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

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package org.deeplearning4j.nn.api;

import org.deeplearning4j.nn.conf.GradientNormalization;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.learning.config.IUpdater;
import org.nd4j.linalg.learning.regularization.Regularization;

import java.util.List;

public interface TrainingConfig {

    /**
     * @return Name of the layer
     */
    String getLayerName();

    /**
     * Get the regularization types (l1/l2/weight decay) for the given parameter. Different parameters may have different
     * regularization types.
     *
     * @param paramName Parameter name ("W", "b" etc)
     * @return Regularization types (if any) for the specified parameter
     */
    List getRegularizationByParam(String paramName);

    /**
     * Is the specified parameter a layerwise pretraining only parameter?

     * For example, visible bias params in an autoencoder (or, decoder params in a variational autoencoder) aren't
     * used during supervised backprop.

     * Layers (like DenseLayer, etc) with no pretrainable parameters will return false for all (valid) inputs.
     *
     * @param paramName Parameter name/key
     * @return True if the parameter is for layerwise pretraining only, false otherwise
     */
    boolean isPretrainParam(String paramName);

    /**
     * Get the updater for the given parameter. Typically the same updater will be used for all updaters, but this
     * is not necessarily the case
     *
     * @param paramName Parameter name
     * @return IUpdater for the parameter
     */
    IUpdater getUpdaterByParam(String paramName);

    /**
     * @return The gradient normalization configuration
     */
    GradientNormalization getGradientNormalization();

    /**
     * @return The gradient normalization threshold
     */
    double getGradientNormalizationThreshold();

    void setDataType(DataType dataType);

}