Working training loop and testing of trained CharacterModel.

3 files changed, 57 insertions, 73 deletions

diff --git a/src/text_recognizer/networks/__init__.py b/src/text_recognizer/networks/__init__.py
index 4ea5bb3..e6b6946 100644
--- a/src/text_recognizer/networks/__init__.py
+++ b/src/text_recognizer/networks/__init__.py
@@ -1 +1,5 @@
 """Network modules."""
+from .lenet import LeNet
+from .mlp import MLP
+
+__all__ = ["MLP", "LeNet"]
diff --git a/src/text_recognizer/networks/lenet.py b/src/text_recognizer/networks/lenet.py
index 71d247f..2839a0c 100644
--- a/src/text_recognizer/networks/lenet.py
+++ b/src/text_recognizer/networks/lenet.py
@@ -1,5 +1,5 @@
 """Defines the LeNet network."""
-from typing import Callable, Optional, Tuple
+from typing import Callable, Dict, Optional, Tuple
 
 import torch
 from torch import nn
@@ -18,28 +18,37 @@ class LeNet(nn.Module):
 
     def __init__(
         self,
-        channels: Tuple[int, ...],
-        kernel_sizes: Tuple[int, ...],
-        hidden_size: Tuple[int, ...],
-        dropout_rate: float,
-        output_size: int,
+        input_size: Tuple[int, ...] = (1, 28, 28),
+        channels: Tuple[int, ...] = (1, 32, 64),
+        kernel_sizes: Tuple[int, ...] = (3, 3, 2),
+        hidden_size: Tuple[int, ...] = (9216, 128),
+        dropout_rate: float = 0.2,
+        output_size: int = 10,
         activation_fn: Optional[Callable] = None,
+        activation_fn_args: Optional[Dict] = None,
     ) -> None:
         """The LeNet network.
 
         Args:
-            channels (Tuple[int, ...]): Channels in the convolutional layers.
-            kernel_sizes (Tuple[int, ...]): Kernel sizes in the convolutional layers.
+            input_size (Tuple[int, ...]): The input shape of the network. Defaults to (1, 28, 28).
+            channels (Tuple[int, ...]): Channels in the convolutional layers. Defaults to (1, 32, 64).
+            kernel_sizes (Tuple[int, ...]): Kernel sizes in the convolutional layers. Defaults to (3, 3, 2).
             hidden_size (Tuple[int, ...]): Size of the flattend output form the convolutional layers.
-            dropout_rate (float): The dropout rate.
-            output_size (int): Number of classes.
+                Defaults to (9216, 128).
+            dropout_rate (float): The dropout rate. Defaults to 0.2.
+            output_size (int): Number of classes. Defaults to 10.
             activation_fn (Optional[Callable]): The non-linear activation function. Defaults to
                 nn.ReLU(inplace).
+            activation_fn_args (Optional[Dict]): The arguments for the activation function. Defaults to None.
 
         """
         super().__init__()
 
-        if activation_fn is None:
+        self._input_size = input_size
+
+        if activation_fn is not None:
+            activation_fn = getattr(nn, activation_fn)(activation_fn_args)
+        else:
             activation_fn = nn.ReLU(inplace=True)
 
         self.layers = [
@@ -68,26 +77,6 @@ class LeNet(nn.Module):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """The feedforward."""
+        if len(x.shape) == 3:
+            x = x.unsqueeze(0)
         return self.layers(x)
-
-
-# def test():
-#     x = torch.randn([1, 1, 28, 28])
-#     channels = [1, 32, 64]
-#     kernel_sizes = [3, 3, 2]
-#     hidden_size = [9216, 128]
-#     output_size = 10
-#     dropout_rate = 0.2
-#     activation_fn = nn.ReLU()
-#     net = LeNet(
-#         channels=channels,
-#         kernel_sizes=kernel_sizes,
-#         dropout_rate=dropout_rate,
-#         hidden_size=hidden_size,
-#         output_size=output_size,
-#         activation_fn=activation_fn,
-#     )
-#     from torchsummary import summary
-#
-#     summary(net, (1, 28, 28), device="cpu")
-#     out = net(x)
diff --git a/src/text_recognizer/networks/mlp.py b/src/text_recognizer/networks/mlp.py
index 2a41790..d704d99 100644
--- a/src/text_recognizer/networks/mlp.py
+++ b/src/text_recognizer/networks/mlp.py
@@ -1,5 +1,5 @@
 """Defines the MLP network."""
-from typing import Callable, Optional
+from typing import Callable, Dict, List, Optional, Union
 
 import torch
 from torch import nn
@@ -10,45 +10,54 @@ class MLP(nn.Module):
 
     def __init__(
         self,
-        input_size: int,
-        output_size: int,
-        hidden_size: int,
-        num_layers: int,
-        dropout_rate: float,
+        input_size: int = 784,
+        output_size: int = 10,
+        hidden_size: Union[int, List] = 128,
+        num_layers: int = 3,
+        dropout_rate: float = 0.2,
         activation_fn: Optional[Callable] = None,
+        activation_fn_args: Optional[Dict] = None,
     ) -> None:
         """Initialization of the MLP network.
 
         Args:
-            input_size (int): The input shape of the network.
-            output_size (int): Number of classes in the dataset.
-            hidden_size (int): The number of `neurons` in each hidden layer.
-            num_layers (int): The number of hidden layers.
-            dropout_rate (float): The dropout rate at each layer.
-            activation_fn (Optional[Callable]): The activation function in the hidden layers, (default:
-                nn.ReLU()).
+            input_size (int): The input shape of the network. Defaults to 784.
+            output_size (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 (Optional[Callable]): The activation function in the hidden layers. Defaults to
+                None.
+            activation_fn_args (Optional[Dict]): The arguments for the activation function. Defaults to None.
 
         """
         super().__init__()
 
-        if activation_fn is None:
+        if activation_fn is not None:
+            activation_fn = getattr(nn, activation_fn)(activation_fn_args)
+        else:
             activation_fn = nn.ReLU(inplace=True)
 
+        if isinstance(hidden_size, int):
+            hidden_size = [hidden_size] * num_layers
+
         self.layers = [
-            nn.Linear(in_features=input_size, out_features=hidden_size),
+            nn.Linear(in_features=input_size, out_features=hidden_size[0]),
             activation_fn,
         ]
 
-        for _ in range(num_layers):
+        for i in range(num_layers - 1):
             self.layers += [
-                nn.Linear(in_features=hidden_size, out_features=hidden_size),
+                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, out_features=output_size))
+        self.layers.append(
+            nn.Linear(in_features=hidden_size[-1], out_features=output_size)
+        )
 
         self.layers = nn.Sequential(*self.layers)
 
@@ -57,25 +66,7 @@ class MLP(nn.Module):
         x = torch.flatten(x, start_dim=1)
         return self.layers(x)
 
-
-# def test():
-#     x = torch.randn([1, 28, 28])
-#     input_size = torch.flatten(x).shape[0]
-#     output_size = 10
-#     hidden_size = 128
-#     num_layers = 5
-#     dropout_rate = 0.25
-#     activation_fn = nn.GELU()
-#     net = MLP(
-#         input_size=input_size,
-#         output_size=output_size,
-#         hidden_size=hidden_size,
-#         num_layers=num_layers,
-#         dropout_rate=dropout_rate,
-#         activation_fn=activation_fn,
-#     )
-#     from torchsummary import summary
-#
-#     summary(net, (1, 28, 28), device="cpu")
-#
-#     out = net(x)
+    @property
+    def __name__(self) -> str:
+        """Returns the name of the network."""
+        return "mlp"