org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex Maven / Gradle / Ivy
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* ******************************************************************************
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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.
* *
* * 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
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package org.deeplearning4j.nn.conf.graph.rnn;
import lombok.Data;
import org.deeplearning4j.nn.conf.graph.GraphVertex;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException;
import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.shade.jackson.annotation.JsonProperty;
@Data
public class LastTimeStepVertex extends GraphVertex {
private String maskArrayInputName;
/**
*
* @param maskArrayInputName The name of the input to look at when determining the last time step. Specifically, the
* mask array of this time series input is used when determining which time step to extract
* and return.
*/
public LastTimeStepVertex(@JsonProperty("maskArrayInputName") String maskArrayInputName) {
this.maskArrayInputName = maskArrayInputName;
}
@Override
public GraphVertex clone() {
return new LastTimeStepVertex(maskArrayInputName);
}
@Override
public boolean equals(Object o) {
if (!(o instanceof LastTimeStepVertex))
return false;
LastTimeStepVertex ltsv = (LastTimeStepVertex) o;
if (maskArrayInputName == null && ltsv.maskArrayInputName != null
|| maskArrayInputName != null && ltsv.maskArrayInputName == null)
return false;
return maskArrayInputName == null || maskArrayInputName.equals(ltsv.maskArrayInputName);
}
@Override
public int hashCode() {
return (maskArrayInputName == null ? 452766971 : maskArrayInputName.hashCode());
}
@Override
public long numParams(boolean backprop) {
return 0;
}
@Override
public int minVertexInputs() {
return 1;
}
@Override
public int maxVertexInputs() {
return 1;
}
@Override
public org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex instantiate(ComputationGraph graph,
String name, int idx, INDArray paramsView, boolean initializeParams, DataType networkDatatype) {
return new org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex(graph, name, idx, maskArrayInputName, networkDatatype);
}
@Override
public InputType getOutputType(int layerIndex, InputType... vertexInputs) throws InvalidInputTypeException {
if (vertexInputs.length != 1)
throw new InvalidInputTypeException("Invalid input type: cannot get last time step of more than 1 input");
if (vertexInputs[0].getType() != InputType.Type.RNN) {
throw new InvalidInputTypeException(
"Invalid input type: cannot get subset of non RNN input (got: " + vertexInputs[0] + ")");
}
return InputType.feedForward(((InputType.InputTypeRecurrent) vertexInputs[0]).getSize());
}
@Override
public MemoryReport getMemoryReport(InputType... inputTypes) {
//No additional working memory (beyond activations/epsilons)
return new LayerMemoryReport.Builder(null, LastTimeStepVertex.class, inputTypes[0],
getOutputType(-1, inputTypes)).standardMemory(0, 0).workingMemory(0, 0, 0, 0).cacheMemory(0, 0)
.build();
}
@Override
public String toString() {
return "LastTimeStepVertex(inputName=" + maskArrayInputName + ")";
}
}