PyTorch Model

You will use the OOP approach to define the model architecture. Recall that this requires setting up a model class and defining two methods inside it:

.__init__(), in which you define the layers you want to use;
forward(), in which you define what happens to the model inputs once it receives them; this is where you pass inputs through pre-defined layers.

Let's build a model with three linear layers and ReLU activations. After the last linear layer, you need a sigmoid activation instead, which is well-suited for binary classification tasks like our water potability prediction problem. Here's the model defined using nn.Sequential(), which you may be more familiar with:

net = nn.Sequential(
  nn.Linear(9, 16),
  nn.ReLU(),
  nn.Linear(16, 8),
  nn.ReLU(),
  nn.Linear(8, 1),
  nn.Sigmoid(),
)

Let's rewrite this model as a class!

In the .__init__() method, define the three linear layers with dimensions corresponding to the model definition provided and assign them to self.fc1, self.fc2, and self.fc3, respectively.
In the forward() method, pass the model input x through all the layers, remembering to add activations on top of them, similarly how it's already done for the first layer.

Exercise

PyTorch Model

Instructions