com.intel.analytics.bigdl.models.autoencoder.Autoencoder.scala Maven / Gradle / Ivy
/*
* Copyright 2016 The BigDL Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.intel.analytics.bigdl.models.autoencoder
import com.intel.analytics.bigdl._
import com.intel.analytics.bigdl.nn.{Graph, _}
import com.intel.analytics.bigdl.numeric.NumericFloat
object Autoencoder {
val rowN = 28
val colN = 28
val featureSize = rowN * colN
def apply(classNum: Int): Module[Float] = {
val model = Sequential[Float]()
model.add(new Reshape(Array(featureSize)))
model.add(new Linear(featureSize, classNum))
model.add(new ReLU[Float]())
model.add(new Linear(classNum, featureSize))
model.add(new Sigmoid[Float]())
model
}
def graph(classNum: Int): Module[Float] = {
val input = Reshape(Array(featureSize)).inputs()
val linear1 = Linear(featureSize, classNum).inputs(input)
val relu = ReLU().inputs(linear1)
val linear2 = Linear(classNum, featureSize).inputs(relu)
val output = Sigmoid().inputs(linear2)
Graph(input, output)
}
}