imagenet.prototxt~

# This file is for the net_surgery.ipynb example notebook.
name: "CaffeNetConv"
layers {
  name: "data"
  type: HDF5_DATA
  top: "data"
  top: "label"
  hdf5_data_param {
    source: "/home/ecater/Desktop/caffe/examples/science_bowl/data/aug_train.txt"
    batch_size: 128
  }
  include: { phase: TRAIN }
}
layers {
  name: "data"
  type: HDF5_DATA
  top: "data"
  top: "label"
  hdf5_data_param {
    source: "/home/ecater/Desktop/caffe/examples/science_bowl/data/aug_test.txt"
    batch_size: 128
  }
  include: { phase: TEST }
}
layers {
  name: "conv1"
  type: CONVOLUTION
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 96
    kernel_size: 5
    stride: 4
  }
}
layers {
  name: "relu1"
  type: RELU
  bottom: "conv1"
  top: "conv1"
}
layers {
  name: "pool1"
  type: POOLING
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "norm1"
  type: LRN
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 3
    alpha: 0.0001
    beta: 0.75
  }
}
layers {
  name: "conv2"
  type: CONVOLUTION
  bottom: "norm1"
  top: "conv2"
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 3
    group: 2
  }
}
layers {
  name: "relu2"
  type: RELU
  bottom: "conv2"
  top: "conv2"
}
layers {
  name: "pool2"
  type: POOLING
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "norm2"
  type: LRN
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 3
    alpha: 0.0001
    beta: 0.75
  }
}
layers {
  name: "conv3"
  type: CONVOLUTION
  bottom: "norm2"
  top: "conv3"
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
  }
}
layers {
  name: "relu3"
  type: RELU
  bottom: "conv3"
  top: "conv3"
}
layers {
  name: "conv4"
  type: CONVOLUTION
  bottom: "conv3"
  top: "conv4"
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
  }
}
layers {
  name: "relu4"
  type: RELU
  bottom: "conv4"
  top: "conv4"
}
layers {
  name: "conv5"
  type: CONVOLUTION
  bottom: "conv3"
  top: "conv5"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
  }
}
layers {
  name: "relu5"
  type: RELU
  bottom: "conv5"
  top: "conv5"
}
layers {
  name: "pool5"
  type: POOLING
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "fc6-conv"
  type: CONVOLUTION
  bottom: "pool5"
  top: "fc6-conv"
  convolution_param {
    num_output: 4096
    kernel_size: 1
  }
}
layers {
  name: "relu6"
  type: RELU
  bottom: "fc6-conv"
  top: "fc6-conv"
}
layers {
  name: "drop6"
  type: DROPOUT
  bottom: "fc6-conv"
  top: "fc6-conv"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layers {
  name: "fc7-conv"
  type: CONVOLUTION
  bottom: "fc6-conv"
  top: "fc7-conv"
  convolution_param {
    num_output: 4096
    kernel_size: 1
  }
}
layers {
  name: "relu7"
  type: RELU
  bottom: "fc7-conv"
  top: "fc7-conv"
}
layers {
  name: "drop7"
  type: DROPOUT
  bottom: "fc7-conv"
  top: "fc7-conv"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layers {
  name: "fc8-conv"
  type: CONVOLUTION
  bottom: "fc7-conv"
  top: "fc8-conv"
  convolution_param {
    num_output: 121
    kernel_size: 1
  }
}
layers {
  name: "accuracy"
  type: ACCURACY
  bottom: "fc8-conv"
  bottom: "label"
  top: "accuracy"
  include: { phase: TEST }
}
layers {
  name: "prob"
  type: SOFTMAX
  bottom: "fc8-conv"
  top: "prob"
}