caffe中mnist中 lenet_train_test.prototxt和lenet.prototxt（deploy文件）区别

参照 http://blog.csdn.net/cham_3/article/details/52682479
http://blog.csdn.net/l18930738887/article/details/54898016
跑了下mnist训练，和使用训练好的模型进行预测

两个文件都在examples/mnist 中, lenet_train_test.prototxt 文件是设置train、test的网络，deploy文件是分类测试加载的文件。

大体区别思路是网络配置文件会规定的详细一些，deploy文件只是告诉分类程序（mnist_classification.bin）网络结构是怎么样的，不需要反向计算，不需要计算误差

两个文件内容先发出来：

cat lenet_train_test.prototxt 

name: "LeNet"
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/mnist/mnist_train_lmdb"
batch_size: 64
backend: LMDB
}
}
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/mnist/mnist_test_lmdb"
batch_size: 100
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
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"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
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: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "ip2"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}

cat lenet.prototxt   

name: "LeNet"
layer {
name: "data"
type: "Input"
top: "data"
input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
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"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
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: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "ip2"
top: "prob"
}

区别：

1.首先删除TEST使用的部分，例如开始的输入数据test部分，

layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/mnist/mnist_test_lmdb"
batch_size: 100
backend: LMDB
}
}

最后的test精度部分

layer {

  name: "accuracy"

  type: "Accuracy"

  bottom: "ip2"

  bottom: "label"

  top: "accuracy"

  include {

    phase: TEST

  }

}

2. trian部分的输入数据部分修改，只需要告诉输入维度

layer {

  name: "mnist"

  type: "Data"

  top: "data"

  top: "label"

  include {

    phase: TRAIN

  }

  transform_param {

    scale: 0.00390625

  }

  data_param {

    source: "examples/mnist/mnist_train_lmdb"

    batch_size: 64

    backend: LMDB

  }

}

改为

layer {

  name: "data"

  type: "Input"

  top: "data"

  input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }

}

shape: { dim: 64 dim: 1 dim: 28 dim: 28 }中第一个dim代表？，第二个dim代表channel个数1个（对于图片是RGB的，3通道就是3），第三第四个分别是width和height图片的。 

3. 原来的最后一层loss层改为pro层

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

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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"
}

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