1. 卷积概念

卷积的过程：如下图所示，用一个3*3的卷积核在5*5的图像上做卷积的过程。

卷积核如下，大小3*3，在原图上滑动的步长为1。（求解过程是：对应位置相成，然后相加）

我们再看一个在三通道图像上的卷积过程，如下：

计算步骤解释如下，原图大小为7*7，通道数为3:，卷积核大小为3*3，Input Volume中的蓝色方框和Filter W0中红色方框的对应位置元素相乘再求和得到res(即，下图中的步骤1.res的计算)，再把res和Bias b0进行相加(即，下图中的步骤2)，得到最终的Output Volume。

2. 卷积函数tf.nn.conv2d

函数源码目录如下(TensorFlow安装方式参考 TensorFlow 学习笔记-01)：

C:\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\ops\gen_nn_ops.py

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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 op
performs 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 patch
vector.
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 same
horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
Args:
input: A `Tensor`. Must be one of the following types: `half`, `float32`.
A 4-D tensor. The dimension order is interpreted according to the value
of `data_format`, see below for details.
filter: A `Tensor`. Must have the same type as `input`.
A 4-D tensor of shape
`[filter_height, filter_width, in_channels, out_channels]`
strides: A list of `ints`.
1-D tensor of length 4. The stride of the sliding window for each
dimension of `input`. The dimension order is determined by the value of
`data_format`, see below for details.
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 the
default format "NHWC", the data is stored in the order of:
[batch, height, width, channels].
Alternatively, the format could be "NCHW", the data storage order of:
[batch, channels, height, width].
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
A 4-D tensor. The dimension order is determined by the value of
`data_format`, see below for details.
"""
result = _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)
return result
_conv2d_backprop_filter_outputs = ["output"]

通过conv2d源码我们可以发现一共有7个参数，参数的详细分析如下：

第一个参数：input

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input: A `Tensor`. Must be one of the following types: `half`, `float32`.
A 4-D tensor. The dimension order is interpreted according to the value
of `data_format`, see below for details.

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input tensor of shape `[batch, in_height, in_width, in_channels]`

通过源码中的描述(如上)，我们可以知道input就是需要做卷积的图像（这里要求用Tensor来表示输入图像，并且Tensor(一个4维的Tensor，要求类型为half或者float32)的shape为[batch, in_height, in_width, in_channels]具体含义[训练时一个batch图像的数量，图像高度，图像宽度，图像通道数]）。

第二个参数：filter

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filter: A `Tensor`. Must have the same type as `input`.
A 4-D tensor of shape
`[filter_height, filter_width, in_channels, out_channels]`

通过源码中的描述(如上)，我们可以知道filter就是卷积核（这里要求用Tensor来表示卷积核，并且Tensor（一个4维的Tensor，要求类型与input相同）的shape为[filter_height, filter_width, in_channels, out_channels]具体含义[卷积核高度，卷积核宽度，图像通道数，卷积核个数]，这里的图片通道数也就input中的图像通道数，二者相同。）

第三个参数：strides

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