SINGA-9 Add Support for Restricted Boltzman Machine (RBM) model

This is to implement RBM in SINGA.
To training RBM models, the Contrastive Divergence (CD) algorithm is implemented.
We have implemented a BPWorker to run the Back-Propagation algorithm. To implement the CD algorithm, we follow the same way
to create a CDWorker whose RunOneBatch function controls the logic of the CD algorithm, including positive phase,
negative phase and computing gradient phase. RBM's layers are different to the layers for feed-forward neural networks,
hence new layers for RBM models are added.

examples/rbm/autoencoder.conf

@@ -0,0 +1,299 @@
+name: "deep-big-simple-mlp"
+train_steps: 12200
+test_steps:100
+test_freq:100
+disp_freq:20
+checkpoint_after: 1000
+checkpoint_freq: 1000
+checkpoint_path: "examples/rbm/checkpoint/rbm0/checkpoint/step6000-worker0.bin"
+checkpoint_path: "examples/rbm/checkpoint/rbm1/checkpoint/step6000-worker0.bin"
+checkpoint_path: "examples/rbm/checkpoint/rbm2/checkpoint/step6000-worker0.bin"
+checkpoint_path: "examples/rbm/checkpoint/rbm3/checkpoint/step6000-worker0.bin"
+alg: kBP
+updater{
+  type: kAdaGrad
+  learning_rate{
+  base_lr: 0.01
+  type: kFixed
+  }
+}
+
+neuralnet {
+  layer {
+    name: "data"
+    type: kShardData
+    sharddata_conf {
+      path: "examples/rbm/mnist_train_shard"
+      batchsize: 1000
+    }
+    exclude: kTest
+  }
+
+  layer {
+    name: "data"
+    type: kShardData
+    sharddata_conf {
+      path: "examples/rbm/mnist_test_shard"
+      batchsize: 1000
+    }
+    exclude: kTrain
+  }
+
+  layer{
+    name:"mnist"
+    type: kMnist
+    srclayers: "data"
+    mnist_conf {
+      norm_a: 255
+      norm_b: 0
+    }
+  }
+
+  layer{
+    name: "label"
+    type: kLabel
+    srclayers: "data"
+  }
+
+  layer{
+    name: "fc1"
+    type: kInnerProduct
+    srclayers:"mnist"
+    innerproduct_conf{
+      num_output: 1000
+    }
+    param{
+      name: "w1"
+      init{
+      type: kUniform
+      low:-0.05
+      high:0.05
+      }
+    }
+    param{
+      name: "rb12"
+      init{
+      type: kUniform
+      low: -0.05
+      high:0.05
+      }
+    }
+  }
+
+  layer{
+    name: "sigmoid1"
+    type: kSigmoid
+    srclayers:"fc1"
+  }
+  layer{
+    name: "fc2"
+    type: kInnerProduct
+    srclayers:"sigmoid1"
+    innerproduct_conf{
+      num_output: 500
+    }
+    param{
+      name: "w2"
+      init{
+      type: kUniform
+      low:-0.05
+      high:0.05
+      }
+    }
+    param{
+      name: "rb22"
+      init{
+      type: kUniform
+      low: -0.05
+      high:0.05
+      }
+    }
+  }
+
+  layer{
+    name: "sigmoid2"
+    type: kSigmoid
+    srclayers:"fc2"
+  }
+
+  layer{
+    name: "fc3"
+    type:  kInnerProduct
+    srclayers:"sigmoid2"
+    innerproduct_conf{
+      num_output: 250
+    }
+    param{
+      name: "w3"
+      init{
+      type: kUniform
+      low:-0.05
+      high:0.05
+      }
+    }
+    param{
+      name: "rb32"
+      init{
+      type: kUniform
+      low: -0.05
+      high:0.05
+      }
+    }
+  }
+
+  layer{
+    name: "sigmoid3"
+    type: kSigmoid
+    srclayers:"fc3"
+  }
+
+  layer{
+    name: "fc4"
+    type: kInnerProduct
+    srclayers:"sigmoid3"
+    innerproduct_conf{
+      num_output: 30
+    }
+    param{
+      name: "w4"
+      init{
+      type: kUniform
+      low:-0.05
+      high:0.05
+      }
+    }
+    param{
+      name: "rb42"
+      init{
+      type: kUniform
+      low: -0.05
+      high:0.05
+      }
+    }
+  }
+
+  layer{
+    name: "fc5"
+    type: kInnerProduct
+    #srclayers:"sigmoid4"
+    srclayers:"fc4"
+    innerproduct_conf{
+      num_output: 250
+      transpose: true
+    }
+    param{
+      name: "w5"
+      share_from: "w4"
+    }
+    param{
+      name: "rb41"
+      init{
+      type: kUniform
+      low: -0.05
+      high:0.05
+      }
+    }
+  }
+
+  layer{
+    name: "sigmoid5"
+    type: kSigmoid
+    srclayers:"fc5"
+  }
+  layer{
+    name: "fc6"
+    type: kInnerProduct
+    srclayers:"sigmoid5"
+    innerproduct_conf{
+      num_output: 500
+      transpose: true
+    }
+    param{
+      name: "w6"
+      share_from: "w3"
+    }
+    param{
+      name: "rb31"
+      init{
+      type: kUniform
+      low: -0.05
+      high:0.05
+      }
+    }
+
+  }
+
+  layer{
+    name: "sigmoid6"
+    type: kSigmoid
+    srclayers:"fc6"
+  }
+ layer{
+    name: "fc7"
+    type: kInnerProduct
+    srclayers:"sigmoid6"
+    innerproduct_conf{
+      num_output: 1000
+      transpose: true
+    }
+    param{
+      name: "w7"
+      share_from: "w2"
+    }
+    param{
+      name: "rb21"
+      init{
+      type: kUniform
+      low: -0.05
+      high:0.05
+      }
+    }
+
+  }
+
+  layer{
+    name: "sigmoid7"
+    type: kSigmoid
+    srclayers:"fc7"
+  }
+ layer{
+    name: "fc8"
+    type: kInnerProduct
+    srclayers:"sigmoid7"
+    innerproduct_conf{
+      num_output: 784
+      transpose: true
+    }
+    param{
+      name: "w8"
+      share_from: "w1"
+    }
+    param{
+      name: "rb11"
+      init{
+      type: kUniform
+      low: -0.05
+      high:0.05
+      }
+    }
+
+  }
+
+  layer{
+    name: "sigmoid8"
+    type: kSigmoid
+    srclayers:"fc8"
+  }
+
+  layer{
+    name: "loss"
+    type:kEuclideanLoss
+    srclayers:"sigmoid8"
+    srclayers:"mnist"
+  }
+}
+cluster {
+  nworker_groups: 1
+  nserver_groups: 1
+  workspace: "examples/rbm/checkpoint/autoencoder/"
+}