name: "vanila"

layer {
   type: "HDF5Data"
   top: "X" # same name as given in create_dataset!
   top: "landmarks"
   hdf5_data_param {
     source: "/Users/ishay/Dev/VanilaCNN/caffeData/train.txt" # do not give the h5 files directly, but the list.
     batch_size: 30
   }
   include { phase:TRAIN }
 }

layer {
   type: "HDF5Data"
   top: "X" # same name as given in create_dataset!
   top: "landmarks"
   hdf5_data_param {
     source: "/Users/ishay/Dev/VanilaCNN/caffeData/test.txt" # do not give the h5 files directly, but the list.
     batch_size: 10
   }
   include { phase:TEST }
}

layer {
    name: "Conv1"
    type: "Convolution"
    bottom: "X"
    top: "Conv1"
    param {
        lr_mult: 1
        decay_mult: 1
    }
    param {
        lr_mult: 2
        decay_mult: 0
    }
    convolution_param {
        num_output: 16
        pad: 2
        kernel_size: 5
        stride: 1
        weight_filler {
            type: "xavier"
            std: 0.1
        }
        bias_filler {
            type: "constant"
            value: 0.2
        }
    }
}

layer {
  name: "ActivationTangH1"
  bottom: "Conv1"
  top: "ActivationTangH1"
  type: "TanH"
}

layer {
  name: "ActivationAbs1"
  bottom: "ActivationTangH1"
  top: "Abs1"
  type: "AbsVal"
}

layer {
    name: "Pool1"
    type: "Pooling"
    bottom: "Abs1"
    top: "Pool1"
    pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
    }
}

layer {
    name: "Conv2"
    type: "Convolution"
    bottom: "Pool1"
    top: "Conv2"
    param {
        lr_mult: 1
        decay_mult: 1
    }
    param {
        lr_mult: 2
        decay_mult: 0
    }
    convolution_param {
        num_output: 48
        pad: 1
        kernel_size: 3
        stride: 1
        weight_filler {
        type: "xavier"
        std: 0.1
    }
    bias_filler {
        type: "constant"
        value: 0.2
        }
    }
}

layer {
  name: "ActivationTangH2"
  bottom: "Conv2"
  top: "ActivationTangH2"
  type: "TanH"
}

layer {
  name: "ActivationAbs2"
  bottom: "ActivationTangH2"
  top: "Abs2"
  type: "AbsVal"
}


layer {
    name: "Pool2"
    type: "Pooling"
    bottom: "Abs2"
    top: "Pool2"
    pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
    }
}

# layer 3
layer {
    name: "Conv3"
    type: "Convolution"
    bottom: "Pool2"
    top: "Conv3"
    param {
        lr_mult: 1
        decay_mult: 1
    }
    param {
        lr_mult: 2
        decay_mult: 0
    }
    convolution_param {
        num_output: 64
        pad: 0
        kernel_size: 3
        stride: 1
        weight_filler {
            type: "xavier"
            std: 0.1
        }
        bias_filler {
            type: "constant"
            value: 0.2
        }
    }
}


layer {
  name: "ActivationTangH3"
  bottom: "Conv3"
  top: "ActivationTangH3"
  type: "TanH"
}

layer {
  name: "ActivationAbs3"
  bottom: "ActivationTangH3"
  top: "Abs3"
  type: "AbsVal"
}

layer {
    name: "Pool3"
    type: "Pooling"
    bottom: "Abs3"
    top: "Pool3"
    pooling_param {
        pool: MAX
        kernel_size: 3
        stride: 2
    }
}

# layer 4
layer {
    name: "Conv4"
    type: "Convolution"
    bottom: "Pool3"
    top: "Conv4"
    param {
        lr_mult: 1
        decay_mult: 1
    }
    param {
        lr_mult: 2
        decay_mult: 0
    }
    convolution_param {
        num_output: 64
        pad: 0
        kernel_size: 2
        stride: 1
        weight_filler {
            type: "xavier"
            std: 0.1
        }
        bias_filler {
            type: "constant"
            value: 0.2
        }
    }
}


layer {
  name: "ActivationTangH4"
  bottom: "Conv4"
  top: "ActivationTangH4"
  type: "TanH"
}

layer {
  name: "ActivationAbs4"
  bottom: "ActivationTangH4"
  top: "Abs4"
  type: "AbsVal"
}


########################################

layer {
    name: "Dense1"
    type: "InnerProduct"
    bottom: "Abs4"
    top: "Dense1"
    param {
        lr_mult: 1
        decay_mult: 1
    }
    param {
        lr_mult: 2
        decay_mult: 0
    }
    inner_product_param {
        num_output: 100
        weight_filler {
            type: "xavier"
        }
        bias_filler {
            type: "constant"
            value: 0
        }
    }
}


layer {
  name: "ActivationTangH5"
  bottom: "Dense1"
  top: "ActivationTangH5"
  type: "TanH"
}

layer {
  name: "ActivationAbs5"
  bottom: "ActivationTangH5"
  top: "Abs5"
  type: "AbsVal"
}


layer {
    name: "Dense2"
    type: "InnerProduct"
    bottom: "Abs5"
    top: "Dense2"
    param {
        lr_mult: 1
        decay_mult: 1
    }
    param {
        lr_mult: 2
        decay_mult: 0
    }
    inner_product_param {
        num_output: 10
        weight_filler {
            type: "xavier"
        }
        bias_filler {
            type: "constant"
            value: 0
        }
    }
}

layer {
  type: 'Python'
  name: 'loss'
  top: 'loss'
  bottom: 'Dense2'
  bottom: 'landmarks'
  python_param {
    # the module name -- usually the filename -- that needs to be in $PYTHONPATH
    module: 'python.NormlizedMSE'
    # the layer name -- the class name in the module
    layer: 'NormlizedMSE'
  }
  # set loss weight so Caffe knows this is a loss layer.
  # since PythonLayer inherits directly from Layer, this isn't automatically
  # known to Caffe
  loss_weight: 1
}