/*
 *
 *  * Copyright 2016 Skymind,Inc.
 *  *
 *  *    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 org.deeplearning4j.nn.graph.vertex.impl;

import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.graph.vertex.BaseGraphVertex;
import org.deeplearning4j.nn.graph.vertex.VertexIndices;
import org.nd4j.linalg.api.ndarray.INDArray;

/**
 * A ScaleVertex is used to scale the size of activations of a single layer<br>
 * For example, ResNet activations can be scaled in repeating blocks to keep variance
 * under control.
 *
 * @author Justin Long (@crockpotveggies)
 */
public class ScaleVertex extends BaseGraphVertex {

    private double scaleFactor;

    public ScaleVertex(ComputationGraph graph, String name, int vertexIndex, double scaleFactor){
        this(graph,name,vertexIndex,null,null,scaleFactor);
    }

    public ScaleVertex(ComputationGraph graph, String name, int vertexIndex, VertexIndices[] inputVertices, VertexIndices[] outputVertices, double scaleFactor) {
        super(graph, name, vertexIndex, inputVertices, outputVertices);
        this.scaleFactor = scaleFactor;
    }

    @Override
    public boolean hasLayer() {
        return false;
    }

    @Override
    public boolean isOutputVertex() {
        return false;
    }

    @Override
    public Layer getLayer() {
        return null;
    }

    @Override
    public INDArray doForward(boolean training) {
        if(!canDoForward()) throw new IllegalStateException("Cannot do forward pass: inputs not set (ScaleVertex "+vertexName+" idx "+vertexIndex+")");

        if(inputs.length > 1) throw new IllegalArgumentException("ScaleVertex (name "+vertexName+" idx "+vertexIndex+") only supports 1 input.");

        INDArray prod = inputs[0].dup();
        prod.muli(scaleFactor);

        return prod;
    }

    @Override
    public Pair<Gradient, INDArray[]> doBackward(boolean tbptt) {
        if(!canDoBackward()) throw new IllegalStateException("Cannot do backward pass: errors not set (ScaleVertex "+vertexName+" idx "+vertexIndex+")");

        return new Pair<>(null,new INDArray[]{epsilon.muli(1/scaleFactor)});
    }

    @Override
    public void setBackpropGradientsViewArray(INDArray backpropGradientsViewArray) {
        if(backpropGradientsViewArray != null) throw new RuntimeException("Vertex does not have gradients; gradients view array cannot be set here (ScaleVertex "+vertexName+" idx "+vertexIndex+")");
    }

    @Override
    public String toString() {
        return "ScaleVertex(id=" + this.getVertexIndex() + ",name=\"" + this.getVertexName() + "\",scaleFactor=" + scaleFactor + ")";
    }
}

@@ -2816,10 +2816,8 @@
 uli(
-1/
 scal

Fix backprop math.