cluster {
  nworker_groups: 1
  nserver_groups: 1
  nservers_per_group: 1
  nworkers_per_group: 1
  workspace: "examples/mnist"
}

model {
  name: "deep-big-simple-dbm"
  train_steps: 46000
  test_steps:1
  test_frequency:1000
  display_frequency: 100
  alg: kContrastiveDivergence
  pcd_k: 15
  updater{
    base_lr: 0.1
    lr_change: kFixed
    type: kSGD
  }

  neuralnet {
  layer {
    name: "data"
    type: kShardData
    sharddata_conf {
      path: "examples/mnist/mnist_train_shard"
      batchsize: 20
    }
    include: kTrain
  }


  layer {
    name: "data"
    type: kShardData
    sharddata_conf {
      path: "examples/mnist/mnist_test_shard"
      batchsize: 20
    }
    include: kTest
  }


  layer{
    name:"mnist"
    type: kMnist
    srclayers: "data"
    mnist_conf {
      norm_a: 255
      norm_b: 0
    }
  }

  layer{
    name: "RBMVis"
    type: kRBMVis
    srclayers:"mnist"
    srclayers:"RBMHid"
    rbmvis_conf{
      num_output: 500
    }
    param{
      name: "w1"
      init_method: kUniformSqrtFanInOut
      low:-9.79
      high:9.79
    }
    param{
      name: "b1"
      init_method: kConstant
      value: 0.0
    }
  }

  layer{
    name: "RBMHid"
    type: kRBMHid
    srclayers:"RBMVis"
    rbmhid_conf{
      hid_dim: 500
    }
    param{
      name: "w2"
      share_from: "w1"
    }
    param{
      name: "b2"
      init_method: kConstant
      value: 0.0
    }
  }
  }
}