##### Solver #####

solver_mode: GPU

base_lr: 0.01
momentum: 0.9
weight_decay: 0.0002

lr_policy: "step"
gamma: 0.9
stepsize: 5000

max_iter: 250000

snapshot: 1000
snapshot_prefix: "path/to/models"

test_iter: 1000
test_interval: 1000

display: 100
average_loss: 100

##### Network #####

net_param: {

    name: "model"

    ##### Training data #####

    layer {
      name: "images"
      type: "Data"
      top: "data"
      top: "label"
      transform_param {        
        mirror: true   
        mean_value: 150
      }
      data_param {
        source: "path/to/training/db"
        backend: LMDB
        batch_size: 64
      }
      include: { phase: TRAIN }
    }

    ##### Testing data #####

    layer {
      name: "images"
      type: "Data"
      top: "data"
      top: "label"
      transform_param {        
        mean_value: 150
      }
      data_param {
        source: "path/to/testing/db"
        backend: LMDB
        batch_size: 128
      }   
      include: { phase: TEST }
    }

    ##### Layer 1 #####

    layer {
      name: "conv1"
      type: "Convolution"
      bottom: "data"
      top: "conv1"
      param {
        lr_mult: 1
        decay_mult: 1
      }
      param {
        lr_mult: 2
        decay_mult: 0
      }
      convolution_param {
        num_output: 16
        kernel_size: 4
        stride: 1
        pad: 0
        weight_filler {
	  type: "gaussian" std: 0.07
        }
        bias_filler {
          type: "constant" value: 0.1
        }
      }
    }

    layer {
      name: "nonlin1"
      type: "ReLU"
      bottom: "conv1"
      top: "conv1"
    }

    layer {
      name: "pool1"
      type: "Pooling"
      bottom: "conv1"
      top: "pool1"
      pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
      }
    }

    ##### Layer 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: 16
        kernel_size: 3
        stride: 1
        pad: 0
        weight_filler {
	  type: "gaussian" std: 0.04
        }
        bias_filler {
          type: "constant" value: 0.1
        }
      }
    }

    layer {
      name: "nonlin2"
      type: "ReLU"
      bottom: "conv2"
      top: "conv2"
    }

    layer {
      name: "pool2"
      type: "Pooling"
      bottom: "conv2"
      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: 16
        kernel_size: 3
        stride: 1
        pad: 0
        weight_filler {
          type: "gaussian" std: 0.04
        }
        bias_filler {
          type: "constant" value: 0.1
        }
      }
    }

    layer {
      name: "nonlin3"
      type: "ReLU"
      bottom: "conv3"
      top: "conv3"
    }
    
    layer {
      name: "pool3"
      type: "Pooling"
      bottom: "conv3"
      top: "pool3"
      pooling_param {
        pool: MAX
        kernel_size: 2
        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: 16
        kernel_size: 3
        stride: 1
        pad: 0
        weight_filler {
          type: "gaussian" std: 0.04
        }
        bias_filler {
          type: "constant" value: 0.1       
        }
      }
    }

    layer {
      name: "nonlin4"
      type: "ReLU"
      bottom: "conv4"
      top: "conv4"
    }
    
    layer {
      name: "pool4"
      type: "Pooling"
      bottom: "conv4"
      top: "pool4"
      pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
      }
    }

    ##### Fully connected 1 #####

    layer {
      name: "ip1"
      type: "InnerProduct"
      bottom: "pool4"
      top: "ip1"
      param {
        lr_mult: 1
        decay_mult: 1
      }
      param {
        lr_mult: 2
        decay_mult: 0
      }
      inner_product_param {
        num_output: 200
        weight_filler {
          type: "gaussian" std: 0.03
        }
        bias_filler {
          type: "constant" value: 0.1
        }
      }
    }

    layer {
      name: "nonlin_ip1"
      type: "ReLU"
      bottom: "ip1"
      top: "ip1"
    }

    ##### Fully connected 2 (output layer) #####

    layer {
      name: "ip2"
      type: "InnerProduct"
      bottom: "ip1"
      top: "ip2"
      param {
        lr_mult: 1
        decay_mult: 1
      }
      param {
        lr_mult: 2
        decay_mult: 0
      }
      inner_product_param {
        num_output: 2
        weight_filler {
          type: "gaussian" std: 0.06
        }
        bias_filler {
          type: "constant"
        }
      }
    }

    ##### Loss #####

    layer {
      name: "loss"
      type: "SoftmaxWithLoss"
      bottom: "ip2"
      bottom: "label"
      top: "loss"
    }

    ##### Test accuracy #####

    layer {
      name: "accuracy"
      type: "Accuracy"
      bottom: "ip2"
      bottom: "label"
      top: "accuracy"
      include: { phase: TEST }
    }

}