利用caffe的Python接口生成prototxt文件

来源：互联网发布：简明python教程豆瓣编辑：程序博客网时间：2024/06/01 21:32

Python版本：Python2.7
mnist数据集
博客来源：http://blog.csdn.net/c406495762/article/details/70306550

如何编译caffe的Python接口就不多说了
下面的代码可以一次生成Lenet网络训练所需的train.prototxt和test.prototxt,还有solver.prototxt

代码:

# -*- coding: UTF-8 -*-import caffe                                                     #导入caffe包def create_net(lmdb, mean_file, batch_size, include_acc=False):    #网络规范    net = caffe.NetSpec()    #Data层    net.data, net.label = caffe.layers.Data(source=lmdb, backend=caffe.params.Data.LMDB, batch_size=batch_size, ntop=2,                                            transform_param = dict(mean_file=mean_file,scale= 0.00390625))    #视觉层    net.conv1 = caffe.layers.Convolution(net.data, num_output=20,kernel_size=5,weight_filler={"type": "xavier"},bias_filler={"type": "constant"})    net.pool1 = caffe.layers.Pooling(net.conv1, pool=caffe.params.Pooling.MAX, kernel_size=2, stride=2)    net.conv2 = caffe.layers.Convolution(net.pool1, kernel_size=5, stride=1,num_output=50, pad=0,weight_filler=dict(type='xavier'),bias_filler={"type": "constant"})    net.pool2 = caffe.layers.Pooling(net.conv2, pool=caffe.params.Pooling.MAX, kernel_size=2, stride=2)    #全连接层    net.fc1 = caffe.layers.InnerProduct(net.pool2, num_output=500,weight_filler=dict(type='xavier'),bias_filler={"type": "constant"})    net.fc_add1 = caffe.layers.InnerProduct(net.fc1, num_output=500,weight_filler=dict(type='xavier'),bias_filler={"type": "constant"})#没什么意义，加一层试试    net.fc_add2 = caffe.layers.InnerProduct(net.fc_add1, num_output=500,weight_filler=dict(type='xavier'),bias_filler={"type": "constant"})#也没什么意义，再加一层试试    #激活层    net.relu1 = caffe.layers.ReLU(net.fc_add2, in_place=True)    #dropout层    net.drop3 = caffe.layers.Dropout(net.fc_add2, in_place=True)    net.fc2 = caffe.layers.InnerProduct(net.fc_add2, num_output=10,weight_filler=dict(type='xavier'))    #sofemax层    net.loss = caffe.layers.SoftmaxWithLoss(net.fc2, net.label)    #训练的prototxt文件不包括Accuracy层,测试的时候需要。    if include_acc:        net.acc = caffe.layers.Accuracy(net.fc2, net.label)        return str(net.to_proto())    return str(net.to_proto())def write_net(mean_file,train_proto, train_lmdb, test_proto, val_lmdb):    #写入prototxt文件    with open(train_proto, 'w') as f:        f.write(str(create_net(train_lmdb,mean_file,batch_size = 64)))    #写入prototxt文件    with open(test_proto, 'w') as f:        f.write(str(create_net(val_lmdb,mean_file,batch_size = 100, include_acc = True)))def write_sovler(my_project_root, solver_proto, train_proto, test_proto):    sovler_string = caffe.proto.caffe_pb2.SolverParameter()                    #sovler存储    sovler_string.train_net = train_proto                                    #train.prototxt位置指定    sovler_string.test_net.append(test_proto)                                 #test.prototxt位置指定    sovler_string.test_iter.append(100)                                        #10000/100 测试迭代次数    sovler_string.test_interval = 938                                        #60000/64 每训练迭代test_interval次进行一次测试    sovler_string.base_lr = 0.01                                            #基础学习率    sovler_string.momentum = 0.9                                            #动量    sovler_string.weight_decay = 5e-4                                        #权重衰减    sovler_string.lr_policy = 'step'                                        #学习策略    sovler_string.stepsize = 3000                                             #学习率变化频率    sovler_string.gamma = 0.1                                                  #学习率变化指数    sovler_string.display = 20                                                #每迭代display次显示结果    sovler_string.max_iter = 9380                                            #10 epoch 938*10 最大迭代数    sovler_string.snapshot = 938                                             #保存临时模型的迭代数    sovler_string.snapshot_prefix = my_project_root + 'mnist/model/mnist'                #模型前缀    sovler_string.solver_mode = caffe.proto.caffe_pb2.SolverParameter.GPU    #优化模式    with open(solver_proto, 'w') as f:        f.write(str(sovler_string))#def train(solver_proto):#    caffe.set_device(0)#    caffe.set_mode_gpu()#    solver = caffe.SGDSolver(solver_proto)#    solver.solve()if __name__ == '__main__':    my_project_root = "F:/python/make_prototxt/"    #my-caffe-project目录    train_lmdb = my_project_root + "mnist/data/mnist_train_lmdb"                #train_lmdb文件的位置    val_lmdb = my_project_root + "mnist/data/mnist_test_lmdb"                    #val_lmdb文件的位置    train_proto = my_project_root + "mnist/train.prototxt"                #保存train.prototxt文件的位置    test_proto = my_project_root + "mnist/test.prototxt"                #保存test.prototxt文件的位置    solver_proto = my_project_root + "mnist/solver.prototxt"            #保存solver.prototxt文件的位置    mean_file = my_project_root + "mnist/data/trainMean.binaryproto"                          #均值文件的位置    write_net(mean_file,train_proto, train_lmdb, test_proto, val_lmdb)    print "生成train.prototxt test.prototxt成功"    write_sovler(my_project_root, solver_proto, train_proto, test_proto)    print "生成solver.prototxt成功"   # train(solver_proto)   # print "训练完成"1
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运行结果:
生成的train.prototxt

layer {  name: "data"  type: "Data"  top: "data"  top: "label"  transform_param {    scale: 0.00390625    mean_file: "F:/python/make_prototxt/mnist/data/trainMean.binaryproto"  }  data_param {    source: "F:/python/make_prototxt/mnist/data/mnist_train_lmdb"    batch_size: 64    backend: LMDB  }}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  convolution_param {    num_output: 20    kernel_size: 5    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool1"  type: "Pooling"  bottom: "conv1"  top: "pool1"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "conv2"  type: "Convolution"  bottom: "pool1"  top: "conv2"  convolution_param {    num_output: 50    pad: 0    kernel_size: 5    stride: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool2"  type: "Pooling"  bottom: "conv2"  top: "pool2"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "fc1"  type: "InnerProduct"  bottom: "pool2"  top: "fc1"  inner_product_param {    num_output: 500    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "fc_add1"  type: "InnerProduct"  bottom: "fc1"  top: "fc_add1"  inner_product_param {    num_output: 500    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "fc_add2"  type: "InnerProduct"  bottom: "fc_add1"  top: "fc_add2"  inner_product_param {    num_output: 500    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "relu1"  type: "ReLU"  bottom: "fc_add2"  top: "fc_add2"}layer {  name: "drop3"  type: "Dropout"  bottom: "fc_add2"  top: "fc_add2"}layer {  name: "fc2"  type: "InnerProduct"  bottom: "fc_add2"  top: "fc2"  inner_product_param {    num_output: 10    weight_filler {      type: "xavier"    }  }}layer {  name: "loss"  type: "SoftmaxWithLoss"  bottom: "fc2"  bottom: "label"  top: "loss"}1
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生成的test.prototxt

layer {  name: "data"  type: "Data"  top: "data"  top: "label"  transform_param {    scale: 0.00390625    mean_file: "F:/python/make_prototxt/mnist/data/trainMean.binaryproto"  }  data_param {    source: "F:/python/make_prototxt/mnist/data/mnist_test_lmdb"    batch_size: 100    backend: LMDB  }}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  convolution_param {    num_output: 20    kernel_size: 5    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool1"  type: "Pooling"  bottom: "conv1"  top: "pool1"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "conv2"  type: "Convolution"  bottom: "pool1"  top: "conv2"  convolution_param {    num_output: 50    pad: 0    kernel_size: 5    stride: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool2"  type: "Pooling"  bottom: "conv2"  top: "pool2"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "fc1"  type: "InnerProduct"  bottom: "pool2"  top: "fc1"  inner_product_param {    num_output: 500    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "fc_add1"  type: "InnerProduct"  bottom: "fc1"  top: "fc_add1"  inner_product_param {    num_output: 500    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "fc_add2"  type: "InnerProduct"  bottom: "fc_add1"  top: "fc_add2"  inner_product_param {    num_output: 500    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "relu1"  type: "ReLU"  bottom: "fc_add2"  top: "fc_add2"}layer {  name: "drop3"  type: "Dropout"  bottom: "fc_add2"  top: "fc_add2"}layer {  name: "fc2"  type: "InnerProduct"  bottom: "fc_add2"  top: "fc2"  inner_product_param {    num_output: 10    weight_filler {      type: "xavier"    }  }}layer {  name: "loss"  type: "SoftmaxWithLoss"  bottom: "fc2"  bottom: "label"  top: "loss"}layer {  name: "acc"  type: "Accuracy"  bottom: "fc2"  bottom: "label"  top: "acc"}1
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生成的solver.prototxt

train_net: "F:/python/make_prototxt/mnist/train.prototxt"test_net: "F:/python/make_prototxt/mnist/test.prototxt"test_iter: 100test_interval: 938base_lr: 0.01display: 20max_iter: 9380lr_policy: "step"gamma: 0.1momentum: 0.9weight_decay: 0.0005stepsize: 3000snapshot: 938snapshot_prefix: "F:/python/make_prototxt/mnist/model/mnist"solver_mode: GPU1
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接下来就可以训练了

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