cnn_train.prototxt

name: "CNN"
layers {
  name: "cifar"
  type: DATA
  top: "data"
  top: "label"
  data_param {
	# path to the DB
    source: "train_lmdb"
	# type of DB: LEVELDB or LMDB (LMDB supports concurrent reads)
    backend: LMDB
    # batch processing improves efficiency.
    # batch_size: 128
    batch_size: 1
	# batch_size: 10
  }
  transform_param {
    mirror: true
    # crop a `crop_size` x `crop_size` patch:
    # - at random during training
    # - from the center during testing
    #crop_size: 256
    mean_file: "/home/ubuntu/faceIdentityDataset/trainImageMean.binaryproto"
  }
# mean pixel / channel-wise mean instead of mean image
#  transform_param {
#    crop_size: 227
#    mean_value: 104
#    mean_value: 117
#    mean_value: 123
#    mirror: true
#  }
  include: { phase: TRAIN }
}
layers {
  name: "cifar"
  type: DATA
  top: "data"
  top: "label"
  data_param {
    source: "test_lmdb"
    backend: LMDB
    batch_size: 1
	# batch_size: 5
  }
  transform_param {
    mean_file: "/home/ubuntu/faceIdentityDataset/trainImageMean.binaryproto"
  }
  include: { phase: TEST }
}
layers {
  name: "conv1"
  type: CONVOLUTION
  bottom: "data"
  top: "conv1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1.
  weight_decay: 0.
  convolution_param {
    num_output: 192
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
	  value: 0.1
    }
  }
}
layers {
  name: "relu1"
  type: RELU
  bottom: "conv1"
  top: "conv1"
}
layers {
  name: "cccp1"
  type: CONVOLUTION
  bottom: "conv1"
  top: "cccp1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 160
    group: 1
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu_cccp1"
  type: RELU
  bottom: "cccp1"
  top: "cccp1"
}
layers {
  name: "cccp2"
  type: CONVOLUTION
  bottom: "cccp1"
  top: "cccp2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 96
    group: 1
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu_cccp2"
  type: RELU
  bottom: "cccp2"
  top: "cccp2"
}
layers {
  name: "pool1"
  type: POOLING
  bottom: "cccp2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "drop3"
  type: DROPOUT
  bottom: "pool1"
  top: "pool1"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layers {
  name: "conv2"
  type: CONVOLUTION
  bottom: "pool1"
  top: "conv2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1.
  weight_decay: 0.
  convolution_param {
    num_output: 192
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
	  value: 0.1
    }
  }
}
layers {
  name: "relu2"
  type: RELU
  bottom: "conv2"
  top: "conv2"
}
layers {
  name: "cccp3"
  type: CONVOLUTION
  bottom: "conv2"
  top: "cccp3"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 192
    group: 1
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu_cccp3"
  type: RELU
  bottom: "cccp3"
  top: "cccp3"
}
layers {
  name: "cccp4"
  type: CONVOLUTION
  bottom: "cccp3"
  top: "cccp4"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 192
    group: 1
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu_cccp4"
  type: RELU
  bottom: "cccp4"
  top: "cccp4"
}
layers {
  name: "pool2"
  type: POOLING
  bottom: "cccp4"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "drop6"
  type: DROPOUT
  bottom: "pool2"
  top: "pool2"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layers {
  name: "conv3"
  type: CONVOLUTION
  bottom: "pool2"
  top: "conv3"
  blobs_lr: 1.
  blobs_lr: 2.
  weight_decay: 1.
  weight_decay: 0.
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
	  value: 0.1
    }
  }
}
layers {
  name: "relu3"
  type: RELU
  bottom: "conv3"
  top: "conv3"
}
layers {
  name: "cccp5"
  type: CONVOLUTION
  bottom: "conv3"
  top: "cccp5"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 192
    group: 1
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu_cccp5"
  type: RELU
  bottom: "cccp5"
  top: "cccp5"
}
layers {
  name: "cccp6"
  type: CONVOLUTION
  bottom: "cccp5"
  top: "cccp6"
  blobs_lr: 0.1
  blobs_lr: 0.1
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 192
    group: 1
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu_cccp6"
  type: RELU
  bottom: "cccp6"
  top: "cccp6"
}
layers {
  name: "pool3"
  type: POOLING
  bottom: "cccp6"
  top: "pool3"
  pooling_param {
    pool: AVE
    kernel_size: 8
    stride: 1
  }
}

# Sarab: Changed above conv output to same as that of conv5 = 192
# Adding innerproduct as last layer to produce class score. Else last pooling layer produces.. image with extracted features..
# Else SoftMax layer was giving error that number labels/class scores do not match input from pool layer.which was batch_size * 9 * 9
# Below we converted to new proto syntax
# http://stackoverflow.com/questions/31008493/caffe-drawing-cnn-net

layers {
  name: "ip1"
  type: INNER_PRODUCT
  bottom: "pool3"
  top: "ip1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 5
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}


layers {
  name: "accuracy"
  type: ACCURACY
  bottom: "ip1"
  bottom: "label"
  top: "accuracy"
  include: { phase: TEST }
}
layers {
  name: "loss"
  type: SOFTMAX_LOSS
  bottom: "ip1"
  bottom: "label"
  top: "loss"
}