Update model.py

model.py

@@ -0,0 +1,38 @@
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras.regularizers import l2
+
+def build_model(input_shape, num_classes):
+    model = Sequential([
+        Conv2D(32, (3, 3), activation='relu',padding='same', input_shape=input_shape,kernel_regularizer='l2'),
+        BatchNormalization(),
+        MaxPooling2D((2, 2)),
+        
+        Conv2D(64, (3, 3), activation='relu',padding='same',kernel_regularizer='l2'),
+        BatchNormalization(),
+        MaxPooling2D((2, 2)),
+        
+        Conv2D(128, (3, 3), activation='relu',padding='same',kernel_regularizer='l2'),
+        BatchNormalization(),
+        MaxPooling2D((2, 2)),
+        
+        Conv2D(256, (3, 3), activation='relu',padding='same',kernel_regularizer='l2'),
+        BatchNormalization(),
+        MaxPooling2D((2, 2)),
+        
+        Conv2D(256, (3, 3), activation='relu',padding='same',kernel_regularizer='l2'),
+        BatchNormalization(),
+        MaxPooling2D((2, 2)),
+        
+        Flatten(),
+        Dense(512, activation='relu',kernel_regularizer='l2'),
+        #BatchNormalization(),
+        Dropout(0.5),
+        Dense(256, activation='relu',kernel_regularizer='l2'),
+        #BatchNormalization(),
+        Dropout(0.5),
+        Dense(3, activation='softmax')  # Assuming 3 classes
+    ])
+
+    return model