TensorFlow 从入门到精通（八）：TensorFlow tf.nn.conv2d 一路追查

读者可能还记得本系列博客（二）和（六）中 tf.nn 模块，其中最关心的是 conv2d 这个函数。

首先将博客（二） MNIST 例程中 convolutional.py 关键源码列出：

  def model(data, train=False):"""The Model definition."""# 2D convolution, with 'SAME' padding (i.e. the output feature map has# the same size as the input). Note that {strides} is a 4D array whose# shape matches the data layout: [image index, y, x, depth].conv = tf.nn.conv2d(data,conv1_weights,strides=[1, 1, 1, 1],padding='SAME')# Bias and rectified linear non-linearity.relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))

看到第一个卷积层的实现使用 tf.nn.conv2d( input_tensor, weight_tensor, strides_param, padding_method) 这个函数。追踪至 tensorflow/tensorflow/python/ops/gen_nn_ops.py 这个文件中，将代码列出：

def conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None,data_format=None, name=None):r"""Computes a 2-D convolution given 4-D `input` and `filter` tensors.Given an input tensor of shape `[batch, in_height, in_width, in_channels]`and a filter / kernel tensor of shape`[filter_height, filter_width, in_channels, out_channels]`, this opperforms the following:1. Flattens the filter to a 2-D matrix with shape`[filter_height * filter_width * in_channels, output_channels]`.2. Extracts image patches from the input tensor to form a *virtual*tensor of shape `[batch, out_height, out_width,filter_height * filter_width * in_channels]`.3. For each patch, right-multiplies the filter matrix and the image patchvector.In detail, with the default NHWC format,output[b, i, j, k] =sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *filter[di, dj, q, k]Must have `strides[0] = strides[3] = 1`.  For the most common case of the samehorizontal and vertices strides, `strides = [1, stride, stride, 1]`.Args:input: A `Tensor`. Must be one of the following types: `float32`, `float64`.filter: A `Tensor`. Must have the same type as `input`.strides: A list of `ints`.1-D of length 4.  The stride of the sliding window for each dimensionof `input`. Must be in the same order as the dimension specified with format.padding: A `string` from: `"SAME", "VALID"`.The type of padding algorithm to use.use_cudnn_on_gpu: An optional `bool`. Defaults to `True`.data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`.Specify the data format of the input and output data. With thedefault format "NHWC", the data is stored in the order of:[batch, in_height, in_width, in_channels].Alternatively, the format could be "NCHW", the data storage order of:[batch, in_channels, in_height, in_width].name: A name for the operation (optional).Returns:A `Tensor`. Has the same type as `input`."""return _op_def_lib.apply_op("Conv2D", input=input, filter=filter,strides=strides, padding=padding,use_cudnn_on_gpu=use_cudnn_on_gpu,data_format=data_format, name=name)

该文件内容为编译时自动生成。生成器的源码位于 tensorflow/tensorflow/python/framework/python_op_gen.h 和 python_op_gen.cc。

_op_def_lib 是这样构建的：

def _InitOpDefLibrary():op_list = op_def_pb2.OpList()text_format.Merge(_InitOpDefLibrary.op_list_ascii, op_list)op_def_registry.register_op_list(op_list)op_def_lib = op_def_library.OpDefLibrary()op_def_lib.add_op_list(op_list)return op_def_lib_InitOpDefLibrary.op_list_ascii = """%s"""_op_def_lib = _InitOpDefLibrary()

看到 _op_def_lib 实际上是个 op_def_pb2.OpList 对象，实现了记录 TensorFlow 支持全部运算的列表。

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