1 files changed, 0 insertions, 73 deletions
diff --git a/text_recognizer/networks/mlp.py b/text_recognizer/networks/mlp.py
deleted file mode 100644
index 1101912..0000000
--- a/text_recognizer/networks/mlp.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""Defines the MLP network."""
-from typing import Callable, Dict, List, Optional, Union
-
-from einops.layers.torch import Rearrange
-import torch
-from torch import nn
-
-from text_recognizer.networks.util import activation_function
-
-
-class MLP(nn.Module):
-    """Multi layered perceptron network."""
-
-    def __init__(
-        self,
-        input_size: int = 784,
-        num_classes: int = 10,
-        hidden_size: Union[int, List] = 128,
-        num_layers: int = 3,
-        dropout_rate: float = 0.2,
-        activation_fn: str = "relu",
-    ) -> None:
-        """Initialization of the MLP network.
-
-        Args:
-            input_size (int): The input shape of the network. Defaults to 784.
-            num_classes (int): Number of classes in the dataset. Defaults to 10.
-            hidden_size (Union[int, List]): The number of `neurons` in each hidden layer. Defaults to 128.
-            num_layers (int): The number of hidden layers. Defaults to 3.
-            dropout_rate (float): The dropout rate at each layer. Defaults to 0.2.
-            activation_fn (str): Name of the activation function in the hidden layers. Defaults to
-                relu.
-
-        """
-        super().__init__()
-
-        activation_fn = activation_function(activation_fn)
-
-        if isinstance(hidden_size, int):
-            hidden_size = [hidden_size] * num_layers
-
-        self.layers = [
-            Rearrange("b c h w -> b (c h w)"),
-            nn.Linear(in_features=input_size, out_features=hidden_size[0]),
-            activation_fn,
-        ]
-
-        for i in range(num_layers - 1):
-            self.layers += [
-                nn.Linear(in_features=hidden_size[i], out_features=hidden_size[i + 1]),
-                activation_fn,
-            ]
-
-            if dropout_rate:
-                self.layers.append(nn.Dropout(p=dropout_rate))
-
-        self.layers.append(
-            nn.Linear(in_features=hidden_size[-1], out_features=num_classes)
-        )
-
-        self.layers = nn.Sequential(*self.layers)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """The feedforward pass."""
-        # If batch dimenstion is missing, it needs to be added.
-        if len(x.shape) < 4:
-            x = x[(None,) * (4 - len(x.shape))]
-        return self.layers(x)
-
-    @property
-    def __name__(self) -> str:
-        """Returns the name of the network."""
-        return "mlp"