public static boolean checkGradients(ComputationGraph graph, double epsilon, double maxRelError,
                    double minAbsoluteError, boolean print, boolean exitOnFirstError, INDArray[] inputs,
                    INDArray[] labels) {
        return checkGradients(graph, epsilon, maxRelError, minAbsoluteError, print, exitOnFirstError, inputs, labels, null, null, null);
    }
public static boolean checkGradients ( ComputationGraph graph , double epsilon , double maxRelError , double minAbsoluteError , boolean print , boolean exitOnFirstError , INDArray [ ] inputs , INDArray [ ] labels ) { return checkGradients ( graph , epsilon , maxRelError , minAbsoluteError , print , exitOnFirstError , inputs , labels , null , null , null ) ; }
Check backprop gradients for a ComputationGraph
@param graph ComputationGraph to test. This must be initialized.
@param epsilon Usually on the order of 1e-4 or so.
@param maxRelError Maximum relative error. Usually < 0.01, though maybe more for deep networks
@param minAbsoluteError Minimum absolute error to cause a failure. Numerical gradients can be non-zero due to precision issues.
For example, 0.0 vs. 1e-18: relative error is 1.0, but not really a failure
@param print Whether to print full pass/failure details for each parameter gradient
@param exitOnFirstError If true: return upon first failure. If false: continue checking even if
one parameter gradient has failed. Typically use false for debugging, true for unit tests.
@param inputs Input arrays to use for forward pass. May be mini-batch data.
@param labels Labels/targets (output) arrays to use to calculate backprop gradient. May be mini-batch data.
@return true if gradients are passed, false otherwise.