regressor_example.py
""" 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()