TensorFlow tf.nn 模块解析 - 菜鸟后飞

#TensorFlow tf.nn 模块解析
tf.nn模块中主要存放各种神经元计算单元，如`conv`、`pool`、`relu`等。其全部函数总结如下。
点击**@名称@**可以跳转到该函数的github！查找相关算法论文！

@@relu  
计算特征图的非线性修正: `max(features, 0)`.`def relu(features, name=None)`.
@@relu6  
计算特征图的非线性修正6：`min(max(features, 0), 6)`
@@crelu 
计算拼接后的relu,即`relu(concat([features, -features], -1, name=name))`.
@@elu 
计算指数线性修正 `exp(features) - 1 if < 0,  features  otherwise`.
@@softplus  ```log(exp(features) + 1)```
@@softsign  ```features / (abs(features) + 1)```
@@dropout  
以keep_prob的概率将输入元素值缩放1/keep_prob,否则输出0，采用缩放是为了保证输出值的和不被改变。
默认情况下，每个元素被保留或舍弃是相互独立的。如果指定了`noise_shape`，则情况有所不同。
```
def dropout(x, keep_prob, noise_shape=None, seed=None, name=None):
```
@@bias_add
```
def bias_add(value, bias, data_format=None, name=None):
```
@@sigmoid `y = 1 / (1 + exp(-x))`.
@@log_sigmoid
@@tanh
@@convolution
```
def convolution(input, filter,  # pylint: disable=redefined-builtin
                padding, strides=None, dilation_rate=None,
                name=None, data_format=None):

```
@@conv2d
```
def conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None,
           data_format=None, name=None):
```
@@depthwise_conv2d
```
def depthwise_conv2d(input,
                     filter,
                     strides,
                     padding,
                     rate=None,
                     name=None,
                     data_format=None):
  """Depthwise 2-D convolution.

Given a 4D input tensor ('NHWC' or 'NCHW' data formats)
  and a filter tensor of shape
  `[filter_height, filter_width, in_channels, channel_multiplier]`
  containing `in_channels` convolutional filters of depth 1, `depthwise_conv2d`
  applies a different filter to each input channel (expanding from 1 channel
  to `channel_multiplier` channels for each), then concatenates the results
  together.  The output has `in_channels * channel_multiplier` channels.

```
@@depthwise_conv2d_native
@@separable_conv2d
@@atrous_conv2d
@@atrous_conv2d_transpose
@@conv2d_transpose
@@conv1d
@@conv3d
@@conv3d_transpose
@@conv2d_backprop_filter
@@conv2d_backprop_input
@@conv3d_backprop_filter_v2
@@depthwise_conv2d_native_backprop_filter
@@depthwise_conv2d_native_backprop_input
@@avg_pool
@@max_pool
@@max_pool_with_argmax
@@avg_pool3d
@@max_pool3d
@@fractional_avg_pool
@@fractional_max_pool
@@pool
@@dilation2d
@@erosion2d
@@with_space_to_batch
@@l2_normalize
@@local_response_normalization
@@sufficient_statistics
@@normalize_moments
@@moments
@@weighted_moments
@@fused_batch_norm
@@batch_normalization
@@batch_norm_with_global_normalization
@@l2_loss
@@log_poisson_loss
@@sigmoid_cross_entropy_with_logits
@@softmax
@@log_softmax
@@softmax_cross_entropy_with_logits
@@sparse_softmax_cross_entropy_with_logits
@@weighted_cross_entropy_with_logits
@@embedding_lookup
@@embedding_lookup_sparse
@@dynamic_rnn
@@bidirectional_dynamic_rnn
@@raw_rnn
@@static_rnn
@@static_state_saving_rnn
@@static_bidirectional_rnn
@@ctc_loss
@@ctc_greedy_decoder
@@ctc_beam_search_decoder
@@top_k
@@in_top_k
@@nce_loss
@@sampled_softmax_loss
@@uniform_candidate_sampler
@@log_uniform_candidate_sampler
@@learned_unigram_candidate_sampler
@@fixed_unigram_candidate_sampler
@@compute_accidental_hits
@@quantized_conv2d
@@quantized_relu_x
@@quantized_max_pool
@@quantized_avg_pool
"""

```python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys as _sys

# pylint: disable=unused-import
from tensorflow.python.ops import ctc_ops as _ctc_ops
from tensorflow.python.ops import embedding_ops as _embedding_ops
from tensorflow.python.ops import nn_grad as _nn_grad
from tensorflow.python.ops import nn_ops as _nn_ops
from tensorflow.python.ops.math_ops import sigmoid
from tensorflow.python.ops.math_ops import tanh
# pylint: enable=unused-import
from tensorflow.python.util.all_util import remove_undocumented

# Bring more nn-associated functionality into this package.
# go/tf-wildcard-import
# pylint: disable=wildcard-import,unused-import
from tensorflow.python.ops.ctc_ops import *
from tensorflow.python.ops.nn_impl import *
from tensorflow.python.ops.nn_ops import *
from tensorflow.python.ops.candidate_sampling_ops import *
from tensorflow.python.ops.embedding_ops import *
from tensorflow.python.ops.rnn import *
from tensorflow.python.ops import rnn_cell
# pylint: enable=wildcard-import,unused-import

# TODO(cwhipkey): sigmoid and tanh should not be exposed from tf.nn.
_allowed_symbols = [
    "zero_fraction",  # documented in training.py
    # Modules whitelisted for reference through tf.nn.
    # TODO(cwhipkey): migrate callers to use the submodule directly.
    # Symbols whitelisted for export without documentation.
    # TODO(cwhipkey): review these and move to contrib or expose through
    # documentation.
    "all_candidate_sampler",  # Excluded in gen_docs_combined.
    "lrn",  # Excluded in gen_docs_combined.
    "relu_layer",  # Excluded in gen_docs_combined.
    "xw_plus_b",  # Excluded in gen_docs_combined.
    "rnn_cell",  # rnn_cell is a submodule of tf.nn.
]

remove_undocumented(__name__, _allowed_symbols,
                    [_sys.modules[__name__], _ctc_ops, _nn_ops, _nn_grad])

```