org.deeplearning4j.nn.params.PReLUParamInitializer Maven / Gradle / Ivy
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package org.deeplearning4j.nn.params;
import lombok.val;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.BaseLayer;
import org.deeplearning4j.nn.conf.layers.Layer;
import org.deeplearning4j.nn.conf.layers.PReLULayer;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInitUtil;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.indexing.NDArrayIndex;
import java.util.Collections;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;
public class PReLUParamInitializer implements ParamInitializer {
public final static String WEIGHT_KEY = "W";
private long[] weightShape;
private long[] sharedAxes;
public PReLUParamInitializer(long[] shape, long[] sharedAxes) {
this.weightShape = shape;
this.sharedAxes = sharedAxes;
// Set shared axes to 1, broadcasting will take place on c++ level.
if (sharedAxes != null) {
for (long axis: sharedAxes) {
weightShape[(int)axis - 1] = 1;
}
}
}
public static PReLUParamInitializer getInstance(long[] shape, long[] sharedAxes) {
return new PReLUParamInitializer(shape, sharedAxes);
}
@Override
public long numParams(NeuralNetConfiguration conf) {
return numParams(conf.getLayer());
}
@Override
public long numParams(Layer l) {
return numParams(weightShape);
}
private long numParams(long[] shape) {
long flattened = 1;
for(long value : shape) {
flattened *= value;
}
return flattened;
}
@Override
public List paramKeys(Layer layer) {
return weightKeys(layer);
}
@Override
public List weightKeys(Layer layer) {
return Collections.singletonList(WEIGHT_KEY);
}
@Override
public List biasKeys(Layer layer) {
return Collections.emptyList();
}
@Override
public boolean isWeightParam(Layer layer, String key) {
return WEIGHT_KEY.equals(key);
}
@Override
public boolean isBiasParam(Layer layer, String key) {
return false;
}
@Override
public Map init(NeuralNetConfiguration conf, INDArray paramsView, boolean initializeParams) {
if (!(conf.getLayer() instanceof BaseLayer))
throw new IllegalArgumentException("unsupported layer type: " + conf.getLayer().getClass().getName());
Map params = Collections.synchronizedMap(new LinkedHashMap());
val length = numParams(conf);
if (paramsView.length() != length)
throw new IllegalStateException(
"Expected params view of length " + length + ", got length " + paramsView.length());
INDArray paramsViewReshape = paramsView.reshape(paramsView.length());
INDArray weightView = paramsViewReshape.get(NDArrayIndex.interval(0, length));
params.put(WEIGHT_KEY, createWeightMatrix(conf, weightView, initializeParams));
conf.addVariable(WEIGHT_KEY);
return params;
}
@Override
public Map getGradientsFromFlattened(NeuralNetConfiguration conf, INDArray gradientView) {
val length = numParams(conf);
INDArray gradientViewReshape = gradientView.reshape(gradientView.length());
INDArray weightGradientView = gradientViewReshape.get(NDArrayIndex.interval(0, length))
.reshape('f', weightShape);
Map out = new LinkedHashMap<>();
out.put(WEIGHT_KEY, weightGradientView);
return out;
}
protected INDArray createWeightMatrix(NeuralNetConfiguration conf, INDArray weightParamView,
boolean initializeParameters) {
PReLULayer layerConf = (PReLULayer) conf.getLayer();
if (initializeParameters) {
return layerConf.getWeightInitFn().init(layerConf.getNIn(), layerConf.getNOut(),
weightShape, IWeightInit.DEFAULT_WEIGHT_INIT_ORDER, weightParamView);
} else {
return WeightInitUtil.reshapeWeights(weightShape, weightParamView);
}
}
}