Caffe CPU训练模型

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运行环境 Ubuntu14.04.4

MNIST数据集是大型的手写数字数据库，60000个训练集和10000个测试集。

一、下载MNIST数据集

cd /home/***/caffe/data/mnist
./get_mnist.sh

二、格式转换

下载的原始数据集为二进制文件，需要转化成LEVELDB或LMDB才能被Caffe识别。

cd /home/***/caffe/example/mnist
./create_mnist.sh

如果没有修改create_mnist.sh文件的话，会出现下面的情况：

说明路径出现问题，要对create_mnist.sh做如下修改：

更改EXAMPLE,DATA,BUILD为绝对路径

然后再次运行./create_mnist.sh，得到结果：

说明已经改好了，浏览所在的目录 example/mnist，发现生成了mnist_train_lmdb/和mnist_test_lmdb两个目录，每个目录下都有两个文件：data.mdb和lock.mdb。接下来可以训练

三、训练

./train_lenet.sh

直接在终端输入会出现下面的情况：无法找到路径。

对train_lenet.sh做如下的修改：修改路径为绝对路径

再次输入命令进行训练得到如下结果：

发现问题，还是出现路径的问题，这次修改lenet_solver.prototxt文件

特别注意最后一行的CPU/GPU模式

# The train/test net protocol buffer definition
net: "/home/neutech/caffe/examples/mnist/lenet_train_test.prototxt"    #修改为绝对路径
# test_iter specifies how many forward passes the test should carry out.
# In the case of MNIST, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
test_iter: 100
# Carry out testing every 500 training iterations.
test_interval: 500
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.01
momentum: 0.9
weight_decay: 0.0005
# The learning rate policy
lr_policy: "inv"
gamma: 0.0001
power: 0.75
# Display every 100 iterations
display: 100
# The maximum number of iterations
max_iter: 10000
# snapshot intermediate results
snapshot: 5000
snapshot_prefix: "/home/neutech/caffe/examples/mnist/lenet"                #修改为绝对路径
# solver mode: CPU or GPU
solver_mode: CPU                                                           #如果为CPU模式改成CPU，默认为GPU

再次运行命令开始训练，出现下面的情况：

还是训练失败，同样的问题，找不到mnist_train_lmdb和mnist_train_lmdb文件的路径。通过查看lenet_solver.prototxt的内容

net: "/home/neutech/caffe/examples/mnist/lenet_train_test.prototxt"    #修改为绝对路径

这一行描述的用于训练/预测的网络描述文件（ProtoBuffer文件格式），打开lenet_train_test.prototxt文件，修改：

name: "LeNet"
layer {
  name: "mnist"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    scale: 0.00390625
  }
  data_param {
    source: "/home/neutech/caffe/examples/mnist/mnist_train_lmdb"      #此处文件路径要检查清楚，修改为绝对路径
    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: "/home/neutech/caffe/examples/mnist/mnist_test_lmdb"      #此处文件路径要检查清楚，修改为绝对路径
    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"
}

接下来可以进行训练了。会出现下面的训练日志：

OK，大功告成！

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