caffe的python接口生成配置文件学习

# -*- coding: utf-8 -*-
"""
Spyder Editor
"""
from caffe import layers as L,params as P,to_proto
path='/home/xxx/data/'                    #保存数据和配置文件的路径
train_lmdb=path+'train_db'                #训练数据LMDB文件的位置
val_lmdb=path+'val_db'                    #验证数据LMDB文件的位置
mean_file=path+'mean.binaryproto'         #均值文件的位置
train_proto=path+'train.prototxt'         #生成的训练配置文件保存的位置
val_proto=path+'val.prototxt'             #生成的验证配置文件保存的位置
#编写一个函数，用于生成网络
def create_net(lmdb,batch_size,include_acc=False):
    #创建第一层：数据层。向上传递两类数据：图片数据和对应的标签
    data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
        transform_param=dict(crop_size=40,mean_file=mean_file,mirror=True))
    #创建第二屋：卷积层
    conv1=L.Convolution(data, kernel_size=5, stride=1,num_output=16, pad=2,weight_filler=dict(type='xavier'))
    #创建激活函数层
    relu1=L.ReLU(conv1, in_place=True)
    #创建池化层
    pool1=L.Pooling(relu1, pool=P.Pooling.MAX, kernel_size=3, stride=2)
    conv2=L.Convolution(pool1, kernel_size=3, stride=1,num_output=32, pad=1,weight_filler=dict(type='xavier'))
    relu2=L.ReLU(conv2, in_place=True)
    pool2=L.Pooling(relu2, pool=P.Pooling.MAX, kernel_size=3, stride=2)
    #创建一个全连接层
    fc3=L.InnerProduct(pool2, num_output=1024,weight_filler=dict(type='xavier'))
    relu3=L.ReLU(fc3, in_place=True)
    #创建一个dropout层
    drop3 = L.Dropout(relu3, in_place=True)
    fc4 = L.InnerProduct(drop3, num_output=10,weight_filler=dict(type='xavier'))
    #创建一个softmax层
    loss = L.SoftmaxWithLoss(fc4, label)
    if include_acc:             #在训练阶段，不需要accuracy层，但是在验证阶段，是需要的
        acc = L.Accuracy(fc4, label)
        return to_proto(loss, acc)
    else:
        return to_proto(loss)
def write_net():
    #将以上的设置写入到prototxt文件
    with open(train_proto, 'w') as f:
        f.write(str(create_net(train_lmdb,batch_size=64)))
    #写入配置文件    
    with open(val_proto, 'w') as f:
        f.write(str(create_net(val_lmdb,batch_size=32, include_acc=True)))
if __name__ == '__main__':
    write_net()

# -*- coding: utf-8 -*-
from caffe import layers as L,params as P,to_proto
path='/home/xxx/data/'
train_list=path+'train.txt'
val_list=path+'val.txt'           
train_proto=path+'train.prototxt'   
val_proto=path+'val.prototxt'       
def create_net(img_list,batch_size,include_acc=False):
    data,label=L.ImageData(source=img_list,batch_size=batch_size,new_width=48,new_height=48,ntop=2,
                           transform_param=dict(crop_size=40,mirror=True))
    conv1=L.Convolution(data, kernel_size=5, stride=1,num_output=16, pad=2,weight_filler=dict(type='xavier'))
    relu1=L.ReLU(conv1, in_place=True)
    pool1=L.Pooling(relu1, pool=P.Pooling.MAX, kernel_size=3, stride=2)
    conv2=L.Convolution(pool1, kernel_size=53, stride=1,num_output=32, pad=1,weight_filler=dict(type='xavier'))
    relu2=L.ReLU(conv2, in_place=True)
    pool2=L.Pooling(relu2, pool=P.Pooling.MAX, kernel_size=3, stride=2)
    conv3=L.Convolution(pool2, kernel_size=53, stride=1,num_output=32, pad=1,weight_filler=dict(type='xavier'))
    relu3=L.ReLU(conv3, in_place=True)
    pool3=L.Pooling(relu3, pool=P.Pooling.MAX, kernel_size=3, stride=2)
    fc4=L.InnerProduct(pool3, num_output=1024,weight_filler=dict(type='xavier'))
    relu4=L.ReLU(fc4, in_place=True)
    drop4 = L.Dropout(relu4, in_place=True)
    fc5 = L.InnerProduct(drop4, num_output=7,weight_filler=dict(type='xavier'))
    loss = L.SoftmaxWithLoss(fc5, label)
    if include_acc:             
        acc = L.Accuracy(fc5, label)
        return to_proto(loss, acc)
    else:
        return to_proto(loss)
def write_net():
    #
    with open(train_proto, 'w') as f:
        f.write(str(create_net(train_list,batch_size=64)))
    #    
    with open(val_proto, 'w') as f:
        f.write(str(create_net(val_list,batch_size=32, include_acc=True)))
if __name__ == '__main__':
    write_net()