卷积神经网络---padding

#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()


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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]))
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