/*- * * * Copyright 2016 Skymind,Inc. * * * * Licensed under the Apache License, Version 2.0 (the "License"); * * you may not use this file except in compliance with the License. * * You may obtain a copy of the License at * * * * http://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. * */ package org.deeplearning4j.nn.conf.graph.rnn; import org.nd4j.shade.jackson.annotation.JsonProperty; import lombok.Data; import lombok.EqualsAndHashCode; 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.graph.ComputationGraph; import org.nd4j.linalg.api.ndarray.INDArray; /** LastTimeStepVertex is used in the context of recurrent neural network activations, to go from 3d (time series) * activations to 2d activations, by extracting out the last time step of activations for each example.<br> * This can be used for example in sequence to sequence architectures, and potentially for sequence classification. * <b>NOTE</b>: Because RNNs may have masking arrays (to allow for examples/time series of different lengths in the same * minibatch), it is necessary to provide the same of the network input that has the corresponding mask array. If this * input does not have a mask array, the last time step of the input will be used for all examples; otherwise, the time * step of the last non-zero entry in the mask array (for each example separately) will be used. * @author Alex Black */ @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 int numParams(boolean backprop) { return 0; } @Override public org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex instantiate(ComputationGraph graph, String name, int idx, INDArray paramsView, boolean initializeParams) { return new org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex(graph, name, idx, maskArrayInputName); } @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 String toString() { return "LastTimeStepVertex(inputName=" + maskArrayInputName + ")"; } }