mnist_autoencoder.prototxt

name: "MNISTAutoencoder"
layers {
  top: "data"
  name: "data"
  type: DATA
  data_param {
    source: "mnist-train-leveldb"
    scale: 0.0039215684
    batch_size: 100
  }
  include: { phase: TRAIN }
}
layers {
  top: "data"
  name: "data"
  type: DATA
  data_param {
    source: "mnist-test-leveldb"
    scale: 0.0039215684
    batch_size: 100
  }
  include: { phase: TEST }
}
layers {
  bottom: "data"
  top: "flatdata"
  name: "flatdata"
  type: FLATTEN
}
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: "gaussian"
      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: "gaussian"
      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: "gaussian"
      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: "gaussian"
      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: "gaussian"
      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: 784
    weight_filler {
      type: "gaussian"
      std: 1
      sparse: 15
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  bottom: "decode1"
  bottom: "flatdata"
  name: "loss"
  type: SIGMOID_CROSS_ENTROPY_LOSS
  include: { phase: TRAIN }
}
layers {
  bottom: "decode1"
  top: "decode1neuron"
  name: "decode1neuron"
  type: SIGMOID
  include: { phase: TEST }
}
layers {
  bottom: "decode1neuron"
  bottom: "flatdata"
  name: "loss"
  type: EUCLIDEAN_LOSS
  top: "loss"
  include: { phase: TEST }
}