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org.deeplearning4j.nn.conf.layers.Convolution1DLayer Maven / Gradle / Ivy
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * 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.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
package org.deeplearning4j.nn.conf.layers;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NoArgsConstructor;
import lombok.ToString;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.deeplearning4j.util.Convolution1DUtils;
import org.deeplearning4j.util.ConvolutionUtils;
import org.deeplearning4j.util.ValidationUtils;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class Convolution1DLayer extends ConvolutionLayer {
private RNNFormat rnnDataFormat = RNNFormat.NCW;
/*
//TODO: We will eventually want to NOT subclass off of ConvolutionLayer.
//Currently, we just subclass off the ConvolutionLayer and hard code the "width" dimension to 1
* This approach treats a multivariate time series with L timesteps and
* P variables as an L x 1 x P image (L rows high, 1 column wide, P
* channels deep). The kernel should be H trainingListeners, int layerIndex, INDArray layerParamsView,
boolean initializeParams, DataType networkDataType) {
LayerValidation.assertNInNOutSet("Convolution1DLayer", getLayerName(), layerIndex, getNIn(), getNOut());
org.deeplearning4j.nn.layers.convolution.Convolution1DLayer ret =
new org.deeplearning4j.nn.layers.convolution.Convolution1DLayer(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 InputType getOutputType(int layerIndex, InputType inputType) {
if (inputType == null || inputType.getType() != InputType.Type.RNN) {
throw new IllegalStateException("Invalid input for 1D CNN layer (layer index = " + layerIndex
+ ", layer name = \"" + getLayerName() + "\"): expect RNN input type with size > 0. Got: "
+ inputType);
}
InputType.InputTypeRecurrent it = (InputType.InputTypeRecurrent) inputType;
long inputTsLength = it.getTimeSeriesLength();
long outLength;
if (inputTsLength < 0) {
//Probably: user did InputType.recurrent(x) without specifying sequence length
outLength = -1;
} else {
outLength = Convolution1DUtils.getOutputSize(inputTsLength, kernelSize[0], stride[0], padding[0],
convolutionMode, dilation[0]);
}
return InputType.recurrent(nOut, outLength, rnnDataFormat);
}
@Override
public void setNIn(InputType inputType, boolean override) {
if (inputType == null || inputType.getType() != InputType.Type.RNN && inputType.getType() != InputType.Type.FF) {
throw new IllegalStateException("Invalid input for 1D CNN layer (layer name = \"" + getLayerName()
+ "\"): expect RNN input type with size > 0 or feed forward. Got: " + inputType);
}
if(inputType.getType() == InputType.Type.RNN) {
InputType.InputTypeRecurrent r = (InputType.InputTypeRecurrent) inputType;
if (nIn <= 0 || override) {
this.nIn = r.getSize();
}
if(this.rnnDataFormat == null || override)
this.rnnDataFormat = r.getFormat();
if(this.cnn2dDataFormat == null || override)
this.cnn2dDataFormat = rnnDataFormat == RNNFormat.NCW ? CNN2DFormat.NCHW : CNN2DFormat.NHWC;
} else if(inputType.getType() == InputType.Type.FF) {
InputType.InputTypeFeedForward r = (InputType.InputTypeFeedForward) inputType;
if (nIn <= 0 || override) {
this.nIn = r.getSize();
}
if(this.rnnDataFormat == null || override) {
DataFormat dataFormat = r.getTimeDistributedFormat();
if(dataFormat instanceof CNN2DFormat) {
CNN2DFormat cnn2DFormat = (CNN2DFormat) dataFormat;
this.rnnDataFormat = cnn2DFormat == CNN2DFormat.NCHW ? RNNFormat.NCW : RNNFormat.NWC;
this.cnn2dDataFormat = cnn2DFormat;
} else if(dataFormat instanceof RNNFormat) {
RNNFormat rnnFormat = (RNNFormat) dataFormat;
this.rnnDataFormat = rnnFormat;
}
}
if(this.cnn2dDataFormat == null || override)
this.cnn2dDataFormat = rnnDataFormat == RNNFormat.NCW ? CNN2DFormat.NCHW : CNN2DFormat.NHWC;
}
}
@Override
public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
if (inputType == null) {
throw new IllegalStateException("Invalid input for Convolution1D layer (layer name=\"" + getLayerName()
+ "\"): input is null");
}
return InputTypeUtil.getPreprocessorForInputTypeRnnLayers(inputType, rnnDataFormat,getLayerName());
}
public static class Builder extends BaseConvBuilder {
private RNNFormat rnnDataFormat = RNNFormat.NCW;
public Builder() {
this(0, 1, 0);
this.setKernelSize((int[]) null);
}
@Override
protected boolean allowCausal() {
return true;
}
public Builder rnnDataFormat(RNNFormat rnnDataFormat) {
this.rnnDataFormat = rnnDataFormat;
return this;
}
/**
* @param kernelSize Kernel size
* @param stride Stride
*/
public Builder(int kernelSize, int stride) {
this(kernelSize, stride, 0);
}
/**
* Constructor with specified kernel size, stride of 1, padding of 0
*
* @param kernelSize Kernel size
*/
public Builder(int kernelSize) {
this(kernelSize, 1, 0);
}
/**
* @param kernelSize Kernel size
* @param stride Stride
* @param padding Padding
*/
public Builder(int kernelSize, int stride, int padding) {
this.kernelSize = new int[] {1, 1};
this.stride = new int[] {1, 1};
this.padding = new int[] {0, 0};
this.setKernelSize(kernelSize);
this.setStride(stride);
this.setPadding(padding);
}
/**
* Size of the convolution
*
* @param kernelSize the length of the kernel
*/
public Builder kernelSize(int kernelSize) {
this.setKernelSize(kernelSize);
return this;
}
/**
* Stride for the convolution. Must be > 0
*
* @param stride Stride
*/
public Builder stride(int stride) {
this.setStride(stride);
return this;
}
/**
* Padding value for the convolution. Not used with {@link org.deeplearning4j.nn.conf.ConvolutionMode#Same}
*
* @param padding Padding value
*/
public Builder padding(int padding) {
this.setPadding(padding);
return this;
}
@Override
public void setKernelSize(int... kernelSize) {
if(kernelSize == null){
this.kernelSize = null;
return;
}
this.kernelSize = ConvolutionUtils.getIntConfig(kernelSize,1);
}
@Override
public void setStride(int... stride) {
if(stride == null){
this.stride = null;
return;
}
this.stride = ConvolutionUtils.getIntConfig(stride,1);
}
@Override
public void setPadding(int... padding) {
if(padding == null){
this.padding = null;
return;
}
this.padding = ConvolutionUtils.getIntConfig(padding,0);
}
@Override
public void setDilation(int... dilation) {
if(dilation == null) {
this.dilation = null;
return;
}
this.dilation = ConvolutionUtils.getIntConfig(dilation,1);
}
@Override
@SuppressWarnings("unchecked")
public Convolution1DLayer build() {
ConvolutionUtils.validateConvolutionModePadding(convolutionMode, padding);
ConvolutionUtils.validateCnnKernelStridePadding(kernelSize, stride, padding);
return new Convolution1DLayer(this);
}
}
}