#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]))

'''

pool_size = 3

pool = tf.nn.max_pool(conv, ksize=[1, pool_size, pool_size, 1], strides=[1, 2, 2, 1], padding='SAME')

print pool.get_shape()

pool = tf.nn.max_pool(conv, ksize=[1, pool_size, pool_size, 1], strides=[1, 2, 2, 1], padding='VALID')

print pool.get_shape()

#激活层

relu = tf.nn.relu(pool)

print relu.get_shape()

l2_regularizer = tf.contrib.layers.l2_regularizer(1.0)

def prelu(x, name = 'prelu'):

    with tf.variable_scope(name):

        alphas = tf.get_variable('alpha', x.get_shape()[-1], initializer=tf.constant_initializer(0.25), regularizer=l2_regularizer, dtype=

                                 tf.float32)

    pos = tf.nn.relu(x)

    neg = tf.multiply(alphas, (x - abs(x)) * 0.5)

    return pos + neg

prelu_out = prelu(pool)

print prelu_out.get_shape()