Update app.py

app.py

@@ -105,42 +105,37 @@ for images, _ in train_ds.take(1):
         plt.imshow(augmented_images[0].numpy().astype("uint8"))
         plt.axis("off")
 
-# Define a deeper CNN model with more regularization techniques
+# Define a deeper CNN model with softmax activation in the final layer
 num_classes = len(class_names)
 model = Sequential([
     data_augmentation,
     layers.Rescaling(1./255),
     
-    layers.Conv2D(32, 3, padding='same', activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
-    layers.BatchNormalization(),
+    layers.Conv2D(32, 3, padding='same', activation='relu'),
     layers.MaxPooling2D(),
     
-    layers.Conv2D(64, 3, padding='same', activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
-    layers.BatchNormalization(),
+    layers.Conv2D(64, 3, padding='same', activation='relu'),
     layers.MaxPooling2D(),
     
-    layers.Conv2D(128, 3, padding='same', activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
-    layers.BatchNormalization(),
+    layers.Conv2D(128, 3, padding='same', activation='relu'),
     layers.MaxPooling2D(),
     
-    layers.Conv2D(256, 3, padding='same', activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
-    layers.BatchNormalization(),
+    layers.Conv2D(256, 3, padding='same', activation='relu'),
     layers.MaxPooling2D(),
     
-    layers.Conv2D(512, 3, padding='same', activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
-    layers.BatchNormalization(),
+    layers.Conv2D(512, 3, padding='same', activation='relu'),
     layers.MaxPooling2D(),
     
     layers.Dropout(0.5),
     layers.Flatten(),
     
-    layers.Dense(256, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
+    layers.Dense(256, activation='relu'),
     layers.Dropout(0.5),
     
     layers.Dense(num_classes, activation='softmax', name="outputs")
 ])
 
-model.compile(optimizer=keras.optimizers.Adam(learning_rate=1e-4),
+model.compile(optimizer='adam',
               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
               metrics=['accuracy'])