Update

app.py

@@ -24,30 +24,22 @@ _tf_model = None
 
 
 class CNN_Torch(nn.Module):
-    """
-    CNN PyTorch léger pour images RGB.
-    Entrée : (B, 3, 150, 150)
-    Sortie : logits transformés en log_softmax
-    """
-    def __init__(self, num_classes: int = 6, dropout: float = 0.5):
+    def __init__(self, num_classes=6):
         super().__init__()
 
         self.features = nn.Sequential(
-            # Block 1
-            nn.Conv2d(3, 32, kernel_size=3, padding=1, bias=False),
+            nn.Conv2d(3, 32, kernel_size=3, padding=1),
             nn.BatchNorm2d(32),
             nn.ReLU(inplace=True),
             nn.MaxPool2d(2),
 
-            # Block 2
-            nn.Conv2d(32, 64, kernel_size=3, padding=1, bias=False),
+            nn.Conv2d(32, 64, kernel_size=3, padding=1),
             nn.BatchNorm2d(64),
             nn.ReLU(inplace=True),
             nn.MaxPool2d(2),
             nn.Dropout2d(0.1),
 
-            # Block 3
-            nn.Conv2d(64, 128, kernel_size=3, padding=1, bias=False),
+            nn.Conv2d(64, 128, kernel_size=3, padding=1),
             nn.BatchNorm2d(128),
             nn.ReLU(inplace=True),
             nn.MaxPool2d(2),
@@ -60,7 +52,7 @@ class CNN_Torch(nn.Module):
             nn.Flatten(),
             nn.Linear(128, 256),
             nn.ReLU(inplace=True),
-            nn.Dropout(dropout),
+            nn.Dropout(0.5),
             nn.Linear(256, num_classes),
         )