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Despite their massive success, training successful deep neural networks stilllargely relies on experimentally choosing an architecture, hyper-parameters,initialization, and training mechanism. In this work, we focus on determiningthe success of standard gradient descent method for training deep neuralnetworks on a specified dataset, architecture, and initialization (DAI)combination. Through extensive systematic experiments, we show that theevolution of singular values of the matrix obtained from the hidden layers of aDNN can aid in determining the success of gradient descent technique to train aDAI, even in the absence of validation labels in the supervised learningparadigm. This phenomenon can facilitate early give-up, stopping the trainingof neural networks which are predicted to not generalize well, early in thetraining process. Our experimentation across multiple datasets, architectures,and initializations reveals that the proposed scores can more accuratelypredict the success of a DAI than simply relying on the validation accuracy atearlier epochs to make a judgment.

Predicting the success of Gradient Descent for a particular Dataset-Architecture-Initialization (DAI)