Add application file

app.py

@@ -0,0 +1,133 @@
+
+# import libraries.
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import make_regression
+from keras.optimizers import SGD,Adam
+from keras.models import Sequential
+import matplotlib.pyplot as plt
+from keras.layers import Dense
+import streamlit as st
+
+
+
+def model_MLP(X_train,y_train,X_test,layers, nodes, activation, solver, rate, iter):
+    """Creates a MLP model and return the predictions"""
+
+    # Define model.
+    model = Sequential() 
+
+    # Adding first layers.
+    model.add(Dense(nodes, activation=activation, kernel_initializer='he_uniform', input_dim=X_train.shape[1]))
+
+    # Adding remaining hidden layers.
+    for i in range(layers-1):
+        model.add(Dense(nodes, activation=activation, kernel_initializer='he_uniform'))
+
+    # Adding output layer.
+    model.add(Dense(1, activation='linear'))
+
+    # Choose optimizer.
+    if solver == 'adam':    
+        opt = Adam(learning_rate=rate)
+    else:
+        opt = SGD(learning_rate=rate)
+
+    # Compile model.
+    model.compile(optimizer=opt,loss = 'mean_squared_error',metrics=['mean_squared_error'])
+
+    # Fit model.
+    model.fit(X_train, y_train, epochs=iter, verbose=0)
+
+    # Evaluate model.
+    y_hat = model.predict(X_test)
+
+    # Return model.
+    return y_hat
+
+
+if __name__ == '__main__':
+
+    # Adding a title to the app.
+    st.title("Visualize MLPs")
+
+    # Adding a subtitle to the app.  
+    st.subheader('MLP Parameters')
+
+    # Adding two columns to display the sliders for the parameters.
+    left_column, right_column = st.columns(2)
+
+    with left_column:
+
+        # slider for max iterations.
+        iter = st.slider('Max Iteration', min_value=100,max_value= 1000,value=500,step=10)
+        # slider for nodes per layer.          
+        nodes = st.slider('Nodes', min_value=1,max_value= 10,value=5,step=1) 
+        # slider for number of hidden layers.                       
+        layers = st.slider('Hidden Layers', min_value=1,max_value= 10,value=3,step=1) 
+        # selectbox for activation function.              
+        activation = st.selectbox('Activation',('linear','relu','sigmoid','tanh'),index=1)                  
+        
+    with right_column:
+
+        # slider for adding noise.
+        noise = st.slider('Noise', min_value=0,max_value= 100,value=50,step=10)
+        # slider for test-train split.                     
+        split = st.slider('Test-Train Split', min_value=0.1,max_value= 0.9,value=0.3,step=0.1) 
+        # selectbox for solver/optimizer.     
+        solver = st.selectbox('Solver',('adam','sgd'),index=0)           
+        # selectbox for learning rate.                           
+        rate = float(st.selectbox('Learning Rate',('0.001','0.003','0.01','0.03','0.1','0.3','1.0'),index=3))   
+
+    # Generating regression data.
+    X, y = make_regression(n_samples=500, n_features=1, noise=noise,random_state=42,bias=3)
+
+    # Split data into training and test sets.    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=split,random_state=42)
+
+    # Plotting the Prediction data.
+    # creating a container to display the graphs.
+    with st.container():
+
+        # Adding a subheader to the container.
+        st.subheader('Predictions')
+
+        # Adding two columns to display the graphs.
+        left_graph, right_graph = st.columns(2)
+
+        with left_graph:
+
+            # Plotting the training data.
+            st.write('Training Data set')
+
+            fig1, ax1 = plt.subplots(1)
+            ax1.scatter(X_train, y_train, label='train',color='blue',alpha=0.6,edgecolors='black')
+
+            # setting the labels and title of the graph.
+            ax1.set_xlabel('X')
+            ax1.set_ylabel('y')
+            ax1.set_title('Training Data set')
+            ax1.legend()
+            
+            # write the graph to the app.
+            st.pyplot(fig1)
+
+        with right_graph:
+
+            # Plotting the test data.
+            st.write('Test Data set')
+
+            # Predicting the test data.
+            y_hat = model_MLP(X_train,y_train,X_test,layers, nodes, activation, solver, rate, iter)
+
+            fig2, ax2 = plt.subplots(1)
+            ax2.scatter(X_test, y_test, label='test',color='blue',alpha=0.4)
+            ax2.scatter(X_test, y_hat, label='prediction',c='red',alpha=0.6,edgecolors='black')
+
+            # setting the labels and title of the graph.
+            ax2.set_xlabel('X')
+            ax2.set_ylabel('y')
+            ax2.set_title('Test Data set')
+            ax2.legend()
+            
+            # write the graph to the app.
+            st.pyplot(fig2)