# -*- coding: utf-8 -*-

from caffe import layers as L,params as P,to_proto

root='/home/xxx/'

deploy=root+'mnist/deploy.prototxt'    #文件保存路径

def create_deploy():

    #少了第一层，data层

    conv1=L.Convolution(bottom='data', kernel_size=5, stride=1,num_output=20, pad=0,weight_filler=dict(type='xavier'))

    pool1=L.Pooling(conv1, pool=P.Pooling.MAX, kernel_size=2, stride=2)

    conv2=L.Convolution(pool1, kernel_size=5, stride=1,num_output=50, pad=0,weight_filler=dict(type='xavier'))

    pool2=L.Pooling(conv2, pool=P.Pooling.MAX, kernel_size=2, stride=2)

    fc3=L.InnerProduct(pool2, num_output=500,weight_filler=dict(type='xavier'))

    relu3=L.ReLU(fc3, in_place=True)

    fc4 = L.InnerProduct(relu3, num_output=10,weight_filler=dict(type='xavier'))

    #最后没有accuracy层，但有一个Softmax层

    prob=L.Softmax(fc4)

    return to_proto(prob)

def write_deploy():

    with open(deploy, 'w') as f:

        f.write('name:"Lenet"\n')

        f.write('input:"data"\n')

        f.write('input_dim:1\n')

        f.write('input_dim:3\n')

        f.write('input_dim:28\n')

        f.write('input_dim:28\n')

        f.write(str(create_deploy()))

if __name__ == '__main__':

    write_deploy()