# Models: Wine Quality

class `model.``WineModel`
  Description:
    The model is a vanilla feedforward neural network that consists of 5 layers. Each layer is followed with a batch normalization layer and a Relu function.
  Dataset:
    Wine Quality.
  Model:
    Group 1:
      Linear Layer:
        input features = 11, output features = 100
      Batch normalization:
        input features = 100

      Relu

    Group 2:
      Linear Layer:
        input features = 100, output features = 50
      Batch normalization:
        input features = 50

      Relu

    Group 3:
      Linear Layer:
        input features = 50, output features = 20
      Batch normalization:
        input features = 20

      Relu

    Group 4:
      Linear Layer:
        input features = 20, output features = 10
      Batch normalization:
        input features = 10

      Relu

      Group 5: Linear Layer:

      > input features = 20, output features = 1

  Args:
    None.
  `forward`(x)

    Defines the computation performed at every call.

    Should be overridden by all subclasses.

    Note

    Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.