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# 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) |