import numpy as np
np.random.seed(1337)
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt
X = np.linspace(-1, 1, 200)
np.random.shuffle(X)
Y = 0.5 * X + 2 + np.random.normal(0, 0.05, (200, ))
plt.scatter(X, Y)
plt.show()
X_train, Y_train = X[:160], Y[:160]
X_test, Y_test = X[160:], Y[160:]
model = Sequential()
model.add(Dense(units=1, input_dim=1))
model.compile(loss='mse', optimizer='sgd')
for step in range(301):
cost = model.train_on_batch(X_train, Y_train)
if step % 100 == 0:
print('train cost:', cost)
cost = model.evaluate(X_test, Y_test, batch_size=40)
W, b = model.layers[0].get_weights()
print('Weights=', W, '\nbiases=', b)
Y_pred = model.predict(X_test)
plt.scatter(X_test, Y_test)
plt.plot(X_test, Y_pred)
plt.show()https://morvanzhou.github.io/tutorials/machine-learning/keras/2-1-regressor/
