,7 @@ from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.contrib.skflow.python.skflow import ops
from tensorflow.contrib.learn.python.learn import ops
class OpsTest(tf.test.TestCase):

2
tensorflow/contrib/skflow/python/skflow/ops/tests/test_seq2seq_ops.py → tensorflow/contrib/learn/python/learn/ops/tests/test_seq2seq_ops.py
View File

@ -18,7 +18,7 @@ from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.contrib.skflow.python.skflow import ops
from tensorflow.contrib.learn.python.learn import ops
class Seq2SeqOpsTest(tf.test.TestCase):

4
tensorflow/contrib/skflow/python/skflow/preprocessing/__init__.py → tensorflow/contrib/learn/python/learn/preprocessing/__init__.py
View File

@ -16,5 +16,5 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.skflow.python.skflow.preprocessing.text import *
from tensorflow.contrib.skflow.python.skflow.preprocessing.categorical import *
from tensorflow.contrib.learn.python.learn.preprocessing.text import *
from tensorflow.contrib.learn.python.learn.preprocessing.categorical import *

0
tensorflow/contrib/skflow/python/skflow/preprocessing/categorical.py → tensorflow/contrib/learn/python/learn/preprocessing/categorical.py
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0
tensorflow/contrib/skflow/python/skflow/preprocessing/categorical_vocabulary.py → tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py
View File

0
tensorflow/contrib/skflow/python/skflow/preprocessing/tests/__init__.py → tensorflow/contrib/learn/python/learn/preprocessing/tests/__init__.py
View File

4
tensorflow/contrib/skflow/python/skflow/preprocessing/tests/test_categorical.py → tensorflow/contrib/learn/python/learn/preprocessing/tests/test_categorical.py
View File

@ -20,8 +20,8 @@ from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.contrib.skflow.python.skflow.preprocessing import categorical
from tensorflow.contrib.skflow.python.skflow.io import *
from tensorflow.contrib.learn.python.learn.preprocessing import categorical
from tensorflow.contrib.learn.python.learn.io import *
class CategoricalTest(tf.test.TestCase):

2
tensorflow/contrib/skflow/python/skflow/preprocessing/tests/test_categorical_vocabulary.py → tensorflow/contrib/learn/python/learn/preprocessing/tests/test_categorical_vocabulary.py
View File

@ -19,7 +19,7 @@ from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib.skflow.python.skflow.preprocessing import categorical_vocabulary
from tensorflow.contrib.learn.python.learn.preprocessing import categorical_vocabulary
class CategoricalVocabularyTest(tf.test.TestCase):

4
tensorflow/contrib/skflow/python/skflow/preprocessing/tests/test_text.py → tensorflow/contrib/learn/python/learn/preprocessing/tests/test_text.py
View File

@ -20,8 +20,8 @@ from __future__ import unicode_literals
import tensorflow as tf
from tensorflow.contrib.skflow.python.skflow.preprocessing import text
from tensorflow.contrib.skflow.python.skflow.preprocessing import CategoricalVocabulary
from tensorflow.contrib.learn.python.learn.preprocessing import text
from tensorflow.contrib.learn.python.learn.preprocessing import CategoricalVocabulary
class TextTest(tf.test.TestCase):

6
tensorflow/contrib/skflow/python/skflow/preprocessing/text.py → tensorflow/contrib/learn/python/learn/preprocessing/text.py
View File

@ -26,6 +26,8 @@ except ImportError:
import numpy as np
from tensorflow.python.platform.default import _gfile as gfile
from .categorical_vocabulary import CategoricalVocabulary
TOKENIZER_RE = re.compile(
@ -207,7 +209,7 @@ class VocabularyProcessor(object):
Args:
filename: Path to output file.
"""
with open(filename, 'wb') as f:
with gfile.Open(filename, 'wb') as f:
f.write(pickle.dumps(self))
@classmethod
@ -220,6 +222,6 @@ class VocabularyProcessor(object):
Returns:
VocabularyProcessor object.
"""
with open(filename, 'rb') as f:
with gfile.Open(filename, 'rb') as f:
return pickle.loads(f.read())

0
tensorflow/contrib/skflow/python/skflow/tests/__init__.py → tensorflow/contrib/learn/python/learn/tests/__init__.py
View File

49
tensorflow/contrib/skflow/python/skflow/tests/test_base.py → tensorflow/contrib/learn/python/learn/tests/test_base.py
View File

@ -17,14 +17,15 @@ from __future__ import print_function
import random
from sklearn import datasets
from sklearn.metrics import accuracy_score, mean_squared_error, log_loss
import numpy as np
import tensorflow as tf
from tensorflow.contrib.skflow.python import skflow
from tensorflow.contrib.skflow.python.skflow.estimators import base
from tensorflow.contrib.learn.python import learn
from tensorflow.contrib.learn.python.learn import datasets
from tensorflow.contrib.learn.python.learn.estimators import base
from tensorflow.contrib.learn.python.learn.estimators._sklearn import accuracy_score
from tensorflow.contrib.learn.python.learn.estimators._sklearn import log_loss
from tensorflow.contrib.learn.python.learn.estimators._sklearn import mean_squared_error
class BaseTest(tf.test.TestCase):
@ -33,21 +34,21 @@ class BaseTest(tf.test.TestCase):
random.seed(42)
X = np.random.rand(1000)
y = 2 * X + 3
regressor = skflow.TensorFlowLinearRegressor()
regressor = learn.TensorFlowLinearRegressor()
regressor.fit(X, y)
score = mean_squared_error(regressor.predict(X), y)
self.assertLess(score, 0.5, "Failed with score = {0}".format(score))
score = mean_squared_error(y, regressor.predict(X))
self.assertLess(score, 1.0, "Failed with score = {0}".format(score))
def testIris(self):
iris = datasets.load_iris()
classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
classifier = learn.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(iris.data, [float(x) for x in iris.target])
score = accuracy_score(iris.target, classifier.predict(iris.data))
self.assertGreater(score, 0.7, "Failed with score = {0}".format(score))
def testIrisClassWeight(self):
iris = datasets.load_iris()
classifier = skflow.TensorFlowLinearClassifier(
classifier = learn.TensorFlowLinearClassifier(
n_classes=3, class_weight=[0.1, 0.8, 0.1])
classifier.fit(iris.data, iris.target)
score = accuracy_score(iris.target, classifier.predict(iris.data))
@ -55,15 +56,15 @@ class BaseTest(tf.test.TestCase):
def testIrisSummaries(self):
iris = datasets.load_iris()
classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(iris.data, iris.target, logdir='/tmp/skflow_tests/')
classifier = learn.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(iris.data, iris.target, logdir='/tmp/learn_tests/')
score = accuracy_score(iris.target, classifier.predict(iris.data))
self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
def testIrisContinueTraining(self):
iris = datasets.load_iris()
classifier = skflow.TensorFlowLinearClassifier(n_classes=3,
classifier = learn.TensorFlowLinearClassifier(n_classes=3,
learning_rate=0.01, continue_training=True, steps=250)
classifier.fit(iris.data, iris.target)
score1 = accuracy_score(iris.target, classifier.predict(iris.data))
@ -89,7 +90,7 @@ class BaseTest(tf.test.TestCase):
for y in iris.target:
yield y
classifier = skflow.TensorFlowLinearClassifier(n_classes=3, steps=100)
classifier = learn.TensorFlowLinearClassifier(n_classes=3, steps=100)
classifier.fit(iris_data(), iris_target())
score1 = accuracy_score(iris.target, classifier.predict(iris.data))
score2 = accuracy_score(iris.target, classifier.predict(iris_predict_data()))
@ -99,17 +100,19 @@ class BaseTest(tf.test.TestCase):
"data.".format(score2, score1))
def testIris_proba(self):
random.seed(42)
iris = datasets.load_iris()
classifier = skflow.TensorFlowClassifier(n_classes=3, steps=250)
classifier.fit(iris.data, iris.target)
score = log_loss(iris.target, classifier.predict_proba(iris.data))
self.assertLess(score, 0.8, "Failed with score = {0}".format(score))
# If sklearn available.
if log_loss:
random.seed(42)
iris = datasets.load_iris()
classifier = learn.TensorFlowClassifier(n_classes=3, steps=250)
classifier.fit(iris.data, iris.target)
score = log_loss(iris.target, classifier.predict_proba(iris.data))
self.assertLess(score, 0.8, "Failed with score = {0}".format(score))
def testBoston(self):
random.seed(42)
boston = datasets.load_boston()
regressor = skflow.TensorFlowLinearRegressor(
regressor = learn.TensorFlowLinearRegressor(
batch_size=boston.data.shape[0],
steps=500,
learning_rate=0.001)
@ -119,7 +122,7 @@ class BaseTest(tf.test.TestCase):
self.assertLess(score, 150, "Failed with score = {0}".format(score))
def testUnfitted(self):
estimator = skflow.TensorFlowEstimator(model_fn=None, n_classes=1)
estimator = learn.TensorFlowEstimator(model_fn=None, n_classes=1)
with self.assertRaises(base.NotFittedError):
estimator.predict([1, 2, 3])
with self.assertRaises(base.NotFittedError):

12
tensorflow/contrib/skflow/python/skflow/tests/test_custom_decay.py → tensorflow/contrib/learn/python/learn/tests/test_custom_decay.py
View File

@ -19,10 +19,11 @@ import tensorflow as tf
import random
from sklearn import datasets, metrics
from sklearn.cross_validation import train_test_split
from tensorflow.contrib.learn.python import learn
from tensorflow.contrib.learn.python.learn import datasets
from tensorflow.contrib.learn.python.learn.estimators._sklearn import accuracy_score
from tensorflow.contrib.learn.python.learn.estimators._sklearn import train_test_split
from tensorflow.contrib.skflow.python import skflow
class CustomDecayTest(tf.test.TestCase):
@ -34,16 +35,17 @@ class CustomDecayTest(tf.test.TestCase):
iris.target,
test_size=0.2,
random_state=42)
# setup exponential decay function
def exp_decay(global_step):
return tf.train.exponential_decay(
learning_rate=0.1, global_step=global_step,
decay_steps=100, decay_rate=0.001)
classifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
n_classes=3, steps=800,
learning_rate=exp_decay)
classifier.fit(X_train, y_train)
score = metrics.accuracy_score(y_test, classifier.predict(X_test))
score = accuracy_score(y_test, classifier.predict(X_test))
self.assertGreater(score, 0.7, "Failed with score = {0}".format(score))

62
tensorflow/contrib/skflow/python/skflow/tests/test_data_feeder.py → tensorflow/contrib/learn/python/learn/tests/test_data_feeder.py
View File

@ -21,50 +21,50 @@ import six
import tensorflow as tf
from tensorflow.contrib.skflow.python.skflow.io import *
class MockPlaceholder(object):
def __init__(self, name):
self.name = name
from tensorflow.contrib.learn.python.learn.io import *
class DataFeederTest(tf.test.TestCase):
def test_unsupervised(self):
data = np.matrix([[1, 2], [2, 3], [3, 4]])
feeder = data_feeder.DataFeeder(data, None, n_classes=0, batch_size=1)
with self.test_session():
inp, out = feeder.input_builder()
feed_dict_fn = feeder.get_feed_dict_fn()
feed_dict = feed_dict_fn()
self.assertAllClose(feed_dict[inp.name], [[1, 2]])
def test_data_feeder_regression(self):
X = np.matrix([[1, 2], [3, 4]])
y = np.array([1, 2])
df = data_feeder.DataFeeder(X, y, n_classes=0, batch_size=3)
feed_dict_fn = df.get_feed_dict_fn(
MockPlaceholder(name='input'),
MockPlaceholder(name='output'))
inp, out = df.input_builder()
feed_dict_fn = df.get_feed_dict_fn()
feed_dict = feed_dict_fn()
self.assertAllClose(feed_dict['input'], [[3, 4], [1, 2]])
self.assertAllClose(feed_dict['output'], [2, 1])
self.assertAllClose(feed_dict[inp.name], [[3, 4], [1, 2]])
self.assertAllClose(feed_dict[out.name], [2, 1])
def test_data_feeder_multioutput_regression(self):
X = np.matrix([[1, 2], [3, 4]])
y = np.array([[1, 2], [3, 4]])
df = data_feeder.DataFeeder(X, y, n_classes=0, batch_size=2)
feed_dict_fn = df.get_feed_dict_fn(
MockPlaceholder(name='input'),
MockPlaceholder(name='output'))
inp, out = df.input_builder()
feed_dict_fn = df.get_feed_dict_fn()
feed_dict = feed_dict_fn()
self.assertAllClose(feed_dict['input'], [[3, 4], [1, 2]])
self.assertAllClose(feed_dict['output'], [[3, 4], [1, 2]])
self.assertAllClose(feed_dict[inp.name], [[3, 4], [1, 2]])
self.assertAllClose(feed_dict[out.name], [[3, 4], [1, 2]])
def test_data_feeder_multioutput_classification(self):
X = np.matrix([[1, 2], [3, 4]])
y = np.array([[0, 1, 2], [2, 3, 4]])
df = data_feeder.DataFeeder(X, y, n_classes=5, batch_size=2)
feed_dict_fn = df.get_feed_dict_fn(
MockPlaceholder(name='input'),
MockPlaceholder(name='output'))
inp, out = df.input_builder()
feed_dict_fn = df.get_feed_dict_fn()
feed_dict = feed_dict_fn()
self.assertAllClose(feed_dict['input'], [[3, 4], [1, 2]])
self.assertAllClose(feed_dict['output'], [[[0, 0, 1, 0, 0],
self.assertAllClose(feed_dict[inp.name], [[3, 4], [1, 2]])
self.assertAllClose(feed_dict[out.name], [[[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1]],
[[1, 0, 0, 0, 0],
@ -81,12 +81,11 @@ class DataFeederTest(tf.test.TestCase):
yield np.array([2])
df = data_feeder.StreamingDataFeeder(X_iter(), y_iter(), n_classes=0,
batch_size=2)
feed_dict_fn = df.get_feed_dict_fn(
MockPlaceholder(name='input'),
MockPlaceholder(name='output'))
inp, out = df.input_builder()
feed_dict_fn = df.get_feed_dict_fn()
feed_dict = feed_dict_fn()
self.assertAllClose(feed_dict['input'], [[1, 2], [3, 4]])
self.assertAllClose(feed_dict['output'], [1, 2])
self.assertAllClose(feed_dict[inp.name], [[1, 2], [3, 4]])
self.assertAllClose(feed_dict[out.name], [1, 2])
def test_dask_data_feeder(self):
if HAS_PANDAS and HAS_DASK:
@ -98,13 +97,12 @@ class DataFeederTest(tf.test.TestCase):
# X = extract_dask_data(X)
# y = extract_dask_labels(y)
df = data_feeder.DaskDataFeeder(X, y, n_classes=2, batch_size=2)
feed_dict_fn = df.get_feed_dict_fn(
MockPlaceholder(name='input'),
MockPlaceholder(name='output'))
inp, out = df.input_builder()
feed_dict_fn = df.get_feed_dict_fn()
feed_dict = feed_dict_fn()
self.assertAllClose(feed_dict['input'], [[ 0.40000001, 0.1],
self.assertAllClose(feed_dict[inp.name], [[ 0.40000001, 0.1],
[ 0.60000002, 0.2]])
self.assertAllClose(feed_dict['output'], [[ 0., 0., 1.],
self.assertAllClose(feed_dict[out.name], [[ 0., 0., 1.],
[ 0., 1., 0.]])

18
tensorflow/contrib/skflow/python/skflow/tests/test_early_stopping.py → tensorflow/contrib/learn/python/learn/tests/test_early_stopping.py
View File

@ -19,10 +19,10 @@ import tensorflow as tf
import random
from sklearn import datasets, metrics
from sklearn.cross_validation import train_test_split
from tensorflow.contrib.skflow.python import skflow
from tensorflow.contrib.learn.python import learn
from tensorflow.contrib.learn.python.learn import datasets
from tensorflow.contrib.learn.python.learn.estimators._sklearn import accuracy_score
from tensorflow.contrib.learn.python.learn.estimators._sklearn import train_test_split
class EarlyStoppingTest(tf.test.TestCase):
@ -37,20 +37,20 @@ class EarlyStoppingTest(tf.test.TestCase):
random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)
val_monitor = skflow.monitors.ValidationMonitor(X_val, y_val, n_classes=3)
val_monitor = learn.monitors.ValidationMonitor(X_val, y_val, n_classes=3)
# classifier without early stopping - overfitting
classifier1 = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
classifier1 = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
n_classes=3, steps=1000)
classifier1.fit(X_train, y_train)
score1 = metrics.accuracy_score(y_test, classifier1.predict(X_test))
score1 = accuracy_score(y_test, classifier1.predict(X_test))
# classifier with early stopping - improved accuracy on testing set
classifier2 = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
classifier2 = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
n_classes=3, steps=1000)
classifier2.fit(X_train, y_train, val_monitor)
score2 = metrics.accuracy_score(y_test, classifier2.predict(X_test))
score2 = accuracy_score(y_test, classifier2.predict(X_test))
# self.assertGreater(score2, score1, "No improvement using early stopping.")

14
tensorflow/contrib/skflow/python/skflow/tests/test_estimators.py → tensorflow/contrib/learn/python/learn/tests/test_estimators.py
View File

@ -19,10 +19,10 @@ import tensorflow as tf
import random
from sklearn import datasets, metrics
from sklearn.cross_validation import train_test_split
from tensorflow.contrib.skflow.python import skflow
from tensorflow.contrib.learn.python import learn
from tensorflow.contrib.learn.python.learn import datasets
from tensorflow.contrib.learn.python.learn.estimators._sklearn import accuracy_score
from tensorflow.contrib.learn.python.learn.estimators._sklearn import train_test_split
class CustomOptimizer(tf.test.TestCase):
@ -40,13 +40,13 @@ class CustomOptimizer(tf.test.TestCase):
return tf.train.exponential_decay(
learning_rate=0.1, global_step=global_step,
decay_steps=100, decay_rate=0.001)
custom_optimizer = lambda x: tf.train.MomentumOptimizer(x, 0.9)
classifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
custom_optimizer = lambda learning_rate: tf.train.MomentumOptimizer(learning_rate, 0.9)
classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
n_classes=3, steps=800,
learning_rate=exp_decay,
optimizer=custom_optimizer)
classifier.fit(X_train, y_train)
score = metrics.accuracy_score(y_test, classifier.predict(X_test))
score = accuracy_score(y_test, classifier.predict(X_test))
self.assertGreater(score, 0.7, "Failed with score = {0}".format(score))

33
tensorflow/contrib/skflow/python/skflow/tests/test_grid_search.py → tensorflow/contrib/learn/python/learn/tests/test_grid_search.py
View File

@ -17,28 +17,33 @@ from __future__ import print_function
import random
from sklearn import datasets
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import accuracy_score, mean_squared_error
try:
from sklearn import datasets
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import accuracy_score, mean_squared_error
HAS_SKLEARN = True
except ImportError:
HAS_SKLEARN = False
import tensorflow as tf
from tensorflow.contrib.skflow.python import skflow
from tensorflow.contrib.learn.python import learn
class GridSearchTest(tf.test.TestCase):
def testIrisDNN(self):
random.seed(42)
iris = datasets.load_iris()
classifier = skflow.TensorFlowDNNClassifier(
hidden_units=[10, 20, 10], n_classes=3, steps=50)
grid_search = GridSearchCV(classifier,
{'hidden_units': [[5, 5], [10, 10]],
'learning_rate': [0.1, 0.01]})
grid_search.fit(iris.data, iris.target)
score = accuracy_score(iris.target, grid_search.predict(iris.data))
self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
if HAS_SKLEARN:
random.seed(42)
iris = datasets.load_iris()
classifier = learn.TensorFlowDNNClassifier(
hidden_units=[10, 20, 10], n_classes=3, steps=50)
grid_search = GridSearchCV(classifier,
{'hidden_units': [[5, 5], [10, 10]],
'learning_rate': [0.1, 0.01]})
grid_search.fit(iris.data, iris.target)
score = accuracy_score(iris.target, grid_search.predict(iris.data))
self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
if __name__ == "__main__":

21
tensorflow/contrib/skflow/python/skflow/tests/test_io.py → tensorflow/contrib/learn/python/learn/tests/test_io.py
View File

@ -17,13 +17,12 @@ from __future__ import print_function
import random
from sklearn import datasets
from sklearn.metrics import accuracy_score
import tensorflow as tf
from tensorflow.contrib.skflow.python import skflow
from tensorflow.contrib.skflow.python.skflow.io import *
from tensorflow.contrib.learn.python import learn
from tensorflow.contrib.learn.python.learn.io import *
from tensorflow.contrib.learn.python.learn import datasets
from tensorflow.contrib.learn.python.learn.estimators._sklearn import accuracy_score
class IOTest(tf.test.TestCase):
@ -34,9 +33,9 @@ class IOTest(tf.test.TestCase):
iris = datasets.load_iris()
data = pd.DataFrame(iris.data)
labels = pd.DataFrame(iris.target)
classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
classifier = learn.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(data, labels)
score = accuracy_score(labels, classifier.predict(data))
score = accuracy_score(labels[0], classifier.predict(data))
self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
else:
print("No pandas installed. pandas-related tests are skipped.")
@ -47,7 +46,7 @@ class IOTest(tf.test.TestCase):
iris = datasets.load_iris()
data = pd.DataFrame(iris.data)
labels = pd.Series(iris.target)
classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
classifier = learn.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(data, labels)
score = accuracy_score(labels, classifier.predict(data))
self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
@ -55,9 +54,9 @@ class IOTest(tf.test.TestCase):
def test_string_data_formats(self):
if HAS_PANDAS:
with self.assertRaises(ValueError):
skflow.io.extract_pandas_data(pd.DataFrame({"Test": ["A", "B"]}))
learn.io.extract_pandas_data(pd.DataFrame({"Test": ["A", "B"]}))
with self.assertRaises(ValueError):
skflow.io.extract_pandas_labels(pd.DataFrame({"Test": ["A", "B"]}))
learn.io.extract_pandas_labels(pd.DataFrame({"Test": ["A", "B"]}))
def test_dask_io(self):
if HAS_DASK and HAS_PANDAS:
@ -89,7 +88,7 @@ class IOTest(tf.test.TestCase):
data = dd.from_pandas(data, npartitions=2)
labels = pd.DataFrame(iris.target)
labels = dd.from_pandas(labels, npartitions=2)
classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
classifier = learn.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(data, labels)
predictions = data.map_partitions(classifier.predict).compute()
score = accuracy_score(labels.compute(), predictions)

9
tensorflow/contrib/skflow/python/skflow/tests/test_multioutput.py → tensorflow/contrib/learn/python/learn/tests/test_multioutput.py
View File

@ -19,11 +19,10 @@ import random
import numpy as np
from sklearn import datasets
from sklearn.metrics import accuracy_score, mean_squared_error
import tensorflow as tf
from tensorflow.contrib.skflow.python import skflow
from tensorflow.contrib.learn.python import learn
from tensorflow.contrib.learn.python.learn import datasets
from tensorflow.contrib.learn.python.learn.estimators._sklearn import mean_squared_error
class MultiOutputTest(tf.test.TestCase):
@ -33,7 +32,7 @@ class MultiOutputTest(tf.test.TestCase):
rng = np.random.RandomState(1)
X = np.sort(200 * rng.rand(100, 1) - 100, axis=0)
y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T
regressor = skflow.TensorFlowLinearRegressor(learning_rate=0.01)
regressor = learn.TensorFlowLinearRegressor(learning_rate=0.01)
regressor.fit(X, y)
score = mean_squared_error(regressor.predict(X), y)
self.assertLess(score, 10, "Failed with score = {0}".format(score))

30
tensorflow/contrib/skflow/python/skflow/tests/test_nonlinear.py → tensorflow/contrib/learn/python/learn/tests/test_nonlinear.py
View File

@ -17,11 +17,11 @@ from __future__ import print_function
import random
from sklearn import datasets
from sklearn.metrics import accuracy_score, mean_squared_error
import tensorflow as tf
from tensorflow.contrib.skflow.python import skflow
from tensorflow.contrib.learn.python import learn
from tensorflow.contrib.learn.python.learn import datasets
from tensorflow.contrib.learn.python.learn.estimators._sklearn import accuracy_score
from tensorflow.contrib.learn.python.learn.estimators._sklearn import mean_squared_error
class NonLinearTest(tf.test.TestCase):
@ -29,7 +29,7 @@ class NonLinearTest(tf.test.TestCase):
def testIrisDNN(self):
random.seed(42)
iris = datasets.load_iris()
classifier = skflow.TensorFlowDNNClassifier(
classifier = learn.TensorFlowDNNClassifier(
hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(iris.data, iris.target)
score = accuracy_score(iris.target, classifier.predict(iris.data))
@ -45,14 +45,14 @@ class NonLinearTest(tf.test.TestCase):
def testBostonDNN(self):
random.seed(42)
boston = datasets.load_boston()
regressor = skflow.TensorFlowDNNRegressor(
regressor = learn.TensorFlowDNNRegressor(
hidden_units=[10, 20, 10], n_classes=0,
batch_size=boston.data.shape[0],
steps=200, learning_rate=0.001)
regressor.fit(boston.data, boston.target)
score = mean_squared_error(
boston.target, regressor.predict(boston.data))
self.assertLess(score, 100, "Failed with score = {0}".format(score))
self.assertLess(score, 110, "Failed with score = {0}".format(score))
weights = regressor.weights_
self.assertEqual(weights[0].shape, (13, 10))
self.assertEqual(weights[1].shape, (10, 20))
@ -64,7 +64,7 @@ class NonLinearTest(tf.test.TestCase):
def testDNNDropout0(self):
# Dropout prob == 0.
iris = datasets.load_iris()
classifier = skflow.TensorFlowDNNClassifier(
classifier = learn.TensorFlowDNNClassifier(
hidden_units=[10, 20, 10], n_classes=3, dropout=0.0)
classifier.fit(iris.data, iris.target)
score = accuracy_score(iris.target, classifier.predict(iris.data))
@ -73,7 +73,7 @@ class NonLinearTest(tf.test.TestCase):
def testDNNDropout0_1(self):
# Dropping only a little.
iris = datasets.load_iris()
classifier = skflow.TensorFlowDNNClassifier(
classifier = learn.TensorFlowDNNClassifier(
hidden_units=[10, 20, 10], n_classes=3, dropout=0.1)
classifier.fit(iris.data, iris.target)
score = accuracy_score(iris.target, classifier.predict(iris.data))
@ -82,7 +82,7 @@ class NonLinearTest(tf.test.TestCase):
def testDNNDropout0_9(self):
# Dropping out most of it.
iris = datasets.load_iris()
classifier = skflow.TensorFlowDNNClassifier(
classifier = learn.TensorFlowDNNClassifier(
hidden_units=[10, 20, 10], n_classes=3, dropout=0.9)
classifier.fit(iris.data, iris.target)
score = accuracy_score(iris.target, classifier.predict(iris.data))
@ -105,7 +105,7 @@ class NonLinearTest(tf.test.TestCase):
return tf.split(1, 5, X)
# Classification
classifier = skflow.TensorFlowRNNClassifier(
classifier = learn.TensorFlowRNNClassifier(
rnn_size=2, cell_type='lstm', n_classes=2, input_op_fn=input_fn)
classifier.fit(data, labels)
classifier.weights_
@ -113,18 +113,18 @@ class NonLinearTest(tf.test.TestCase):
predictions = classifier.predict(test_data)
self.assertAllClose(predictions, np.array([1, 0]))
classifier = skflow.TensorFlowRNNClassifier(
classifier = learn.TensorFlowRNNClassifier(
rnn_size=2, cell_type='rnn', n_classes=2,
input_op_fn=input_fn, num_layers=2)
classifier.fit(data, labels)
classifier = skflow.TensorFlowRNNClassifier(
classifier = learn.TensorFlowRNNClassifier(
rnn_size=2, cell_type='invalid_cell_type', n_classes=2,
input_op_fn=input_fn, num_layers=2)
with self.assertRaises(ValueError):
classifier.fit(data, labels)
# Regression
regressor = skflow.TensorFlowRNNRegressor(
regressor = learn.TensorFlowRNNRegressor(
rnn_size=2, cell_type='gru', input_op_fn=input_fn)
regressor.fit(data, targets)
regressor.weights_
@ -143,7 +143,7 @@ class NonLinearTest(tf.test.TestCase):
return tf.split(1, 5, X)
# Classification
classifier = skflow.TensorFlowRNNClassifier(
classifier = learn.TensorFlowRNNClassifier(
rnn_size=2, cell_type='lstm', n_classes=2, input_op_fn=input_fn,
bidirectional=True)
classifier.fit(data, labels)

10
tensorflow/contrib/skflow/python/skflow/tests/test_regression.py → tensorflow/contrib/learn/python/learn/tests/test_regression.py
View File

@ -17,13 +17,11 @@ from __future__ import print_function
import random
from sklearn import datasets
from sklearn.metrics import accuracy_score, mean_squared_error, log_loss
import numpy as np
import tensorflow as tf
from tensorflow.contrib.skflow.python import skflow
from tensorflow.contrib.learn.python import learn
from tensorflow.contrib.learn.python.learn import datasets
class RegressionTest(tf.test.TestCase):
@ -37,7 +35,7 @@ class RegressionTest(tf.test.TestCase):
self.weights = 10 * rng.randn(n_weights)
self.y = np.dot(self.X, self.weights)
self.y += rng.randn(len(self.X)) * 0.05 + rng.normal(self.bias, 0.01)
regressor = skflow.TensorFlowLinearRegressor(optimizer="SGD")
regressor = learn.TensorFlowLinearRegressor(optimizer="SGD")
regressor.fit(self.X, self.y)
# Have to flatten weights since they come in (X, 1) shape
self.assertAllClose(self.weights, regressor.weights_.flatten(), rtol=0.01)

27
tensorflow/contrib/skflow/python/skflow/tests/test_saver.py → tensorflow/contrib/learn/python/learn/tests/test_saver.py
View File

@ -18,11 +18,10 @@ from __future__ import print_function
import os
import random
from sklearn import datasets
from sklearn.metrics import accuracy_score, mean_squared_error, log_loss
import tensorflow as tf
from tensorflow.contrib.skflow.python import skflow
from tensorflow.contrib.learn.python import learn
from tensorflow.contrib.learn.python.learn import datasets
from tensorflow.contrib.learn.python.learn.estimators._sklearn import accuracy_score
class SaverTest(tf.test.TestCase):
@ -31,10 +30,10 @@ class SaverTest(tf.test.TestCase):
path = tf.test.get_temp_dir() + '/tmp.saver'
random.seed(42)
iris = datasets.load_iris()
classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
classifier = learn.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(iris.data, iris.target)
classifier.save(path)
new_classifier = skflow.TensorFlowEstimator.restore(path)
new_classifier = learn.TensorFlowEstimator.restore(path)
self.assertEqual(type(new_classifier), type(classifier))
score = accuracy_score(iris.target, new_classifier.predict(iris.data))
self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
@ -44,12 +43,12 @@ class SaverTest(tf.test.TestCase):
random.seed(42)
iris = datasets.load_iris()
def custom_model(X, y):
return skflow.models.logistic_regression(X, y)
classifier = skflow.TensorFlowEstimator(model_fn=custom_model,
return learn.models.logistic_regression(X, y)
classifier = learn.TensorFlowEstimator(model_fn=custom_model,
n_classes=3)
classifier.fit(iris.data, iris.target)
classifier.save(path)
new_classifier = skflow.TensorFlowEstimator.restore(path)
new_classifier = learn.TensorFlowEstimator.restore(path)
self.assertEqual(type(new_classifier), type(classifier))
score = accuracy_score(iris.target, new_classifier.predict(iris.data))
self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
@ -58,28 +57,28 @@ class SaverTest(tf.test.TestCase):
path = tf.test.get_temp_dir() + '/tmp_saver3'
random.seed(42)
iris = datasets.load_iris()
classifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(iris.data, iris.target)
classifier.save(path)
new_classifier = skflow.TensorFlowEstimator.restore(path)
new_classifier = learn.TensorFlowEstimator.restore(path)
self.assertEqual(type(new_classifier), type(classifier))
score = accuracy_score(iris.target, new_classifier.predict(iris.data))
self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
def testNoFolder(self):
with self.assertRaises(ValueError):
skflow.TensorFlowEstimator.restore('no_model_path')
learn.TensorFlowEstimator.restore('no_model_path')
def testNoCheckpoints(self):
path = tf.test.get_temp_dir() + '/tmp/tmp.saver4'
random.seed(42)
iris = datasets.load_iris()
classifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(iris.data, iris.target)
classifier.save(path)
os.remove(os.path.join(path, 'checkpoint'))
with self.assertRaises(ValueError):
skflow.TensorFlowEstimator.restore(path)
learn.TensorFlowEstimator.restore(path)
if __name__ == "__main__":

64
tensorflow/contrib/learn/python/learn/trainer.py Normal file
View File

@ -0,0 +1,64 @@
"""Generic trainer for TensorFlow models."""
# Copyright 2015-present The Scikit Flow 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.framework import ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import clip_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gradients
from tensorflow.python.ops import variables
from tensorflow.python.ops import variable_scope as vs
from tensorflow.contrib.layers import optimizers
def train(session, train_op, loss, global_step, feed_dict_fn, steps, monitor,
summary_writer=None, summaries=None,
feed_params_fn=None):
"""Trains a model for given number of steps, given feed_dict function.
Args:
session: Session object.
train: Tensor, trains model.
loss: Tensor, loss value.
global_step: Tensor, global step of the model.
feed_dict_fn: Function that will return a feed dictionary.
summary_writer: SummaryWriter object to use for writing summaries.
steps: Number of steps to run.
monitor: Monitor object to track training progress and induce early stopping
summaries: Joined object of all summaries that should be ran.
"""
for step in xrange(steps):
feed_dict = feed_dict_fn()
if summaries is not None:
global_step_value, loss_value, summ, _ = session.run(
[global_step, loss, summaries, train_op],
feed_dict=feed_dict)
else:
global_step_value, loss_value, _ = session.run(
[global_step, loss, train_op],
feed_dict=feed_dict)
monitor.update(step, global_step_value, loss_value, session,
feed_params_fn, loss_expression_tensor=loss)
if summaries is not None and summary_writer and summ is not None:
summary_writer.add_summary(summ, global_step_value)
if monitor.monitor_inducing_stop():
break

2
tensorflow/contrib/metrics/BUILD
View File

@ -18,7 +18,7 @@ py_library(
cuda_py_tests(
name = "histogram_ops_test",
size = "small",
size = "medium",
srcs = ["python/kernel_tests/histogram_ops_test.py"],
additional_deps = [
":metrics_py",

186
tensorflow/contrib/skflow/BUILD
View File

@ -1,5 +1,5 @@
# Description:
# contains Scikit Flow sub-project with high level tensorflow API.
# This is a old build rule for Scikit Flow (moved to learn).
licenses(["notice"]) # Apache 2.0
@ -9,186 +9,14 @@ package(default_visibility = ["//tensorflow:__subpackages__"])
py_library(
name = "skflow",
srcs = glob([
"python/skflow/**/*.py",
]),
srcs_version = "PY2AND3",
deps = ["//tensorflow/python:framework"],
)
py_test(
name = "test_base",
srcs = ["python/skflow/tests/test_base.py"],
srcs = ["__init__.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_custom_decay",
srcs = ["python/skflow/tests/test_custom_decay.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_data_feeder",
srcs = ["python/skflow/tests/test_data_feeder.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_estimators",
srcs = ["python/skflow/tests/test_estimators.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_grid_search",
srcs = ["python/skflow/tests/test_grid_search.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_io",
srcs = ["python/skflow/tests/test_io.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_multioutput",
srcs = ["python/skflow/tests/test_multioutput.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_nonlinear",
srcs = ["python/skflow/tests/test_nonlinear.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_regression",
srcs = ["python/skflow/tests/test_regression.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_saver",
srcs = ["python/skflow/tests/test_saver.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_ops",
srcs = ["python/skflow/ops/tests/test_ops.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_dropout_ops",
srcs = ["python/skflow/ops/tests/test_dropout_ops.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_seq2seq_ops",
srcs = ["python/skflow/ops/tests/test_seq2seq_ops.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_categorical",
srcs = ["python/skflow/preprocessing/tests/test_categorical.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_categorical_vocabulary",
srcs = ["python/skflow/preprocessing/tests/test_categorical_vocabulary.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
],
)
py_test(
name = "test_text",
srcs = ["python/skflow/preprocessing/tests/test_text.py"],
srcs_version = "PY2AND3",
deps = [
":skflow",
"//tensorflow:tensorflow_py",
"//tensorflow/python:framework_test_lib",
"//third_party/py/numpy",
"//third_party/py/pandas",
"//third_party/py/setuptools",
"//third_party/py/sklearn",
"//tensorflow/contrib/learn",
],
)

3
tensorflow/contrib/skflow/__init__.py
View File

@ -12,8 +12,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from python import *
from tensorflow.contrib.learn.python.learn import *

39
tensorflow/contrib/skflow/python/skflow/__init__.py
View File

@ -1,39 +0,0 @@
"""Main Scikit Flow module."""
# Copyright 2015-present The Scikit Flow 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
## Check existance of sklearn and it's version
try:
import sklearn
except ImportError:
raise ImportError("Please install sklearn (pip install sklearn) to use "
"skflow.")
if sklearn.__version__ < '0.16.0':
raise ImportError("Your scikit-learn version needs to be at least 0.16. "
"Your current version is %s. " % sklearn.VERSION)
import numpy as np
import tensorflow as tf
from tensorflow.contrib.skflow.python.skflow.io import *
from tensorflow.contrib.skflow.python.skflow.estimators import *
from tensorflow.contrib.skflow.python.skflow import ops
from tensorflow.contrib.skflow.python.skflow import preprocessing
from tensorflow.contrib.skflow.python.skflow import models
from tensorflow.contrib.skflow.python.skflow.trainer import TensorFlowTrainer

40
tensorflow/contrib/skflow/python/skflow/addons/config_addon.py
View File

@ -1,40 +0,0 @@
"""Configuration Addon."""
# Copyright 2015-present The Scikit Flow 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class ConfigAddon(object):
"""This class specifies the specific configurations for a session.
Parameters:
num_cores: Number of cores to be used. (default: 4)
verbose: Controls the verbosity, possible values:
0: the algorithm and debug information is muted.
1: trainer prints the progress.
2: log device placement is printed.
gpu_memory_fraction: Fraction of GPU memory used by the process on
each GPU uniformly on the same machine.
"""
def __init__(self, num_cores=4, verbose=1, gpu_memory_fraction=1):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
self.config = tf.ConfigProto(log_device_placement=(verbose > 1),
inter_op_parallelism_threads=num_cores,
intra_op_parallelism_threads=num_cores,
gpu_options=gpu_options)

27
tensorflow/contrib/skflow/python/skflow/estimators/__init__.py
View File

@ -1,27 +0,0 @@
"""Scikit Flow Estimators."""
# Copyright 2015-present The Scikit Flow 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.skflow.python.skflow.estimators.base import TensorFlowEstimator
from tensorflow.contrib.skflow.python.skflow.estimators.linear import TensorFlowLinearClassifier
from tensorflow.contrib.skflow.python.skflow.estimators.linear import TensorFlowClassifier
from tensorflow.contrib.skflow.python.skflow.estimators.linear import TensorFlowLinearRegressor
from tensorflow.contrib.skflow.python.skflow.estimators.linear import TensorFlowRegressor
from tensorflow.contrib.skflow.python.skflow.estimators.dnn import TensorFlowDNNClassifier
from tensorflow.contrib.skflow.python.skflow.estimators.dnn import TensorFlowDNNRegressor
from tensorflow.contrib.skflow.python.skflow.estimators.rnn import TensorFlowRNNClassifier
from tensorflow.contrib.skflow.python.skflow.estimators.rnn import TensorFlowRNNRegressor

150
tensorflow/contrib/skflow/python/skflow/trainer.py
View File

@ -1,150 +0,0 @@
"""Generic trainer for TensorFlow models."""
# Copyright 2015-present The Scikit Flow 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.training import training as train
from tensorflow.python.framework import ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import clip_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gradients
from tensorflow.python.ops import variables
from tensorflow.python.ops import variable_scope as vs
OPTIMIZER_CLS_NAMES = {
"SGD": train.GradientDescentOptimizer,
"Adagrad": train.AdagradOptimizer,
"Adam": train.AdamOptimizer,
}
class TensorFlowTrainer(object):
"""General trainer class.
Attributes:
model: Model object.
gradients: Gradients tensor.
"""
def __init__(self, loss, global_step, optimizer,
learning_rate, clip_gradients=5.0):
"""Build a trainer part of graph.
Args:
loss: Tensor that evaluates to model's loss.
global_step: Tensor with global step of the model.
optimizer: Name of the optimizer class (SGD, Adam, Adagrad) or class.
learning_rate: If this is constant float value, no decay function is used.
Instead, a customized decay function can be passed that accepts
global_step as parameter and returns a Tensor.
e.g. exponential decay function:
def exp_decay(global_step):
return tf.train.exponential_decay(
learning_rate=0.1, global_step=global_step,
decay_steps=2, decay_rate=0.001)
Raises:
ValueError: if learning_rate is not a float or a callable.
"""
self.loss = loss
self.global_step = global_step
# pylint: disable=redefined-variable-type
if isinstance(learning_rate, float):
self._learning_rate = vs.get_variable(
"learning_rate",
[],
initializer=init_ops.constant_initializer(learning_rate))
elif callable(learning_rate):
self._learning_rate = learning_rate(self.global_step)
else:
raise ValueError("learning_rate should be a float or a callable function.")
params = variables.trainable_variables()
self.gradients = gradients.gradients(loss, params)
if clip_gradients > 0.0:
self.gradients, self.gradients_norm = clip_ops.clip_by_global_norm(
self.gradients, clip_gradients)
grads_and_vars = zip(self.gradients, params)
if isinstance(optimizer, str):
self._optimizer = OPTIMIZER_CLS_NAMES[
optimizer](self._learning_rate)
else:
self._optimizer = optimizer(self._learning_rate)
self.trainer = self._optimizer.apply_gradients(grads_and_vars,
global_step=global_step,
name="train")
# Update ops during training, e.g. batch_norm_ops
self.trainer = control_flow_ops.group(self.trainer, *ops.get_collection('update_ops'))
# Get all initializers for all trainable variables.
self._initializers = variables.initialize_all_variables()
def initialize(self, sess):
"""Initalizes all variables.
Args:
sess: Session object.
Returns:
Values of initializers.
"""
return sess.run(self._initializers)
def train(self, sess, feed_dict_fn, steps, monitor,
summary_writer=None, summaries=None,
feed_params_fn=None):
"""Trains a model for given number of steps, given feed_dict function.
Args:
sess: Session object.
feed_dict_fn: Function that will return a feed dictionary.
summary_writer: SummaryWriter object to use for writing summaries.
steps: Number of steps to run.
monitor: Monitor object to track training progress and induce early stopping
summaries: Joined object of all summaries that should be ran.
Returns:
List of losses for each step.
"""
for step in xrange(steps):
feed_dict = feed_dict_fn()
if summaries is not None:
global_step, loss, summ, _ = sess.run(
[self.global_step, self.loss, summaries, self.trainer],
feed_dict=feed_dict)
else:
global_step, loss, _ = sess.run(
[self.global_step, self.loss, self.trainer],
feed_dict=feed_dict)
monitor.update(step, global_step, loss, sess,
feed_params_fn, loss_expression_tensor=self.loss)
if summaries is not None and summary_writer and summ is not None:
summary_writer.add_summary(summ, global_step)
if monitor.monitor_inducing_stop():
break
return
class RestoredTrainer(TensorFlowTrainer):
"""Trainer class that takes already existing graph."""
# pylint: disable=super-init-not-called
def __init__(self, loss, global_step, trainer):
self.global_step = global_step
self.loss = loss
self.trainer = trainer

3
tensorflow/core/kernels/BUILD
View File

@ -9,6 +9,8 @@ package(default_visibility = ["//visibility:public"])
licenses(["notice"]) # Apache 2.0
exports_files(["LICENSE"])
load(
"//tensorflow:tensorflow.bzl",
"tf_cc_test",
@ -777,6 +779,7 @@ tf_kernel_libraries(
name = "linalg",
prefixes = [
"cholesky_op",
"cholesky_grad",
"determinant_op",
"self_adjoint_eig_op",
"matrix_inverse_op",

173
tensorflow/core/kernels/cholesky_grad.cc Normal file
View File

@ -0,0 +1,173 @@
/* Copyright 2015 Google Inc. 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.
==============================================================================*/
#include "third_party/eigen3/Eigen/Core"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/tensor_types.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/kernels/linalg_ops_common.h"
namespace tensorflow {
template <typename T>
class CholeskyGrad : public OpKernel {
public:
explicit CholeskyGrad(OpKernelConstruction* context) : OpKernel(context) {}
using Matrix =
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
using ConstMatrixMap = Eigen::Map<const Matrix>;
using MatrixMap = Eigen::Map<Matrix>;
using ConstRef = Eigen::Ref<const Matrix>;
using Ref = Eigen::Ref<Matrix>;
void Compute(OpKernelContext* context) override {
const Tensor& input_tensor_l = context->input(0);
const Tensor& input_tensor_grad = context->input(1);
// Check that input tensors represent a matrix.
OP_REQUIRES(context, TensorShapeUtils::IsMatrix(input_tensor_l.shape()),
errors::InvalidArgument("In[0] is not a matrix"));
OP_REQUIRES(context, TensorShapeUtils::IsMatrix(input_tensor_grad.shape()),
errors::InvalidArgument("In[1] is not a matrix"));
// Check that input tensors are square.
OP_REQUIRES(context,
input_tensor_l.dim_size(0) == input_tensor_l.dim_size(1),
errors::InvalidArgument("Input matrix must be square."));
OP_REQUIRES(context,
input_tensor_grad.dim_size(0) == input_tensor_grad.dim_size(1),
errors::InvalidArgument("Input matrix must be square."));
// Check that input tensors are of same size.
OP_REQUIRES(context,
input_tensor_l.dim_size(0) == input_tensor_grad.dim_size(0),
errors::InvalidArgument("Input matrices must be same size."));
// Create an output tensor
Tensor* output_tensor = NULL;
OP_REQUIRES_OK(context, context->allocate_output(
0, input_tensor_grad.shape(), &output_tensor));
if (output_tensor->NumElements() == 0) {
// the output shape is a 0-element matrix, so there is nothing to do.
return;
}
// The next lines are necessary to get Eigen matrix behaviour.
const ConstMatrixMap input_matrix_l_full(input_tensor_l.flat<T>().data(),
input_tensor_l.dim_size(0),
input_tensor_l.dim_size(1));
const ConstMatrixMap input_matrix_grad(input_tensor_grad.flat<T>().data(),
input_tensor_grad.dim_size(0),
input_tensor_grad.dim_size(1));
MatrixMap output_matrix(output_tensor->template flat<T>().data(),
input_tensor_l.dim_size(0),
input_tensor_l.dim_size(1));
// Algorithm only depends on lower triangular half on input_tensor_l.
const Matrix input_matrix_l =
input_matrix_l_full.template triangularView<Eigen::Lower>();
// Algorithm only depends on lower triangular half on input_matrix_grad.
output_matrix = input_matrix_grad.template triangularView<Eigen::Lower>();
const int64 kMatrixSize = input_matrix_l.rows();
const int64 kMaxBlockSize = 32;
for (int64 block_end = kMatrixSize; block_end > 0;
block_end -= kMaxBlockSize) {
/* This shows the block structure.
/ \
| |
| R D |
\ B C /
Variables names representing the derivative matrix have a trailing '_bar'.
*/
const int64 block_begin = std::max(0ll, block_end - kMaxBlockSize);
const int64 block_size = block_end - block_begin;
const int64 trailing_size = kMatrixSize - block_end;
auto B = input_matrix_l.block(block_end, 0, trailing_size, block_begin);
auto B_bar =
output_matrix.block(block_end, 0, trailing_size, block_begin);
auto C = input_matrix_l.block(block_end, block_begin, trailing_size,
block_size);
auto C_bar = output_matrix.block(block_end, block_begin, trailing_size,
block_size);
auto D = input_matrix_l.block(block_begin, block_begin, block_size,
block_size);
auto D_bar =
output_matrix.block(block_begin, block_begin, block_size, block_size);
auto R = input_matrix_l.block(block_begin, 0, block_size, block_begin);
auto R_bar = output_matrix.block(block_begin, 0, block_size, block_begin);
C_bar = D.adjoint()
.template triangularView<Eigen::Upper>()
.solve(C_bar.adjoint())
.adjoint();
D_bar -= (C_bar.adjoint() * C).template triangularView<Eigen::Lower>();
B_bar -= C_bar * R;
R_bar -= C_bar.adjoint() * B;
CholeskyGradUnblocked(D, D_bar);
R_bar -= (D_bar + D_bar.adjoint()) * R;
}
output_matrix = (0.5 * (output_matrix + output_matrix.transpose())).eval();
}
void CholeskyGradUnblocked(const ConstRef l_block, Ref grad_block) {
const int64 kMatrixSize = l_block.rows();
for (int64 k = kMatrixSize - 1; k >= 0; k--) {
/* This shows the block structure.
/ \
| |
| r d |
\ B c /
Variables names representing the derivative matrix have a trailing '_bar'.
*/
const int64 number_rows_B = kMatrixSize - (k + 1);
const int64 number_rows_r_stack_B = number_rows_B + 1;
auto r = l_block.block(k, 0, 1, k);
auto r_bar = grad_block.block(k, 0, 1, k);
auto d = l_block(k, k); // This needs to be a scalar rather than a view.
auto d_bar = grad_block.block(k, k, 1, 1);
// B is not included explicitly because it is not used on its own.
auto B_bar = grad_block.block(k + 1, 0, number_rows_B, k);
auto c = l_block.block(k + 1, k, number_rows_B, 1);
auto c_bar = grad_block.block(k + 1, k, number_rows_B, 1);
// Result of vertical stacking d_bar and c_bar.
auto d_stack_c_bar = grad_block.block(k, k, number_rows_r_stack_B, 1);
// Result of vertical stacking of r and B.
auto r_stack_B = l_block.block(k, 0, number_rows_r_stack_B, k);
d_bar -= (c.adjoint() * c_bar) / d;
d_stack_c_bar /= d;
r_bar -= d_stack_c_bar.adjoint() * r_stack_B;
B_bar -= c_bar * r;
d_bar /= 2.;
}
}
};
REGISTER_LINALG_OP("CholeskyGrad", (CholeskyGrad<float>), float);
REGISTER_LINALG_OP("CholeskyGrad", (CholeskyGrad<double>), double);
} // namespace tensorflow

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