Experimenting with dropout
Dropout helps prevent overfitting by randomly setting some output values to zero during training. In this exercise, you'll build a simple neural network with dropout and observe how it behaves in training and evaluation modes.
torch.nn
package is preloaded as nn
, and features
is already defined for you.
This exercise is part of the course
Introduction to Deep Learning with PyTorch
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Model with Dropout
model = nn.Sequential(
nn.Linear(8, 6),
nn.Linear(6, 4),
____)
# Forward pass in training mode (Dropout active)
model.____
output_train = ____