Update pages/1_User_Defined_DataLab.py

pages/1_User_Defined_DataLab.py

@@ -3,6 +3,12 @@ from sklearn.datasets import make_classification
 import pandas as pd
 import seaborn as sns
 import matplotlib.pyplot as plt
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, InputLayer
+from tensorflow.keras.optimizers import SGD
+from sklearn.model_selection import train_test_split
+from mlxtend.plotting import plot_decision_regions
+import numpy as np
 
 st.title("🧠 Neural Network Playground - Custom Dataset")
 
@@ -13,7 +19,8 @@ random_state = st.number_input("Random State", value=42)
 
 # Generate synthetic 2-feature data
 X, y = make_classification(n_samples=n_samples, n_features=2, n_redundant=0,
-                           n_informative=2, n_clusters_per_class=1, flip_y=noise, random_state=random_state,class_sep=4)
+                           n_informative=2, n_clusters_per_class=1, flip_y=noise, 
+                           random_state=random_state, class_sep=4)
 
 df = pd.DataFrame(X, columns=["X1", "X2"])
 df["label"] = y
@@ -21,12 +28,49 @@ df["label"] = y
 st.write("### 📄 Preview of Generated Data")
 st.dataframe(df.head())
 
-# Save to session state
 st.session_state['X'] = X
 st.session_state['y'] = y
 
-# Plot the data using seaborn
+# Scatter plot
 st.write("### 🎯 Feature Scatter Plot by Class Label")
 fig, ax = plt.subplots()
 sns.scatterplot(data=df, x="X1", y="X2", hue="label", palette="deep", ax=ax)
-st.pyplot(fig)
+st.pyplot(fig)
+
+# Sidebar - Network Config
+st.sidebar.title("🛠️ Network Configuration")
+n_layers = st.sidebar.slider("Number of Hidden Layers", 1, 4, 2)
+hidden_activation = st.sidebar.selectbox("Activation for Hidden Layers", ["tanh", "sigmoid"])
+
+hidden_config = []
+for i in range(n_layers):
+    units = st.sidebar.slider(f"Neurons in Layer {i+1}", 1, 10, 4)
+    hidden_config.append(units)
+
+learning_rate = st.sidebar.slider("Learning Rate", 0.0001, 1.0, 0.1, step=0.0001, format="%.4f")
+batch_size = st.sidebar.slider("Batch Size", 1, 1000, 200)
+epochs = st.sidebar.slider("Epochs", 100, 2000, 350, step=50)
+
+# Train model
+if st.button("🚀 Train Model"):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    model = Sequential()
+    model.add(InputLayer(shape=(2,)))
+    for units in hidden_config:
+        model.add(Dense(units, activation=hidden_activation))
+    model.add(Dense(1, activation="sigmoid"))
+
+    model.compile(optimizer=SGD(learning_rate=learning_rate),
+                  loss='binary_crossentropy',
+                  metrics=['accuracy'])
+
+    history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=0)
+
+    # Plot decision region
+    st.write("### 🧭 Decision Boundary")
+    fig2, ax2 = plt.subplots()
+    plot_decision_regions(X=X, y=y.astype(int), 
+                          clf=lambda X_: (model.predict(X_) > 0.5).astype(int).flatten(),
+                          legend=2, ax=ax2)
+    st.pyplot(fig2)