Abstract
Deep learning is the process of determining parameters that reduce the cost function derived from the dataset. The optimization in neural net-works at the time is known as the optimal parameters. To solve optimization, it initialize the parameters during the optimization process. There should be no variation in the cost function parameters at the global minimum. The momentum technique is a parameters optimization approach; however, it has difficulties stopping the parameter when the cost function value fulfills the global minimum (non-stop problem). Moreover, existing approaches use techniques; the learning rate is reduced during the iteration period. These techniques are monotonically reducing at a steady rate over time; our goal is to make the learning rate parameters. We present a method for determining the best parameters that adjust the learning rate in response to the cost function value. As a result, after the cost function has been optimized, the process of the rate Schedule is complete. This approach is shown to ensure convergence to the optimal parameters. This indicates that our strategy minimizes the cost function (or effective learning). The momentum approach is used in the proposed method. To solve the Momentum approach non-stop problem, we use the cost function of the parameter in our proposed method. As a result, this learning technique reduces the quantity of the parameter due to the impact of the cost function parameter. To verify that the learning works to test the strategy, we employed proof of convergence and empirical tests using current methods and the results are obtained using Python.