Dl4j-fit(DataSetIterator iterator)源码阅读（九）利用梯度更新参数

清疚 2022-06-04 10:11 82阅读 0赞

在前面我们已经通过反向传播计算出梯度。  
并且梯度也经过梯度标准化，已经L1， L2参数的调整，接下来继续返回到`StochasticGradientDescent.optimize()`方法中继续执行以下语句

//先获取模型的参数
    INDArray params = model.params();
    //然后使用StepFunction对参数进行更新
    stepFunction.step(params, gradient.gradient());

# stepFunction.step(params, gradient.gradient()); #

这里调用的实体类为：

/** * Subtract the line * * @author Adam Gibson */
    public class NegativeGradientStepFunction implements StepFunction { 
        @Override
        public void step(INDArray x, INDArray line, double step) {
            step(x, line);
        }
    
        @Override
        public void step(INDArray x, INDArray line) {
            x.subi(line);
        }
    
        @Override
        public void step() {
    
        }
    }

相当于参数直接减去对应的梯度。

然后再执行

//更新模型参数
    model.setParams(params);
    //获取模型当前的iteration的次数统计
    int iterationCount = BaseOptimizer.getIterationCount(model);
    //遍历 监听器
    for (IterationListener listener : iterationListeners)
        listener.iterationDone(model, iterationCount);
    
    //判断是否达到某种终止条件，
    //如果满足则进行log日志的打印
    checkTerminalConditions(pair.getFirst().gradient(), oldScore, score, i);
    //增加迭代次数
    BaseOptimizer.incrementIterationCount(model, 1);

# StepFunction #

/** * Custom step function for line search * * @author Adam Gibson */
    public interface StepFunction extends Serializable { 
    
        /** * Step with the given parameters * @param x the current parameters * @param line the line to step * @param step */
        void step(INDArray x, INDArray line, double step);
    
    
        /** * Step with no parameters */
        void step(INDArray x, INDArray line);
    
    
        void step();
    
    }

# checkTerminalConditions #

package org.deeplearning4j.optimize.solvers;
    /** * Base optimizer * @author Adam Gibson */
    
    @Override
    public boolean checkTerminalConditions(INDArray gradient, double oldScore, double score, int i) {
        for (TerminationCondition condition : terminationConditions) {
            if (condition.terminate(score, oldScore, new Object[] {gradient})) {
                log.debug("Hit termination condition on iteration {}: score={}, oldScore={}, condition={}", i, score,
                                oldScore, condition);
                if (condition instanceof EpsTermination && conf.getLayer() != null
                                && conf.getLearningRatePolicy() == LearningRatePolicy.Score) {
                    model.applyLearningRateScoreDecay();
                }
                return true;
            }
        }
        return false;
    }