#coding:utf-8 import tensorflow as tf tf.reset_default_graph() image = tf.random_normal([1, 112, 96, 3]) in_channels = 3 out_channels = 32 kernel_size = 5 conv_weight = tf.Variable(tf.truncated_normal([kernel_size, kernel_size, in_channels, out_channels], stddev=0.1, dtype=tf.float32)) print ‘image shape‘, image.get_shape() print ‘conv weight shape‘, conv_weight.get_shape() bias = tf.Variable(tf.zeros([out_channels], dtype=tf.float32)) conv = tf.nn.conv2d(image, conv_weight, strides=[1, 3, 3, 1], padding=‘SAME‘) conv = tf.nn.bias_add(conv, bias) print ‘conv output shape with SAME padded‘, conv.get_shape() conv = tf.nn.conv2d(image, conv_weight, strides=[1, 3, 3, 1], padding=‘VALID‘) conv = tf.nn.bias_add(conv, bias) print ‘conv output shape with VALID padded‘, conv.get_shape() ‘‘‘ 两种padding方式的不同 SAME 简而言之就是丢弃，像素不够的时候对那部分不进行卷积，输出图像的宽高计算公式如下(向上取整，进1)： HEIGHT = ceil(float(in_height)/float(strides[1])) WIDTH = ceil(float(in_width)/float(strides[2])) VALID 简而言之就是补全，像素不够的时候补0，输出图像的宽高计算公式如下 HEIGHT = ceil(float(in_height - filter_height + 1)/float(strides[1])) WIDTH = ceil(float(in_width - filter_width + 1)/float(strides[2])) ‘‘‘
原文：https://www.cnblogs.com/cnugis/p/9309113.html