Dropout

You saw that dropout is an effective technique to avoid overfitting. Typically, dropout is applied in fully-connected neural networks, or in the fully-connected layers of a convolutional neural network. You are now going to implement dropout and use it on a small fully-connected neural network.

For the first hidden layer use 200 units, for the second hidden layer use 500 units, and for the output layer use 10 units (one for each class). For the activation function, use ReLU. Use .Dropout() with strength 0.5, between the first and second hidden layer. Use the sequential module, with the order being: fully-connected, activation, dropout, fully-connected, activation, fully-connected.

Implement the __init__ method, based on the description of the network in the context.

Exercise

Dropout

Instructions 1/2