"""
To know more or get code samples, please visit my website:
https://morvanzhou.github.io/tutorials/
Or search: 莫烦Python
Thank you for supporting!
"""
# please note, all tutorial code are running under python3.5.
# If you use the version like python2.7, please modify the code accordingly
# 4 - Regressor example
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt
# create some data
X = np.linspace(-1, 1, 200)
np.random.shuffle(X) # randomize the data
Y = 0.5 * X + 2 + np.random.normal(0, 0.05, (200, ))
# plot data
plt.scatter(X, Y)
plt.show()
X_train, Y_train = X[:160], Y[:160] # first 160 data points
X_test, Y_test = X[160:], Y[160:] # last 40 data points
# build a neural network from the 1st layer to the last layer
model = Sequential()
model.add(Dense(units=1, input_dim=1))
# choose loss function and optimizing method
model.compile(loss='mse', optimizer='sgd')
# training
print('Training -----------')
for step in range(301):
cost = model.train_on_batch(X_train, Y_train)
if step % 100 == 0:
print('train cost: ', cost)
# test
print('\nTesting ------------')
cost = model.evaluate(X_test, Y_test, batch_size=40)
print('test cost:', cost)
W, b = model.layers[0].get_weights()
print('Weights=', W, '\nbiases=', b)
# plotting the prediction
Y_pred = model.predict(X_test)
plt.scatter(X_test, Y_test)
plt.plot(X_test, Y_pred)
plt.show()
