/*******************************************************************************
 * DIANNE  - Framework for distributed artificial neural networks
 * Copyright (C) 2015  iMinds - IBCN - UGent
 *
 * This file is part of DIANNE.
 *
 * DIANNE is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Affero General Public License as
 * published by the Free Software Foundation, either version 3 of the
 * License, or (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Affero General Public License for more details.
 *
 * You should have received a copy of the GNU Affero General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 *
 * Contributors:
 *     Tim Verbelen, Steven Bohez
 *******************************************************************************/
package be.iminds.iot.dianne.nn.module.regularization;

import java.util.UUID;

import be.iminds.iot.dianne.api.nn.module.AbstractModule;
import be.iminds.iot.dianne.tensor.Tensor;
import be.iminds.iot.dianne.tensor.TensorOps;

public class Dropout extends AbstractModule {

	private final float p;
	
	private Tensor mask = null;
	
	public Dropout(float p){
		super();
		this.p = 1-p; // p = rate to drop, so 1-p is bernoulli parameter
	}
	
	public Dropout(UUID id, float p){
		super(id);
		this.p = 1-p;
	}
	
	@Override
	protected void forward() {
		if(mask == null || !mask.hasDim(input.dims())){
			mask = new Tensor(input.dims());
		}
		
		mask.bernoulli(p);
		TensorOps.div(mask, mask, p);
		
		output = TensorOps.cmul(output, input, mask);
	}

	@Override
	protected void backward() {
		gradInput = TensorOps.cmul(gradInput, gradOutput, mask);
	}

}

@@ -1662,16 +1662,43 @@
 %0A%09%09%7D%0A%09%09%0A
+%09%09if(p %3C 1 && train) %7B%0A%09%09%0A%09
 %09%09mask.b
@@ -1712,16 +1712,17 @@
 i(p);%0A%09%09
+%09
 TensorOp
@@ -1746,16 +1746,17 @@
  p);%0A%09%09%0A
+%09
 %09%09output
@@ -1791,24 +1791,80 @@
 put, mask);%0A
+%09%09%0A%09%09%7D else %7B%0A%09%09%09// just forward%0A%09%09%09output = input;%0A%09%09%7D%0A
 %09%7D%0A%0A%09@Overri
@@ -1895,16 +1895,31 @@
 ard() %7B%0A
+%09%09if(p %3C 1 )%7B%0A%09
 %09%09gradIn
@@ -1969,14 +1969,56 @@
  mask);%0A
+%09%09%7D else %7B%0A%09%09%09gradInput = gradOutput;%0A%09%09%7D%0A
 %09%7D%0A%0A%7D%0A

fix dropout ... only drop in case of training