diff options
Diffstat (limited to 'tensorflow/python/training/training.py')
| -rw-r--r-- | tensorflow/python/training/training.py | 138 |
1 files changed, 138 insertions, 0 deletions
diff --git a/tensorflow/python/training/training.py b/tensorflow/python/training/training.py new file mode 100644 index 0000000000..a400e9fa7d --- /dev/null +++ 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 |