From f41959ccb2d9d4c722fe8fc3351401d53bcf4900 Mon Sep 17 00:00:00 2001
From: Manjunath Kudlur <keveman@gmail.com>
Date: Fri, 6 Nov 2015 16:27:58 -0800
Subject: TensorFlow: Initial commit of TensorFlow library. TensorFlow is an
 open source software library for numerical computation using data flow
 graphs.

Base CL: 107276108
---
 tensorflow/python/training/training.py | 138 +++++++++++++++++++++++++++++++++
 1 file changed, 138 insertions(+)
 create mode 100644 tensorflow/python/training/training.py

(limited to 'tensorflow/python/training/training.py')

diff --git a/tensorflow/python/training/training.py b/tensorflow/python/training/training.py
new file mode 100644
index 0000000000..a400e9fa7d
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+++ b/tensorflow/python/training/training.py
@@ -0,0 +1,138 @@
+# pylint: disable=wildcard-import,unused-import,g-bad-import-order,line-too-long
+"""This library provides a set of classes and functions that helps train models.
+
+## Optimizers.
+
+The Optimizer base class provides methods to compute gradients for a loss and
+apply gradients to variables.  A collection of subclasses implement classic
+optimization algorithms such as GradientDescent and Adagrad.
+
+You never instantiate the Optimizer class itself, but instead instantiate one
+of the subclasses.
+
+@@Optimizer
+
+@@GradientDescentOptimizer
+@@AdagradOptimizer
+@@MomentumOptimizer
+@@AdamOptimizer
+@@FtrlOptimizer
+@@RMSPropOptimizer
+
+## Gradient Computation.
+
+TensorFlow provides functions to compute the derivatives for a given
+TensorFlow computation graph, adding operations to the graph. The
+optimizer classes automatically compute derivatives on your graph, but
+creators of new Optimizers or expert users can call the lower-level
+functions below.
+
+@@gradients
+@@AggregationMethod
+
+@@stop_gradient
+
+
+## Gradient Clipping
+
+TensorFlow provides several operations that you can use to add clipping
+functions to your graph. You can use these functions to perform general data
+clipping, but they're particularly useful for handling exploding or vanishing
+gradients.
+
+@@clip_by_value
+@@clip_by_norm
+@@clip_by_average_norm
+@@clip_by_global_norm
+@@global_norm
+
+## Decaying the learning rate.
+@@exponential_decay
+
+## Moving Averages.
+
+Some training algorithms, such as GradientDescent and Momentum often benefit
+from maintaining a moving average of variables during optimization.  Using the
+moving averages for evaluations often improve results significantly.
+
+@@ExponentialMovingAverage
+
+## Coordinator and QueueRunner.
+
+See [Threading and Queues](../../how_tos/threading_and_queues/index.md)
+for how to use threads and queues.  For documentation on the Queue API,
+see [Queues](../../api_docs/python/io_ops.md#queues).
+
+@@Coordinator
+@@QueueRunner
+@@add_queue_runner
+@@start_queue_runners
+
+## Summary Operations.
+
+The following ops output
+[`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
+protocol buffers as serialized string tensors.
+
+You can fetch the output of a summary op in a session, and pass it to a
+[SummaryWriter](train.md#SummaryWriter) to append it to an event file.  You can
+then use TensorBoard to visualize the contents of the event files.  See
+[TensorBoard and Summaries](../../how_tos/summaries_and_tensorboard/index.md)
+for more details.
+
+@@scalar_summary
+@@image_summary
+@@histogram_summary
+@@zero_fraction
+
+@@merge_summary
+@@merge_all_summaries
+
+## Adding Summaries to Event Files.
+
+See [Summaries and
+TensorBoard](../../how_tos/summaries_and_tensorboard/index.md) for an
+overview of summaries, event files, and visualization in TensorBoard.
+
+@@SummaryWriter
+@@summary_iterator
+
+## Training utilities.
+
+@@global_step
+@@write_graph
+"""
+
+# Optimizers.
+from tensorflow.python.training.adagrad import AdagradOptimizer
+from tensorflow.python.training.adam import AdamOptimizer
+from tensorflow.python.training.ftrl import FtrlOptimizer
+from tensorflow.python.training.momentum import MomentumOptimizer
+from tensorflow.python.training.moving_averages import ExponentialMovingAverage
+from tensorflow.python.training.optimizer import Optimizer
+from tensorflow.python.training.rmsprop import RMSPropOptimizer
+from tensorflow.python.training.gradient_descent import GradientDescentOptimizer
+
+# Utility classes for training.
+from tensorflow.python.training.coordinator import Coordinator
+from tensorflow.python.training.queue_runner import *
+
+# For the module level doc.
+from tensorflow.python.training import input as _input
+from tensorflow.python.training.input import *
+
+from tensorflow.python.training.saver import get_checkpoint_state
+from tensorflow.python.training.saver import latest_checkpoint
+from tensorflow.python.training.saver import Saver
+from tensorflow.python.training.saver import update_checkpoint_state
+from tensorflow.python.training.summary_io import summary_iterator
+from tensorflow.python.training.summary_io import SummaryWriter
+from tensorflow.python.training.training_util import write_graph
+from tensorflow.python.training.training_util import global_step
+
+# Training data protos.
+from tensorflow.core.example.example_pb2 import *
+from tensorflow.core.example.feature_pb2 import *
+
+# Utility op.  Open Source. TODO(mdevin): move to nn?
+from tensorflow.python.training.learning_rate_decay import exponential_decay
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