caffe中train_val.prototxt文件和deploy.prototxt文件区别和转换--caffe学习（14）

先放出二者的完整例子文件，然后分析：
train_val.prototxt文件如下：

name: "CaffeNet"layer {name: "data"type: "Data"top: "data"top: "label"include {phase: TRAIN}transform_param {mirror: truecrop_size: 227mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"}# mean pixel / channel-wise mean instead of mean image# transform_param {# crop_size: 227# mean_value: 104# mean_value: 117# mean_value: 123# mirror: true# }data_param {source: "examples/imagenet/ilsvrc12_train_lmdb"batch_size: 256backend: LMDB}}layer {name: "data"type: "Data"top: "data"top: "label"include {phase: TEST}transform_param {mirror: falsecrop_size: 227mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"}# mean pixel / channel-wise mean instead of mean image# transform_param {# crop_size: 227# mean_value: 104# mean_value: 117# mean_value: 123# mirror: false# }data_param {source: "examples/imagenet/ilsvrc12_val_lmdb"batch_size: 50backend: LMDB}}layer {name: "conv1"type: "Convolution"bottom: "data"top: "conv1"param {lr_mult: 1decay_mult: 1}param {lr_mult: 2decay_mult: 0}convolution_param {num_output: 96kernel_size: 11stride: 4weight_filler {type: "gaussian"std: 0.01}bias_filler {type: "constant"value: 0}}}param {lr_mult: 2decay_mult: 0}convolution_param {num_output: 256pad: 2kernel_size: 5group: 2weight_filler {type: "gaussian"std: 0.01}bias_filler {type: "constant"value: 1}m {lr_mult: 2decay_mult: 0}convolution_param {num_output: 384pad: 1kernel_size: 3weight_filler {type: "gaussian"std: 0.01}bias_filler {type: "constant"value: 0}}}layer {name: "relu3"type: "ReLU"bottom: "conv3"top: "conv3"}layer {name: "conv4"type: "Convolution"bottom: "conv3"top: "conv4"param {lr_mult: 1decay_mult: 1}param {lr_mult: 2decay_mult: 0}convolution_param {num_output: 384pad: 1kernel_size: 3group: 2weight_filler {type: "gaussian"std: 0.01}bias_filler {type: "constant"value: 1}}}layer {name: "relu4"type: "ReLU"bottom: "conv4"top: "conv4"}layer {name: "conv5"type: "Convolution"bottom: "conv4"top: "conv5"param {lr_mult: 1decay_mult: 1}param {lr_mult: 2decay_mult: 0}convolution_param {num_output: 256pad: 1kernel_size: 3group: 2weight_filler {type: "gaussian"std: 0.01}bias_filler {type: "constant"value: 1}}layer {name: "fc6"type: "InnerProduct"bottom: "pool5"top: "fc6"param {lr_mult: 1decay_mult: 1}param {lr_mult: 2decay_mult: 0}inner_product_param {num_output: 4096weight_filler {type: "gaussian"std: 0.005}bias_filler {type: "constant"value: 1}}}layer {name: "relu6"type: "ReLU"bottom: "fc6"top: "fc6"}layer {name: "drop6"type: "Dropout"bottom: "fc6"top: "fc6"dropout_param {dropout_ratio: 0.5}layer {name: "relu7"type: "ReLU"bottom: "fc7"top: "fc7"}}layer {name: "fc8"type: "InnerProduct"bottom: "fc7"top: "fc8"param {lr_mult: 1decay_mult: 1}param {lr_mult: 2decay_mult: 0}inner_product_param {num_output: 1000weight_filler {type: "gaussian"std: 0.01}bias_filler {type: "constant"value: 0}}}layer {name: "accuracy"type: "Accuracy"bottom: "fc8"bottom: "label"top: "accuracy"include {phase: TEST}}layer {name: "loss"type: "SoftmaxWithLoss"bottom: "fc8"bottom: "label"top: "loss"}

修改后的deploy.prototxt文件：

name: "CaffeNet"layer {  name: "data"  type: "Input"  top: "data"  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  convolution_param {    num_output: 96    kernel_size: 11    stride: 4  }。。。。。。。。。。。。。。layer {  name: "fc8"  type: "InnerProduct"  bottom: "fc7"  top: "fc8"  inner_product_param {    num_output: 1000  }}layer {  name: "prob"  type: "Softmax"  bottom: "fc8"  top: "prob"}

变化的有几个地方：
1：输入数据层data层：将原来的train和test输入数据参数完全删除，
删除的部分：

layer {name: "data"type: "Data"top: "data"top: "label"include {phase: TRAIN}transform_param {mirror: truecrop_size: 227mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"}# mean pixel / channel-wise mean instead of mean image# transform_param {# crop_size: 227# mean_value: 104# mean_value: 117# mean_value: 123# mirror: true# }data_param {source: "examples/imagenet/ilsvrc12_train_lmdb"batch_size: 256backend: LMDB}}layer {name: "data"type: "Data"top: "data"top: "label"include {phase: TEST}transform_param {mirror: falsecrop_size: 227mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"}# mean pixel / channel-wise mean instead of mean image# transform_param {# crop_size: 227# mean_value: 104# mean_value: 117# mean_value: 123# mirror: false# }data_param {source: "examples/imagenet/ilsvrc12_val_lmdb"batch_size: 50backend: LMDB}}

用这样的代替

layer {  name: "data"  type: "Input"  top: "data"  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }}

shape: { dim: 10 dim: 3 dim: 227 dim: 227 }中第一个dim代表卷积核个数10个，第二个dim代表channel个数3个（对于图片是RGB三通道），第三第四个分别是width和height图片的。
2：卷积层中对应参数：

**weight_filler {type: "gaussian"std: 0.01}bias_filler {type: "constant"value: 1}**

完全删除，可以用关键字weight_filler和bias_filler来删除，他们是指定学习的参数的，deploy时用model中的参数，而不用自己更新，故删去。
3：最后的

layer {name: "accuracy"type: "Accuracy"bottom: "fc8"bottom: "label"top: "accuracy"include {phase: TEST}

完全删除，不需要test
4：原来的最后一层loss层改为pro层

layer {name: "loss"type: "SoftmaxWithLoss"bottom: "fc8"bottom: "label"top: "loss"}

A:将其中的SoftmaxWithLoss替换为Softmax
B:删除其中的bottom:”label”行，因为测试时需要预测label而不是给你一个已知label。
C:同时将最后一层loss层改为pro层
因此改完之后最后一层就是这样的：

layer {  name: "prob"  type: "Softmax"  bottom: "fc8"  top: "prob"}

这里的name: “prob”就是你在用python预测时读取的layer的name，一定要对应上