Doc update

CollaborativeCoding/dataloaders/mnist_4_9.py

@@ -19,6 +19,10 @@ class MNISTDataset4_9(Dataset):
         Array of indices spcifying which samples to load. This determines the samples used by the dataloader.
     train : bool, optional
         Whether to train the model or not, by default False
+    transorm : callable, optional
+        Transform to apply to the images, by default None
+    nr_channels : int, optional
+        Number of channels in the images, by default 1
     """
 
     def __init__(

CollaborativeCoding/metrics/precision.py

@@ -4,14 +4,14 @@
 
 
 class Precision(nn.Module):
-    """Metric module for precision. Can calculate precision both as a mean of precisions or as brute function of true positives and false positives.
+    """Metric module for precision. Can calculate both the micro- and macro-averaged precision.
 
     Parameters
     ----------
     num_classes : int
         Number of classes in the dataset.
-    micro_averaging : bool
-        Wheter to compute the micro or macro precision (default False)
+    macro_averaging : bool
+        Performs macro-averaging if True, otherwise micro-averaging.
     """
 
     def __init__(self, num_classes: int, macro_averaging: bool = False):
@@ -23,19 +23,15 @@ def __init__(self, num_classes: int, macro_averaging: bool = False):
         self.y_pred = []
 
     def forward(self, y_true: torch.tensor, logits: torch.tensor) -> torch.tensor:
-        """Compute precision of model
+        """Add true and predicted values to the class-global lists.
 
         Parameters
         ----------
         y_true : torch.tensor
             True labels
-        y_pred : torch.tensor
+        logits : torch.tensor
             Predicted labels
 
-        Returns
-        -------
-        torch.tensor
-            Precision score
         """
         y_pred = logits.argmax(dim=-1)
 
@@ -100,6 +96,13 @@ def _macro_avg_precision(
         return torch.nanmean(tp / (tp + fp))
 
     def __returnmetric__(self):
+        """Return the micro- or macro-averaged precision.
+
+        Returns
+        -------
+        torch.tensor
+            Micro- or macro-averaged precision
+        """
         if self.y_true == [] and self.y_pred == []:
             return np.nan
         elif self.y_true == [] or self.y_pred == []:

CollaborativeCoding/models/johan_model.py

@@ -4,28 +4,32 @@
 Multi-layer perceptron model for image classification.
 """
 
-# class NeuronLayer(nn.Module):
-#     def __init__(self, in_features, out_features):
-#         super().__init__()
-
-#         self.fc = nn.Linear(in_features, out_features)
-#         self.relu = nn.ReLU()
-
-#     def forward(self, x):
-#         x = self.fc(x)
-#         x = self.relu(x)
-#         return x
-
 
 class JohanModel(nn.Module):
     """Small MLP model for image classification.
 
     Parameters
     ----------
-    in_features : int
-        Numer of input features.
+    image_shape : tuple(int, int, int)
+        Shape of the input image (C, H, W).
     num_classes : int
         Number of classes in the dataset.
+
+    Processing Images
+    -----------------
+    Input: (N, C, H, W)
+        N: Batch size
+        C: Number of input channels
+        H: Height of the input image
+        W: Width of the input image
+
+    Example:
+    Grayscale images (like MNIST) have C = 1.
+    Input shape: (N, 1, 28, 28)
+    fc1 Output shape: (N, 77)
+    fc2 Output shape: (N, 77)
+    fc3 Output shape: (N, 77)
+    fc4 Output shape: (N, num_classes)
     """
 
     def __init__(self, image_shape, num_classes):

main.py

@@ -1,11 +1,11 @@
 import numpy as np
 import torch as th
 import torch.nn as nn
-import wandb
 from torch.utils.data import DataLoader
 from torchvision import transforms
 from tqdm import tqdm
 
+import wandb
 from CollaborativeCoding import (
     MetricWrapper,
     createfolders,