# Module `models.femnist`

CNN model for FEMNIST Dataset.

Expand source code

    #!/usr/bin/env python3

    """
    CNN model for FEMNIST Dataset.
    """
    from torch import nn

    # Import PyTorch layers, activations and more
    import torch.nn.functional as F

    class FEMNIST(nn.Module):
        def __init__(self, channel_1=32, channel_2=64, num_classes=62):
            super(FEMNIST, self).__init__()

            self.conv1 = nn.Conv2d(1, channel_1, (5, 5))
            self.conv2 = nn.Conv2d(channel_1, channel_2, (5, 5))

            # Fully connected layer from 16 * channel_2 to num_classes units
            self.fc = nn.Linear(16 * channel_2, num_classes)

        def forward(self, x):
            out = F.relu(F.max_pool2d(self.conv1(x), 2))
            out = F.relu(F.max_pool2d(self.conv2(out), 2))
            out = self.fc(nn.Flatten(out))
            return out

    # TODO: Any way we can actually have an useful pretrained argument here?
    def femnist(pretrained=False, num_classes=62):
        return FEMNIST(num_classes=num_classes)

    def minifemnist(pretrained=False, num_classes=62):
        return FEMNIST(num_classes=num_classes, channel_1=10, channel_2=20)

## Functions

`def femnist(pretrained=False, num_classes=62)`
  Expand source code

      def femnist(pretrained=False, num_classes=62):
          return FEMNIST(num_classes=num_classes)

`def minifemnist(pretrained=False, num_classes=62)`
  Expand source code

      def minifemnist(pretrained=False, num_classes=62):
          return FEMNIST(num_classes=num_classes, channel_1=10, channel_2=20)

## Classes

`class FEMNIST (channel_1=32, channel_2=64, num_classes=62)`

  Base class for all neural network modules.

  Your models should also subclass this class.

  Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

      import torch.nn as nn
      import torch.nn.functional as F

      class Model(nn.Module):
          def __init__(self):
              super(Model, self).__init__()
              self.conv1 = nn.Conv2d(1, 20, 5)
              self.conv2 = nn.Conv2d(20, 20, 5)

          def forward(self, x):
              x = F.relu(self.conv1(x))
              return F.relu(self.conv2(x))

  Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:`to`, etc.

  :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

  Initializes internal Module state, shared by both nn.Module and ScriptModule.

  Expand source code

      class FEMNIST(nn.Module):
          def __init__(self, channel_1=32, channel_2=64, num_classes=62):
              super(FEMNIST, self).__init__()

              self.conv1 = nn.Conv2d(1, channel_1, (5, 5))
              self.conv2 = nn.Conv2d(channel_1, channel_2, (5, 5))

              # Fully connected layer from 16 * channel_2 to num_classes units
              self.fc = nn.Linear(16 * channel_2, num_classes)

          def forward(self, x):
              out = F.relu(F.max_pool2d(self.conv1(x), 2))
              out = F.relu(F.max_pool2d(self.conv2(out), 2))
              out = self.fc(nn.Flatten(out))
              return out

  ### Ancestors

  - torch.nn.modules.module.Module

  ### Class variables

  `var dump_patches : bool`
  `var training : bool`

  ### Methods

  `def forward(self, x) ‑> Callable[..., Any]`

    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 :class:`Module` instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

    Expand source code

        def forward(self, x):
            out = F.relu(F.max_pool2d(self.conv1(x), 2))
            out = F.relu(F.max_pool2d(self.conv2(out), 2))
            out = self.fc(nn.Flatten(out))
            return out