Varying the loss optimizer to improve network accuracy

So far, in the previous recipes, we considered the loss optimizer to be the Adam optimizer. However, there are multiple other variants of optimizers, and a change in the optimizer is likely to impact the speed with which the model learns to fit the input and the output.

In this recipe, we will understand the impact of changing the optimizer on model accuracy.

Getting ready

To understand the impact of varying the optimizer on network accuracy, let's contrast the scenario laid out in previous sections (which was the Adam optimizer) with using a stochastic gradient descent optimizer in this section, while reusing the same MNIST training and test datasets that were scaled...