nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/AdamUpdater.java
@Override
    public void applyUpdater(INDArray gradient, int iteration, int epoch) {
        if (m == null || v == null)
            throw new IllegalStateException("Updater has not been initialized with view state");

        double beta1 = config.getBeta1();
        double beta2 = config.getBeta2();
        double learningRate = config.getLearningRate(iteration, epoch);
        double epsilon = config.getEpsilon();

        INDArray oneMinusBeta1Grad = gradient.mul(1.0 - beta1);
        m.muli(beta1).addi(oneMinusBeta1Grad);

        INDArray oneMinusBeta2GradSquared = gradient.mul(gradient).muli(1 - beta2);
        v.muli(beta2).addi(oneMinusBeta2GradSquared);

        double beta1t = FastMath.pow(beta1, iteration + 1);
        double beta2t = FastMath.pow(beta2, iteration + 1);

        double alphat = learningRate * FastMath.sqrt(1 - beta2t) / (1 - beta1t);
        if (Double.isNaN(alphat) || alphat == 0.0)
            alphat = epsilon;
        INDArray sqrtV = Transforms.sqrt(v.dup(gradientReshapeOrder), false).addi(epsilon);

        gradient.assign(m).muli(alphat).divi(sqrtV);
    }
@Override
    public void applyUpdater(INDArray gradient, int iteration, int epoch) {
        if (m == null || v == null)
            throw new IllegalStateException("Updater has not been initialized with view state");

        double beta1 = config.getBeta1();
        double beta2 = config.getBeta2();
        double learningRate = config.getLearningRate(iteration, epoch);
        double epsilon = config.getEpsilon();

        INDArray oneMinusBeta1Grad = gradient.mul(1.0 - beta1);
        m.muli(beta1).addi(oneMinusBeta1Grad);

        INDArray oneMinusBeta2GradSquared = gradient.mul(gradient).muli(1 - beta2);
        v.muli(beta2).addi(oneMinusBeta2GradSquared);

        double beta1t = FastMath.pow(beta1, iteration + 1);
        double beta2t = FastMath.pow(beta2, iteration + 1);

        double alphat = learningRate * FastMath.sqrt(1 - beta2t) / (1 - beta1t);
        if (Double.isNaN(alphat) || alphat == 0.0)
            alphat = epsilon;
        INDArray sqrtV = Transforms.sqrt(v.dup(gradientReshapeOrder), false).addi(epsilon);

        gradient.assign(m).muli(alphat).divi(sqrtV);
    }
Calculate the update based on the given gradient

@param gradient  the gradient to get the update for
@param iteration
@return the gradient