Models

src.pytorch.models

LinearRegressionModel

Bases: Module

Source code in src/pytorch/models.py

class LinearRegressionModel(nn.Module):
    """
    Linear regression model for Chapter 1
    """
    def __init__(self):
        """Init constructor
        """

        # call __init__ from nn.Module
        super().__init__()

        # set random seed
        torch.random.manual_seed(42)

        # initialize parameters with random values and require gradient
        self.bias = nn.Parameter(torch.randn(1, requires_grad=True, dtype=torch.float))
        self.weight = nn.Parameter(torch.randn(1, requires_grad=True, dtype=torch.float))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """forward model

        Method that computes runs data through
        the neural net

        Args:
            x: Input data

        Returns:
            preds: prediction of the neural net
        """

        # computation of the neural net
        preds = self.weight * x + self.bias
        return preds

__init__()

Source code in src/pytorch/models.py

def __init__(self):
    """Init constructor
    """

    # call __init__ from nn.Module
    super().__init__()

    # set random seed
    torch.random.manual_seed(42)

    # initialize parameters with random values and require gradient
    self.bias = nn.Parameter(torch.randn(1, requires_grad=True, dtype=torch.float))
    self.weight = nn.Parameter(torch.randn(1, requires_grad=True, dtype=torch.float))

forward(x)

Parameters:

Name	Type	Description	Default
x	Tensor	Input data	required

Returns:

Name	Type	Description
preds	Tensor	prediction of the neural net

Source code in src/pytorch/models.py

def forward(self, x: torch.Tensor) -> torch.Tensor:
    """forward model

    Method that computes runs data through
    the neural net

    Args:
        x: Input data

    Returns:
        preds: prediction of the neural net
    """

    # computation of the neural net
    preds = self.weight * x + self.bias
    return preds