org.deeplearning4j.nn.api.Trainable Maven / Gradle / Ivy

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 *  * 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.
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package org.deeplearning4j.nn.api;

import org.nd4j.linalg.api.ndarray.INDArray;

import java.util.Map;

public interface Trainable {

    /**
     * @return Training configuration
     */
    TrainingConfig getConfig();

    /**
     * @return Number of parameters
     */
    long numParams();

    /**
     * @return 1d parameter vector
     */
    INDArray params();

    /**
     * @param backpropOnly If true: return only parameters that are not exclusively used for layerwise pretraining
     * @return Parameter table
     */
    Map paramTable(boolean backpropOnly);

    /**
     * DL4J layers typically produce the sum of the gradients during the backward pass for each layer, and if required
     * (if minibatch=true) then divide by the minibatch size.

     * However, there are some exceptions, such as the batch norm mean/variance estimate parameters: these "gradients"
     * are actually not gradients, but are updates to be applied directly to the parameter vector. Put another way,
     * most gradients should be divided by the minibatch to get the average; some "gradients" are actually final updates
     * already, and should not be divided by the minibatch size.
     *
     * @param paramName Name of the parameter
     * @return True if gradients should be divided by minibatch (most params); false otherwise (edge cases like batch norm mean/variance estimates)
     */
    boolean updaterDivideByMinibatch(String paramName);

    /**
     * @return 1D gradients view array
     */
    INDArray getGradientsViewArray();

}