org.deeplearning4j.nn.conf.layers.AbstractLSTM Maven / Gradle / Ivy

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package org.deeplearning4j.nn.conf.layers;

import lombok.*;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.activations.impl.ActivationSigmoid;

@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public abstract class AbstractLSTM extends BaseRecurrentLayer {

    protected double forgetGateBiasInit;
    protected IActivation gateActivationFn = new ActivationSigmoid();
    protected boolean helperAllowFallback = true;

    protected AbstractLSTM(Builder builder) {
        super(builder);
        this.forgetGateBiasInit = builder.forgetGateBiasInit;
        this.gateActivationFn = builder.gateActivationFn;
        this.helperAllowFallback = builder.helperAllowFallback;
    }

    @AllArgsConstructor
    @NoArgsConstructor
    @Getter
    @Setter
    public static abstract class Builder> extends BaseRecurrentLayer.Builder {

        /**
         * Set forget gate bias initalizations. Values in range 1-5 can potentially help with learning or longer-term
         * dependencies.
         */
        protected double forgetGateBiasInit = 1.0;

        /**
         * Activation function for the LSTM gates. Note: This should be bounded to range 0-1: sigmoid or hard sigmoid,
         * for example
         */
        protected IActivation gateActivationFn = new ActivationSigmoid();

        /**
         * When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed?
         * If set to false, an exception in CuDNN will be propagated back to the user. If false, the built-in
         * (non-CuDNN) implementation for LSTM/GravesLSTM will be used
         *
         */
        protected boolean helperAllowFallback = true;

        /**
         * Set forget gate bias initalizations. Values in range 1-5 can potentially help with learning or longer-term
         * dependencies.
         */
        public T forgetGateBiasInit(double biasInit) {
            this.setForgetGateBiasInit(biasInit);
            return (T) this;
        }

        /**
         * Activation function for the LSTM gates. Note: This should be bounded to range 0-1: sigmoid or hard sigmoid,
         * for example
         *
         * @param gateActivationFn Activation function for the LSTM gates
         */
        public T gateActivationFunction(String gateActivationFn) {
            return (T) gateActivationFunction(Activation.fromString(gateActivationFn));
        }

        /**
         * Activation function for the LSTM gates. Note: This should be bounded to range 0-1: sigmoid or hard sigmoid,
         * for example
         *
         * @param gateActivationFn Activation function for the LSTM gates
         */
        public T gateActivationFunction(Activation gateActivationFn) {
            return (T) gateActivationFunction(gateActivationFn.getActivationFunction());
        }

        /**
         * Activation function for the LSTM gates. Note: This should be bounded to range 0-1: sigmoid or hard sigmoid,
         * for example
         *
         * @param gateActivationFn Activation function for the LSTM gates
         */
        public T gateActivationFunction(IActivation gateActivationFn) {
            this.setGateActivationFn(gateActivationFn);
            return (T) this;
        }

        /**
         * When using a helper (CuDNN or MKLDNN in some cases) and an error is encountered, should fallback to the non-helper implementation be allowed?
         * If set to false, an exception in the helper will be propagated back to the user. If false, the built-in
         * (non-helper) implementation for LSTM/GravesLSTM will be used
         *
         * @param allowFallback Whether fallback to non-helper implementation should be used
         */
        public T helperAllowFallback(boolean allowFallback) {
            this.setHelperAllowFallback(allowFallback);
            return (T) this;
        }

    }

}