CAFFE CIFAR10 MODEL IMAGE 之 cifar10 full sigmoid

来源：互联网发布：移动硬盘盒知乎编辑：程序博客网时间：2024/04/30 02:06
cifar10 添加sigmoid层后的拓扑文件为：
name: "CIFAR10_full"layer {  name: "cifar"  type: "Data"  top: "data"  top: "label"  include {    phase: TRAIN  }  transform_param {    mean_file: "examples/cifar10/mean.binaryproto"  }  data_param {    source: "examples/cifar10/cifar10_train_lmdb"    batch_size: 111    backend: LMDB  }}layer {  name: "cifar"  type: "Data"  top: "data"  top: "label"  include {    phase: TEST  }  transform_param {    mean_file: "examples/cifar10/mean.binaryproto"  }  data_param {    source: "examples/cifar10/cifar10_test_lmdb"    batch_size: 1000    backend: LMDB  }}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param {    num_output: 32    pad: 2    kernel_size: 5    stride: 1    weight_filler {      type: "gaussian"      std: 0.0001    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool1"  type: "Pooling"  bottom: "conv1"  top: "pool1"  pooling_param {    pool: MAX    kernel_size: 3    stride: 2  }}layer {  name: "Sigmoid1"  type: "Sigmoid"  bottom: "pool1"  top: "Sigmoid1"}layer {  name: "conv2"  type: "Convolution"  bottom: "Sigmoid1"  top: "conv2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param {    num_output: 32    pad: 2    kernel_size: 5    stride: 1    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"    }  }}layer {  name: "Sigmoid2"  type: "Sigmoid"  bottom: "conv2"  top: "Sigmoid2"}layer {  name: "pool2"  type: "Pooling"  bottom: "Sigmoid2"  top: "pool2"  pooling_param {    pool: AVE    kernel_size: 3    stride: 2  }}layer {  name: "conv3"  type: "Convolution"  bottom: "pool2"  top: "conv3"  convolution_param {    num_output: 64    pad: 2    kernel_size: 5    stride: 1    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"    }  }  param {    lr_mult: 1  }  param {    lr_mult: 1  }}layer {  name: "Sigmoid3"  type: "Sigmoid"  bottom: "conv3"  top: "Sigmoid3"}layer {  name: "pool3"  type: "Pooling"  bottom: "Sigmoid3"  top: "pool3"  pooling_param {    pool: AVE    kernel_size: 3    stride: 2  }}layer {  name: "ip1"  type: "InnerProduct"  bottom: "pool3"  top: "ip1"  param {    lr_mult: 1    decay_mult: 0  }  param {    lr_mult: 2    decay_mult: 0  }  inner_product_param {    num_output: 10    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"    }  }}layer {  name: "accuracy"  type: "Accuracy"  bottom: "ip1"  bottom: "label"  top: "accuracy"  include {    phase: TEST  }}layer {  name: "loss"  type: "SoftmaxWithLoss"  bottom: "ip1"  bottom: "label"  top: "loss"}
拓扑图就变为：
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