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* * 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.
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* * information regarding copyright ownership.
* * 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.
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* * SPDX-License-Identifier: Apache-2.0
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package org.deeplearning4j.nn.conf.layers;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NoArgsConstructor;
import lombok.ToString;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.params.EmptyParamInitializer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;
import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class CnnLossLayer extends FeedForwardLayer {
protected ILossFunction lossFn;
protected CNN2DFormat format = CNN2DFormat.NCHW;
private CnnLossLayer(Builder builder) {
super(builder);
this.lossFn = builder.lossFn;
this.format = builder.format;
}
@Override
public Layer instantiate(NeuralNetConfiguration conf, Collection trainingListeners,
int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
org.deeplearning4j.nn.layers.convolution.CnnLossLayer ret =
new org.deeplearning4j.nn.layers.convolution.CnnLossLayer(conf, networkDataType);
ret.setListeners(trainingListeners);
ret.setIndex(layerIndex);
ret.setParamsViewArray(layerParamsView);
Map paramTable = initializer().init(conf, layerParamsView, initializeParams);
ret.setParamTable(paramTable);
ret.setConf(conf);
return ret;
}
@Override
public ParamInitializer initializer() {
return EmptyParamInitializer.getInstance();
}
@Override
public InputType getOutputType(int layerIndex, InputType inputType) {
if (inputType == null || (inputType.getType() != InputType.Type.CNN
&& inputType.getType() != InputType.Type.CNNFlat)) {
throw new IllegalStateException(
"Invalid input type for CnnLossLayer (layer index = " + layerIndex + ", layer name=\""
+ getLayerName() + "\"): Expected CNN or CNNFlat input, got " + inputType);
}
return inputType;
}
@Override
public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
return InputTypeUtil.getPreProcessorForInputTypeCnnLayers(inputType, getLayerName());
}
@Override
public LayerMemoryReport getMemoryReport(InputType inputType) {
//During inference and training: dup the input array. But, this counts as *activations* not working memory
return new LayerMemoryReport.Builder(layerName, getClass(), inputType, inputType).standardMemory(0, 0) //No params
.workingMemory(0, 0, 0, 0)
.cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching
.build();
}
@Override
public void setNIn(InputType inputType, boolean override) {
if(inputType instanceof InputType.InputTypeConvolutional){
this.format = ((InputType.InputTypeConvolutional) inputType).getFormat();
}
}
public static class Builder extends BaseOutputLayer.Builder {
protected CNN2DFormat format = CNN2DFormat.NCHW;
public Builder() {
this.activationFn = Activation.IDENTITY.getActivationFunction();
}
public Builder(LossFunction lossFunction) {
lossFunction(lossFunction);
}
public Builder(ILossFunction lossFunction) {
this.lossFn = lossFunction;
}
public Builder format(CNN2DFormat format){
this.format = format;
return this;
}
@Override
@SuppressWarnings("unchecked")
public Builder nIn(int nIn) {
throw new UnsupportedOperationException("Ths layer has no parameters, thus nIn will always equal nOut.");
}
@Override
@SuppressWarnings("unchecked")
public Builder nOut(int nOut) {
throw new UnsupportedOperationException("Ths layer has no parameters, thus nIn will always equal nOut.");
}
@Override
public void setNIn(long nIn){
throw new UnsupportedOperationException(
"This layer has no parameters, thus nIn will always equal nOut.");
}
@Override
public void setNOut(long nOut){
throw new UnsupportedOperationException(
"This layer has no parameters, thus nIn will always equal nOut.");
}
@Override
@SuppressWarnings("unchecked")
public CnnLossLayer build() {
return new CnnLossLayer(this);
}
}
}