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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Ops for building neural network layers, regularizers, summaries, etc.
## Higher level ops for building neural network layers.
This package provides several ops that take care of creating variables that are
used internally in a consistent way and provide the building blocks for many
common machine learning algorithms.
@@avg_pool2d
@@batch_norm
@@convolution2d
@@convolution2d_in_plane
@@convolution2d_transpose
@@flatten
@@fully_connected
@@layer_norm
@@max_pool2d
@@one_hot_encoding
@@repeat
@@safe_embedding_lookup_sparse
@@separable_convolution2d
@@stack
@@unit_norm
Aliases for fully_connected which set a default activation function are
available: `relu`, `relu6` and `linear`.
## Regularizers
Regularization can help prevent overfitting. These have the signature
`fn(weights)`. The loss is typically added to
`tf.GraphKeys.REGULARIZATION_LOSSES`.
@@apply_regularization
@@l1_regularizer
@@l2_regularizer
@@sum_regularizer
## Initializers
Initializers are used to initialize variables with sensible values given their
size, data type, and purpose.
@@xavier_initializer
@@xavier_initializer_conv2d
@@variance_scaling_initializer
## Optimization
Optimize weights given a loss.
@@optimize_loss
## Summaries
Helper functions to summarize specific variables or ops.
@@summarize_activation
@@summarize_tensor
@@summarize_tensors
@@summarize_collection
The layers module defines convenience functions `summarize_variables`,
`summarize_weights` and `summarize_biases`, which set the `collection` argument
of `summarize_collection` to `VARIABLES`, `WEIGHTS` and `BIASES`, respectively.
@@summarize_activations
## Feature columns
Feature columns provide a mechanism to map data to a model.
@@bucketized_column
@@check_feature_columns
@@create_feature_spec_for_parsing
@@crossed_column
@@embedding_column
@@hashed_embedding_column
@@input_from_feature_columns
@@joint_weighted_sum_from_feature_columns
@@make_place_holder_tensors_for_base_features
@@one_hot_column
@@parse_feature_columns_from_examples
@@parse_feature_columns_from_sequence_examples
@@real_valued_column
@@shared_embedding_columns
@@sparse_column_with_hash_bucket
@@sparse_column_with_integerized_feature
@@sparse_column_with_keys
@@weighted_sparse_column
@@weighted_sum_from_feature_columns
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
# pylint: disable=unused-import,wildcard-import
from tensorflow.contrib.layers.python.layers import *
from tensorflow.contrib.layers.python.ops import sparse_ops
from tensorflow.python.util.all_util import make_all
__all__ = make_all(__name__)
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