Update app.py

app.py

@@ -58,88 +58,90 @@ with st.sidebar:
             
         with col4:
             patience = st.number_input("Patience",3,20,step=1)
+
+    btn=st.sidebar.button("Train")
             
     
     
     
-    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)
+if btn:
+    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)
 
-        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
 
 
-        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 es == "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)
     
-        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 es == "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=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)
+    hist=model.fit(x_train,y_train,epochs=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)