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Java example source code file (BaseRecurrentLayer.java)

This example Java source code file (BaseRecurrentLayer.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

baselayer, baserecurrentlayer, concurrenthashmap, hashmap, indarray, map, pair, threading, threads, util

The BaseRecurrentLayer.java Java example source code

package org.deeplearning4j.nn.layers.recurrent;

import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;

import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.BaseLayer;
import org.nd4j.linalg.api.ndarray.INDArray;

public abstract class BaseRecurrentLayer<LayerConfT extends org.deeplearning4j.nn.conf.layers.Layer> extends BaseLayer {

	/** stateMap stores the INDArrays needed to do rnnTimeStep() forward pass. */
	protected Map<String,INDArray> stateMap = new ConcurrentHashMap<>();

	/** State map for use specifically in truncated BPTT training. Whereas stateMap contains the
	 * state from which forward pass is initialized, the tBpttStateMap contains the state at the
	 * end of the last truncated bptt
	 * */
	protected Map<String,INDArray> tBpttStateMap = new ConcurrentHashMap<>();

	public BaseRecurrentLayer(NeuralNetConfiguration conf) {
		super(conf);
	}

	public BaseRecurrentLayer(NeuralNetConfiguration conf, INDArray input) {
		super(conf, input);
	}

	/**Do one or more time steps using the previous time step state stored in stateMap.<br>
	 * Can be used to efficiently do forward pass one or n-steps at a time (instead of doing
	 * forward pass always from t=0)<br>
	 * If stateMap is empty, default initialization (usually zeros) is used<br>
	 * Implementations also update stateMap at the end of this method
	 * @param input Input to this layer
	 * @return activations
	 */
	public abstract INDArray rnnTimeStep(INDArray input);

	/** Returns a shallow copy of the stateMap */
	public Map<String,INDArray> rnnGetPreviousState(){
		return new HashMap<>(stateMap);
	}

	/** Set the state map. Values set using this method will be used
	 * in next call to rnnTimeStep()
	 */
	public void rnnSetPreviousState(Map<String,INDArray> stateMap){
		this.stateMap.clear();
		this.stateMap.putAll(stateMap);
	}

	/** Reset/clear the stateMap for rnnTimeStep() and tBpttStateMap for rnnActivateUsingStoredState() */
	public void rnnClearPreviousState(){
		stateMap.clear();
		tBpttStateMap.clear();
	}

	/** Similar to rnnTimeStep, this method is used for activations using the state
	 * stored in the stateMap as the initialization. However, unlike rnnTimeStep this
	 * method does not alter the stateMap; therefore, unlike rnnTimeStep, multiple calls to
	 * this method (with identical input) will:<br>
	 * (a) result in the same output<br>
	 * (b) leave the state maps (both stateMap and tBpttStateMap) in an identical state
	 * @param input Layer input
	 * @param training if true: training. Otherwise: test
	 * @param storeLastForTBPTT If true: store the final state in tBpttStateMap for use in truncated BPTT training
	 * @return Layer activations
	 */
	public abstract INDArray rnnActivateUsingStoredState(INDArray input, boolean training, boolean storeLastForTBPTT);

	public Map<String,INDArray> rnnGetTBPTTState(){
		return new HashMap<>(tBpttStateMap);
	}

	public void rnnSetTBPTTState(Map<String,INDArray> state){
		tBpttStateMap.clear();
		tBpttStateMap.putAll(state);
	}

	/**Truncated BPTT equivalent of Layer.backpropGradient().
	 * Primary difference here is that forward pass in the context of BPTT is that we do
	 * forward pass using stored state for truncated BPTT vs. from zero initialization
	 * for standard BPTT.
	 */
	public abstract Pair<Gradient,INDArray> tbpttBackpropGradient(INDArray epsilon, int tbpttBackLength);
}

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