lenetlight

name: "LeNetSimplifie2"
layer {
  name: "data"
  type: "MemoryData"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    scale: 0.00390625
  }
  memory_data_param 
  {
    batch_size: 50 
    channels: 1 
    height: 28
    width: 28 
  }
}
layer {
  name: "test_inputdata"
  type: "MemoryData"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    scale: 0.00390625
  }
  memory_data_param 
  {
    batch_size: 50
    channels: 1 
    height: 28
    width: 28 
  }
}
layer {
  name: "conv1a"
  type: "Convolution"
  bottom: "data"
  top: "conv1a"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 6
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1a"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "ip1a"
  type: "InnerProduct"
  bottom: "pool1"
  top: "ip1a"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 120
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "sigmoid"
  type: "Sigmoid"
  bottom: "ip1a"
  top: "sigmoid"
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "sigmoid"
  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"
}