more models added

app.py

@@ -25,8 +25,23 @@ def create_datasets(n_samples=1500):
 
 
 # Function to create a simple one-layer neural network
-def create_model(input_shape):
-    model = Sequential([Dense(1, input_shape=input_shape, activation="sigmoid")])
+def create_model(input_shape, layers):
+    model = Sequential()
+    # Add layers based on the selection
+    if layers == 'one-layer neural network':
+        model.add(Dense(1, input_shape=input_shape, activation="sigmoid"))
+    elif layers == 'three-layer neural network':
+        model.add(Dense(128, input_shape=input_shape, activation="relu"))
+        model.add(Dense(128, activation="relu"))
+        model.add(Dense(128, activation="relu"))
+        model.add(Dense(1, activation="sigmoid"))
+    elif layers == 'five-layer neural network':
+        model.add(Dense(128, input_shape=input_shape, activation="relu"))
+        model.add(Dense(128, activation="relu"))
+        model.add(Dense(128, activation="relu"))
+        model.add(Dense(128, activation="relu"))
+        model.add(Dense(128, activation="relu"))
+        model.add(Dense(1, activation="sigmoid"))
     return model
 
 
@@ -106,6 +121,12 @@ def main():
     st.sidebar.header('Configuration')
     n_samples = st.sidebar.slider('Number of Samples', min_value=500, max_value=3000, value=1500, step=100)
     threshold = st.sidebar.slider('Threshold', min_value=0.0, max_value=1.0, value=0.1, step=0.01)
+
+    # Sidebar: model selection
+    model_option = st.sidebar.selectbox(
+        'Choose the neural network model:',
+        ('one-layer neural network', 'three-layer neural network', 'five-layer neural network')
+    )
     epochs = st.sidebar.slider('Number of Epochs', min_value=10, max_value=100, value=20, step=5)
 
     # Generate datasets
@@ -154,7 +175,7 @@ def main():
             )
 
             # Train with MSE loss
-            model_mse = create_model((2,))
+            model_mse = create_model((2,), model_option)
             model_mse.compile(
                 optimizer="sgd", loss="binary_crossentropy", metrics=["accuracy"]
             )
@@ -175,7 +196,7 @@ def main():
                 st.pyplot(fig)
 
             # Train with custom loss
-            model_custom = create_model((2,))
+            model_custom = create_model((2,), model_option)
             model_custom.compile(
                 optimizer="sgd",
                 loss=soft_loss_wrapper(threshold),