numbers_net.autoencoder.prototxt

name: "digit auto encoder"
layers {
  name: "alpha"
  type: IMAGE_DATA
  top: "data"
  top: "label"
  image_data_param {
    source: "train_index_256x256.txt"
    batch_size: 32 #32 #128
    is_color: false
  }
  transform_param {
    scale: 0.00390625 # uint [0..255]*(1/256.0) -> [0..1]float
    mirror: 0  # 1 = on, 0 = off
    # crop a `crop_size` x `crop_size` patch:
    # - at random during training
    # - from the center during testing
    #crop_size: 512
  }
  include: { phase: TRAIN }
}
layers {
  name: "alpha"
  type: IMAGE_DATA
  top: "data"
  top: "label"
  image_data_param {
    source: "test_index_256x256.txt"
    batch_size: 1 
    is_color: false
  }
  transform_param {
    scale: 0.00390625
  }
  include: { phase: TEST }
}

layers {
  bottom: "data"
  top: "encode1"
  name: "encode1"
  type: INNER_PRODUCT
  blobs_lr: 1
  blobs_lr: 1
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 1000
    weight_filler {
      type: "xavier"
      std: 1
#      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  bottom: "encode1"
  top: "encode1neuron"
  name: "encode1neuron"
  type: SIGMOID
}
layers {
  bottom: "encode1neuron"
  top: "encode2"
  name: "encode2"
  type: INNER_PRODUCT
  blobs_lr: 1
  blobs_lr: 1
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
      std: 1
#      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  bottom: "encode2"
  top: "encode2neuron"
  name: "encode2neuron"
  type: SIGMOID
}
layers {
  bottom: "encode2neuron"
  top: "encode3"
  name: "encode3"
  type: INNER_PRODUCT
  blobs_lr: 1
  blobs_lr: 1
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 250
    weight_filler {
      type: "xavier"
      std: 1
#      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  bottom: "encode3"
  top: "encode3neuron"
  name: "encode3neuron"
  type: SIGMOID
}
layers {
  bottom: "encode3neuron"
  top: "encode4"
  name: "encode4"
  type: INNER_PRODUCT
  blobs_lr: 1
  blobs_lr: 1
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 30
    weight_filler {
      type: "xavier"
      std: 1
#      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  bottom: "encode4"
  top: "decode4"
  name: "decode4"
  type: INNER_PRODUCT
  blobs_lr: 1
  blobs_lr: 1
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 250
    weight_filler {
      type: "xavier"
      std: 1
#      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  bottom: "decode4"
  top: "decode4neuron"
  name: "decode4neuron"
  type: SIGMOID
}
layers {
  bottom: "decode4neuron"
  top: "decode3"
  name: "decode3"
  type: INNER_PRODUCT
  blobs_lr: 1
  blobs_lr: 1
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  bottom: "decode3"
  top: "decode3neuron"
  name: "decode3neuron"
  type: SIGMOID
}
layers {
  bottom: "decode3neuron"
  top: "decode2"
  name: "decode2"
  type: INNER_PRODUCT
  blobs_lr: 1
  blobs_lr: 1
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 1000
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  bottom: "decode2"
  top: "decode2neuron"
  name: "decode2neuron"
  type: SIGMOID
}
layers {
  bottom: "decode2neuron"
  top: "decode1"
  name: "decode1"
  type: INNER_PRODUCT
  blobs_lr: 1
  blobs_lr: 1
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 65536 
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layers {
  name: "ip2"
  type: INNER_PRODUCT
  bottom: "encode4"
  top: "ip2"
  blobs_lr: 1
  blobs_lr: 2
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "accuracy"
  type: ACCURACY
  bottom: "ip2"
  bottom: "label"
  top: "accuracy"
#  include: { phase: TEST }
}

#layers {
#  name: "loss"
#  type: SOFTMAX_LOSS
#  bottom: "ip2"
#  bottom: "label"
#  top: "loss"
#  loss_weight: 1
#}


layers {
  bottom: "data"
  top: "flatdata"
  name: "flatdata"
  type: FLATTEN
}
layers {
  bottom: "decode1"
  bottom: "flatdata"
  top: "cross_entropy_loss"
  name: "loss"
  type: SIGMOID_CROSS_ENTROPY_LOSS
  loss_weight: 0.1
}
layers {
  bottom: "decode1"
  top: "decode1neuron"
  name: "decode1neuron"
  type: SIGMOID
}
layers {
  bottom: "decode1neuron"
  bottom: "flatdata"
  top: "l2_error"
  name: "loss"
  type: EUCLIDEAN_LOSS
  loss_weight: 1 
}