Update app.py

app.py

@@ -27,7 +27,7 @@ with st.sidebar:
     noise = st.slider("Noise",0.0,1.0,0.1)
     test_size = st.slider("Test Size",0.1,0.5,0.05)
 
-    st.header("ANN Hyperparameters")
+    st.header("Model Hyperparameters")
     hl = st.number_input("Hidden Layers",1,10,step=1)
     numbers = st.text_input("Neurons for each hidden layer" ,placeholder="e.g. 8,16,32")
     input_func = lambda x: [int(i.strip()) for i in x.split(",") if i.strip() != ""]
@@ -39,7 +39,7 @@ with st.sidebar:
     col1, col2 = st.columns(2)
 
     with col1:
-        af = st.selectbox("Activation Function",["Sigmoid","Tanh","Relu"])
+        af = st.selectbox("Activation Function",["sigmoid","tanh","relu"],index=2)
     with col2:
         lr = st.selectbox("Learning Rate",[0.1,0.01,0.02,0.2])
 
@@ -59,11 +59,84 @@ with st.sidebar:
         with col4:
             patience = st.number_input("Patience",3,20,step=1)
             
-    # if st.sidebar.button("start trainning"):
+    if st.sidebar.button("Train"):
+        if dataset:
+            if dataset=="Circles":
+                x,y=make_circles(n_samples=1000,noise=noise,random_state=42,factor=0.1)
+            elif dataset=="moons":
+                x,y=make_moons(n_samples=1000,noise=noise,random_state=42)
+            elif dataset == "Blobs":
+                x,y=make_blobs(n_samples=1000, centers=2, cluster_std=noise, random_state=42)
 
-        
+        x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=test_size,random_state=42,stratify=y)
+
+        std = StandardScaler()
+        x_train = std.fit_transform(x_train)
+        x_test = std.transform(x_test)
+
+
+        if reg == "L1":
+            reg = l1(reg_rate)
+        elif reg == "L2":
+            reg = l2(reg_rate)
+        elif reg == "ElasticNet":
+            reg = l1_l2(l1=reg_rate, l2=reg_rate)
+        else:
+            reg=None
+
+
+    
+        model = Sequential()
+        model.add(InputLayer(shape=(2,)))
+        if hl == len(nn):
+            for i in range(0,len(nn)):
+                model.add(Dense(units=nn[i],activation=af,kernel_regularizer=reg))
+
+        model.add(Dense(units=1,activation="sigmoid",kernel_regularizer=reg))
+        sgd=SGD(learning_rate=lr)
+        model.compile(loss="binary_crossentropy",optimizer=sgd,metrics=["accuracy"])
+        train_size=round(x_train.shape[0]-x_train.shape[0]*0.2)
 
+        callbacks = []
+        if early_stop_option == "Yes":
+            es = EarlyStopping(
+                monitor="val_loss",
+                #min_delta= min_delta if min_delta else 0.001,
+                min_delta = min_delta
+                patience=patience,
+                verbose=1,
+                restore_best_weights=True,
+                start_from_epoch=50
+            )
+            callbacks.append(es)
         
+        hist=model.fit(x_train,y_train,epochs=n_epochs,batch_size=train_size,validation_data=(x_test, y_test),verbose=False)
+
+    # --- Neural Network Diagram ---
+    st.subheader("Neural Network Architecture")
+    stringlist = []
+    model.summary(print_fn=lambda x: stringlist.append(x))
+    summary_str = "\n".join(stringlist)
+    st.text(summary_str)
+
+
+    # --- Plotting Decision region ---
+    st.subheader("Decision Region")
+    fig1, ax1 = plt.subplots(figsize=(6, 5))
+    plot_decision_regions(X=x_test, y=y_test.astype(np.int_), clf=model, ax=ax1)
+    ax1.set_title("Decision Regions", fontsize=12, weight="bold")
+    st.pyplot(fig1)
+
+     # --- Plot 2: Training vs Validation Loss ---
+    st.subheader("Training vs Validation Loss")
+    fig2, ax2 = plt.subplots(figsize=(6, 5))
+    ax2.plot(hist.history["loss"], label="Training Loss", linewidth=2)
+    ax2.plot(hist.history["val_loss"], label="Validation Loss", linewidth=2, linestyle="--")
+    ax2.set_xlabel("Epochs")
+    ax2.set_ylabel("Loss")
+    ax2.legend()
+    ax2.grid(alpha=0.3)
+    st.pyplot(fig2)