Windowscaffe跑mnist实例

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_leveldb"
    batch_size: 64
    backend: LEVELDB
  }
}
layer {
  name: "mnist"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    scale: 0.00390625
  }
  data_param {
    source: "examples/mnist/mnist_test_leveldb"
    batch_size: 100
    backend: LEVELDB
  }
}
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"
}

 1 # The train/test net protocol buffer definition
 2 net: "examples/mnist/lenet_train_test.prototxt"
 3 # test_iter specifies how many forward passes the test should carry out.
 4 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 5 # covering the full 10,000 testing images.
 6 test_iter: 100
 7 # Carry out testing every 500 training iterations.
 8 test_interval: 500
 9 # The base learning rate, momentum and the weight decay of the network.
10 base_lr: 0.01
11 momentum: 0.9
12 weight_decay: 0.0005
13 # The learning rate policy
14 lr_policy: "inv"
15 gamma: 0.0001
16 power: 0.75
17 # Display every 100 iterations
18 display: 100
19 # The maximum number of iterations
20 max_iter: 10000
21 # snapshot intermediate results
22 snapshot: 5000
23 snapshot_prefix: "examples/mnist/lenet"
24 # solver mode: CPU or GPU
25 solver_mode: CPU

cd ../../
"Build/x64/Debug/caffe.exe" train --solver=examples/mnist/lenet_solver.prototxt 
pause 

cd ../../
"Build/x64/Debug/caffe.exe" test --model=examples/mnist/lenet_train_test.prototxt -weights=examples/mnist/lenet_iter_10000.caffemodel
pause