extendablelearner.java

来自「一个纯java写的神经网络源代码」· Java 代码 · 共 469 行 · 第 1/2 页

        }
        return myGradient;
    }
    
    /**
     * Gets the default (normal calculation of the) gradient for weights.
     *
     * @param aCurrentInps the forwarded input.
     * @param j the input index of the weight.
     * @param currentPattern the back propagated gradients.
     * @param k the output index of the weight.
     *
     * @return the gradient for the weight w_j_k
     */
    public double getDefaultGradientWeight(double[] currentInps, int j, double[] currentPattern, int k) {
        return currentInps[j] * currentPattern[k];
    }
    
    /**
     * Gives learners and extenders a change to do some pre-computing before the
     * biases are updated.
     *
     * @param currentGradientOuts the back propagated gradients.
     */
    protected final void preBiasUpdate(double[] currentGradientOuts) {
        preBiasUpdateImpl(currentGradientOuts);
        
        // update weight extender...
        if(theUpdateWeightExtender != null && theUpdateWeightExtender.isEnabled()) {
            theUpdateWeightExtender.preBiasUpdate(currentGradientOuts);
        }
        
        // delta rule extenders...
        for(int i = 0; i < theDeltaRuleExtenders.size(); i++) {
            if(((DeltaRuleExtender)theDeltaRuleExtenders.get(i)).isEnabled()) {
                ((DeltaRuleExtender)theDeltaRuleExtenders.get(i)).
                        preBiasUpdate(currentGradientOuts);
            }
        }
        
        // gradient extenders...
        for(int i = 0; i < theGradientExtenders.size(); i++) {
            if(((GradientExtender)theGradientExtenders.get(i)).isEnabled()) {
                ((GradientExtender)theGradientExtenders.get(i)).
                        preBiasUpdate(currentGradientOuts);
            }
        }
    }
    
    /**
     * Gives learners a change to do some pre-computing before the biases are
     * updated.
     *
     * @param currentGradientOuts
     */
    protected void preBiasUpdateImpl(double[] currentGradientOuts) {
        
    }
    
    /**
     * Gives learners and extenders a change to do some pre-computing before the
     * weights are updated.
     *
     * @param currentPattern the back propagated gradients.
     * @param currentInps the forwarded input.
     */
    protected final void preWeightUpdate(double[] currentPattern, double[] currentInps) {
        preWeightUpdateImpl(currentPattern, currentInps);
        
        // update weight extender...
        if(theUpdateWeightExtender != null && theUpdateWeightExtender.isEnabled()) {
            theUpdateWeightExtender.preWeightUpdate(currentInps, currentPattern);
        }
        
        // delta rule extenders...
        for(int i = 0; i < theDeltaRuleExtenders.size(); i++) {
            if(((DeltaRuleExtender)theDeltaRuleExtenders.get(i)).isEnabled()) {
                ((DeltaRuleExtender)theDeltaRuleExtenders.get(i)).
                        preWeightUpdate(currentInps, currentPattern);
            }
        }
        
        // gradient extenders...
        for(int i = 0; i < theGradientExtenders.size(); i++) {
            if(((GradientExtender)theGradientExtenders.get(i)).isEnabled()) {
                ((GradientExtender)theGradientExtenders.get(i)).
                        preWeightUpdate(currentInps, currentPattern);
            }
        }
    }
    
    /**
     * Gives learners a change to do some pre-computing before the weights are
     * updated.
     *
     * @param currentPattern the back propagated gradients.
     * @param currentInps the forwarded input.
     */
    protected void preWeightUpdateImpl(double[] currentPattern, double[] currentInps) {
        
    }
    
    /**
     * Gives learners and extenders a change to do some post-computing after the
     * biases are updated.
     *
     * @param currentGradientOuts the back propagated gradients.
     */
    protected final void postBiasUpdate(double[] currentGradientOuts) {
        // gradient extenders...
        for(int i = 0; i < theGradientExtenders.size(); i++) {
            if(((GradientExtender)theGradientExtenders.get(i)).isEnabled()) {
                ((GradientExtender)theGradientExtenders.get(i)).
                        postBiasUpdate(currentGradientOuts);
            }
        }
        
        // delta rule extenders...
        for(int i = 0; i < theDeltaRuleExtenders.size(); i++) {
            if(((DeltaRuleExtender)theDeltaRuleExtenders.get(i)).isEnabled()) {
                ((DeltaRuleExtender)theDeltaRuleExtenders.get(i)).
                        postBiasUpdate(currentGradientOuts);
            }
        }
        
        // update weight extenders...
        if(theUpdateWeightExtender != null && theUpdateWeightExtender.isEnabled()) {
            theUpdateWeightExtender.postBiasUpdate(currentGradientOuts);
        }
        
        postBiasUpdateImpl(currentGradientOuts);
    }
    
    /**
     * Gives learners a change to do some post-computing after the biases are
     * updated.
     *
     * @param currentGradientOuts the back propagated gradients.
     */
    protected void postBiasUpdateImpl(double[] currentGradientOuts) {
        
    }
    
    /**
     * Gives learners and extenders a change to do some post-computing after the
     * weights are updated.
     *
     * @param currentPattern the back propagated gradients.
     * @param currentInps the forwarded input.
     */
    protected final void postWeightUpdate(double[] currentPattern, double[] currentInps) {
        // gradient extenders...
        for(int i = 0; i < theGradientExtenders.size(); i++) {
            if(((GradientExtender)theGradientExtenders.get(i)).isEnabled()) {
                ((GradientExtender)theGradientExtenders.get(i)).
                        postWeightUpdate(currentInps, currentPattern);
            }
        }
        
        // delta extenders...
        for(int i = 0; i < theDeltaRuleExtenders.size(); i++) {
            if(((DeltaRuleExtender)theDeltaRuleExtenders.get(i)).isEnabled()) {
                ((DeltaRuleExtender)theDeltaRuleExtenders.get(i)).
                        postWeightUpdate(currentInps, currentPattern);
            }
        }
        
        // update weight extenders...
        if(theUpdateWeightExtender != null && theUpdateWeightExtender.isEnabled()) {
            theUpdateWeightExtender.postWeightUpdate(currentInps, currentPattern);
        }
        
        postWeightUpdateImpl(currentInps, currentInps);
    }
    
    /**
     * Gives learners a change to do some post-computing after the weights are
     * updated.
     *
     * @param currentPattern the back propagated gradients.
     * @param currentInps the forwarded input.
     */
    protected void postWeightUpdateImpl(double[] currentPattern, double[] currentInps) {
        
    }
    
    /**
     * Adds a delta extender.
     *
     * @param aDeltaRuleExtender the delta rule extender to add.
     */
    public void addDeltaRuleExtender(DeltaRuleExtender aDeltaRuleExtender) {
        // Note one needs to be careful to the order of the extenders,
        // also note that basic and batch learner add a delta (momentum)
        // extender in their constructor
        
        theDeltaRuleExtenders.add(aDeltaRuleExtender);
        
        aDeltaRuleExtender.setLearner(this);
    }
    
    /**
     * Adds a gradient extender.
     *
     * @param aGradientExtender the gradient extender to add.
     */
    public void addGradientExtender(GradientExtender aGradientExtender) {
        theGradientExtenders.add(aGradientExtender);
        
        aGradientExtender.setLearner(this);
    }
    
    
    /**
     * Sets an update weight extender.
     *
     * @param anUpdateWeightExtender the update weight extender to set.
     */
    public void setUpdateWeightExtender(UpdateWeightExtender anUpdateWeightExtender) {
        theUpdateWeightExtender = anUpdateWeightExtender;
        
        theUpdateWeightExtender.setLearner(this);
    }
    
    /**
     * Gets the update weight extender.
     *
     * @return the update weight extender.
     */
    public UpdateWeightExtender getUpdateWeightExtender() {
        return theUpdateWeightExtender;
    }
}