Add BatchNorm and LayerNorm to the simple CNN, with explanation.

pybiscus/ml/models/cnn/cnn.py

@@ -3,13 +3,38 @@
 
 
 def net(input_shape: int = 3, mid_shape: int = 6, n_classes: int = 10):
+    """Create a simple CNN module.
+
+    This module is definitely NOT here to provide a powerful architecture,
+    but only to provide a simple example, to use out-of-the-box with Pybiscus.
+    The presence of two BatchNorm and one LayerNorm instances are only for
+    educational purposes. BatchNorms are known to have a lot of issues, in particular
+    with the way they handle their internal state. To avoid issues, please turn
+    the parameter 'track_running_stats' to False, like below.
+
+    Parameters
+    ----------
+    input_shape : int, optional
+        the number of channels of the input, by default 3
+    mid_shape : int, optional
+        number of channels at mid module, by default 6
+    n_classes : int, optional
+        number of classes, by default 10
+
+    Returns
+    -------
+    nn.Sequential
+    """
     model = nn.Sequential(
+        nn.BatchNorm2d(num_features=input_shape, track_running_stats=False),
         nn.Conv2d(input_shape, mid_shape, 5),
+        nn.LayerNorm(normalized_shape=[mid_shape, 28, 28]),
         nn.ReLU(),
         nn.MaxPool2d(2, 2),
         nn.Conv2d(mid_shape, 16, 5),
         nn.ReLU(),
         nn.MaxPool2d(2, 2),
+        nn.BatchNorm2d(num_features=16, track_running_stats=False),
         Rearrange("b c h w -> b (c h w)"),
         nn.Linear(16 * 5 * 5, 120),
         nn.ReLU(),