TensorFlow实现inception-v3

import tensorflow as tf

slim = tf.contrib.slim

#设置函数的参数默认取值,这里将这三个函数的stride和padding参数设定好默认值，以后就不需要设置了
#若以后重新设置，则以最新值代替
with slim.arg_scope([slim.conv2d,slim.max_pool2d,slim.avg_pool2d],stride=1,padding='SAME'):
    net = 'net'

    with tf.variable_scope('Mixed_7c'):
        with tf.variable_scope('Branch_0'):
            #第一个参数是输入的网络,第二个是卷积核数量,第三个是卷积核大小
            branch_0 = slim.conv2d(net,320,[1,1],scope='conv_1a')

        with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net,384,[1,1],scope='conv_1a')
            #将多个网络合并,第一个参数是合并的维度,[batch,width,length,depth],3代表合并的维度是深度
            branch_1 = tf.concat(3,[
                slim.conv2d(branch_1,384,[1,3],scope='conv_2a'),
                slim.conv2d(branch_1,384,[3,1],scope='conv_2b')
            ])

        with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net,448,[1,1],scope='conv_1a')
            branch_2 = slim.conv2d(branch_2,384,[3,3],scope='conv_2a')
            branch_2 = tf.concat(3,[
                slim.conv2d(branch_2,384,[1,3],scope='conv_3a'),
                slim.conv2d(branch_2,384,[3,1],scope='conv_3b')
            ])

        with tf.variable_scope('Branch_3'):
            branch_3 = slim.avg_pool2d(net,[3,3],scope='avg_pool_1a')
            branch_3 = slim.conv2d(branch_3,192,[1,1],scope='conv_2a')

        net = tf.concat(3,[branch_0,branch_1,branch_2,branch_3])