discriminator.prototxt

force_backward: true
input: "data"
input_shape {
  dim: 64
  dim: 3
  dim: 64
  dim: 64
}

input: "label"
input_shape {
  dim: 64
  dim: 1
}


# Discriminator net

layer {
  name: "Dconv1"
  type: "Convolution"
  bottom: "data"
  top: "Dconv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 4
    stride: 2
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.02
    }
    bias_filler {
      type: "constant"
      value: 0
    }
#    engine: CUDNN
  }
}
layer {
  name: "Drelu1"
  type: "ReLU"
  bottom: "Dconv1"
  top: "Dconv1"
  relu_param {
    negative_slope: 0.2
  }

}

layer {
  name: "Dconv2"
  type: "Convolution"
  bottom: "Dconv1"
  top: "Dconv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 4
    stride: 2
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.02
    }
    bias_filler {
      type: "constant"
      value: 0
    }
#    engine: CUDNN
  }
}
layer {
  name: "Dconv2_BN"
  type: "BatchNorm" include { phase: TRAIN}
  bottom: "Dconv2"
  top: "Dconv2_BN"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: false
    moving_average_fraction: 0.95
  }
}
layer {
  name: "Dconv2_BN"
  type: "BatchNorm" include { phase: TEST}
  bottom: "Dconv2"
  top: "Dconv2_BN"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.95
  }
}

layer {
  name: "Drelu2"
  type: "ReLU"
  bottom: "Dconv2_BN"
  top: "Dconv2_BN"
  relu_param {
    negative_slope: 0.2
  }

}
layer {
  name: "Dconv3"
  type: "Convolution"
  bottom: "Dconv2_BN"
  top: "Dconv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 4
    stride: 2
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.02
    }
    bias_filler {
      type: "constant"
      value: 0
    }
#    engine: CUDNN
  }
}
layer {
  name: "Dconv3_BN"
  type: "BatchNorm" include { phase: TRAIN}
  bottom: "Dconv3"
  top: "Dconv3_BN"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: false
    moving_average_fraction: 0.95
  }
}
layer {
  name: "Dconv3_BN"
  type: "BatchNorm" include { phase: TEST}
  bottom: "Dconv3"
  top: "Dconv3_BN"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.95
  }
}

layer {
  name: "Drelu3"
  type: "ReLU"
  bottom: "Dconv3_BN"
  top: "Dconv3_BN"
  relu_param {
    negative_slope: 0.2
  }

}
layer {
  name: "Dconv4"
  type: "Convolution"
  bottom: "Dconv3_BN"
  top: "Dconv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 4
    stride: 2
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
#    engine: CUDNN
  }
}
layer {
  name: "Dconv4_BN"
  type: "BatchNorm" include { phase: TRAIN}
  bottom: "Dconv4"
  top: "Dconv4_BN"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: false
    moving_average_fraction: 0.95
  }
}
layer {
  name: "Dconv4_BN"
  type: "BatchNorm" include { phase: TEST}
  bottom: "Dconv4"
  top: "Dconv4_BN"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    moving_average_fraction: 0.95
  }
}

layer {
  name: "Drelu4"
  type: "ReLU"
  bottom: "Dconv4_BN"
  top: "Dconv4_BN"
  relu_param {
    negative_slope: 0.2
  }

}
layer {
  name: "Dconv5"
  type: "Convolution"
  bottom: "Dconv4_BN"
  top: "Dconv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 4
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.02
    }
    bias_filler {
      type: "constant"
      value: 0
    }
#    engine: CUDNN
  }
}
#layer { 
#  name: "relu5"
#  type: "ReLU"
#  bottom: "Dconv5"
#  top: "Dconv5"
#}
#
#layer {
#  name: "Dpool5"
#  type: "Pooling"
#  bottom: "Dconv5"
#  top: "Dpool5"
#  pooling_param {
#    pool: AVE
#    kernel_size: 11
#    stride: 11
#  }
#}

layer {
  name: "Dfc7"
  type: "InnerProduct"
  bottom: "Dconv5"
  top: "Dfc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}


layer {
  name: "discr_loss"
  type: "SigmoidCrossEntropyLoss"
  bottom: "Dfc7"
  bottom: "label"
  top: "discr_loss"
  loss_weight: 1
#   loss_weight: 1
}