TensorFlow学习笔记（十七）tf.nn.conv2d
在给定的4D input与filter下计算2D卷积输入shape为[batch, height, width, in_channels]
TensorFlow的CNN代码中有
这样一句，本文介绍tf.nn.conv2d的用法：
- tf.nn.conv2d(X, W1, strides=[1, 1, 1, 1], padding='SAME')
tf.nn.conv2d
tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None)
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:
Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels].
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].
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, float64. 输入一个4维数据[batch, in_height,
in_width, in_channels] ；
filter: A Tensor. Must have the same type as input. 过滤器，也是一个4维的Tensor[filter_height, filter_width,
in_channels, out_channels]
strides: A list of ints. 1-D of length 4. The stride of the sliding window for each dimension of input. Must be in the same order as the dimension
specified with format.卷积滑动步长
padding: A string from: "SAME", "VALID". The type of padding algorithm to use.边缘填充.简单理解SAME是边缘填0，左边（上边）补0的个数和右边（上边）补0的个数一样或少一个；VALID不补，多余的还丢弃。
参考1：https://www.tensorflow.org/api_docs/python/nn/convolution#convolution
参考2：
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 defaultformat"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.