Update app.py

app.py

@@ -184,17 +184,25 @@ def CNNModel(trainX, trainy, testX, testy, epochs=1000, batch_size=64, learning_
     model = Sequential()
     
     # Convolutional layers
+    model.add(Conv1D(512, kernel_size=3, activation='relu', input_shape=(trainX.shape[1], 1), kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(MaxPooling1D(pool_size=2))
+    model.add(Dropout(dropout_rate))
+    
     model.add(Conv1D(256, kernel_size=3, activation='relu', input_shape=(trainX.shape[1], 1), kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
     model.add(MaxPooling1D(pool_size=2))
     model.add(Dropout(dropout_rate))
     
     model.add(Conv1D(128, kernel_size=3, activation='relu', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
     model.add(MaxPooling1D(pool_size=2))
+    model.add(Dropout(dropout_rate))
+
+     model.add(Conv1D(64, kernel_size=3, activation='relu', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(MaxPooling1D(pool_size=2))
     model.add(Dropout(dropout_rate))
     
     # Flatten and Dense layers
     model.add(Flatten())
-    model.add(Dense(64, kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(Dense(32, kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
     model.add(LeakyReLU(alpha=0.1))
     model.add(Dropout(dropout_rate))