Pytorch for Neural Networks Part 2: Initializing Weights and Biases

In the

You can see the weights and biases shown in the diagram above. Let us now add them to our code.

We start with the basic neural network class:

class MyBasicNN(nn.Module):

    def __init__(self):

        super().__init__()

Our first weight has the value 1.70.

We can represent it like this:

class MyBasicNN(nn.Module):

    def __init__(self):

        super().__init__()

        self.w00 = nn.Parameter(torch.tensor(1.7), requires_grad=False)

Here, we initialize a new variable called w00 and make it a neural network parameter.

When we define a weight as a parameter, PyTorch treats it as part of the neural network and gives us the option to optimize it during training.

Since this value is stored as a tensor, the neural network can take advantage of features such as:

• automatic differentiation


• accelerated mathematical operations

If you are unfamiliar with tensors, check out my

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