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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.nn.params;
import lombok.val;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.weights.WeightInitUtil;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.indexing.NDArrayIndex;
import java.util.LinkedHashMap;
import java.util.Map;
public class DeconvolutionParamInitializer extends ConvolutionParamInitializer {
private static final DeconvolutionParamInitializer INSTANCE = new DeconvolutionParamInitializer();
public static DeconvolutionParamInitializer getInstance() {
return INSTANCE;
}
@Override
protected INDArray createWeightMatrix(NeuralNetConfiguration conf, INDArray weightView, boolean initializeParams) {
/*
Create a 4d weight matrix of:
(number of kernels, num input channels, kernel height, kernel width)
Note c order is used specifically for the CNN weights, as opposed to f order elsewhere
Inputs to the convolution layer are:
(batch size, num input feature maps, image height, image width)
*/
org.deeplearning4j.nn.conf.layers.Deconvolution2D layerConf =
(org.deeplearning4j.nn.conf.layers.Deconvolution2D) conf.getLayer();
if (initializeParams) {
int[] kernel = layerConf.getKernelSize();
int[] stride = layerConf.getStride();
val inputDepth = layerConf.getNIn();
val outputDepth = layerConf.getNOut();
double fanIn = inputDepth * kernel[0] * kernel[1];
double fanOut = outputDepth * kernel[0] * kernel[1] / ((double) stride[0] * stride[1]);
val weightsShape = new long[] {inputDepth, outputDepth, kernel[0], kernel[1]};
INDArray weights = layerConf.getWeightInitFn().init(
fanIn, fanOut, weightsShape, 'c', weightView);
return weights;
} else {
int[] kernel = layerConf.getKernelSize();
INDArray weights = WeightInitUtil.reshapeWeights(
new long[] {layerConf.getNIn(), layerConf.getNOut(), kernel[0],
kernel[1]}, weightView, 'c');
return weights;
}
}
@Override
public Map getGradientsFromFlattened(NeuralNetConfiguration conf, INDArray gradientView) {
org.deeplearning4j.nn.conf.layers.Deconvolution2D layerConf =
(org.deeplearning4j.nn.conf.layers.Deconvolution2D) conf.getLayer();
int[] kernel = layerConf.getKernelSize();
val nIn = layerConf.getNIn();
val nOut = layerConf.getNOut();
Map out = new LinkedHashMap<>();
if(layerConf.hasBias()){
INDArray biasGradientView = gradientView.get(NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(0, nOut));
INDArray weightGradientView =
gradientView.get(NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(nOut, numParams(conf)))
.reshape('c', nIn, nOut, kernel[0], kernel[1]);
out.put(BIAS_KEY, biasGradientView);
out.put(WEIGHT_KEY, weightGradientView);
} else {
INDArray weightGradientView = gradientView.reshape('c', nIn, nOut, kernel[0], kernel[1]);
out.put(WEIGHT_KEY, weightGradientView);
}
return out;
}
}
