Tensorflow入门小程序

# 线性回归预测
x_data = np.random.rand(100)
noise = np.random.normal(0, 0.01, x_data.shape)
y_data = x_data*0.1+0.2+noise
plt.scatter(x_data, y_data)
plt.show()
# 构建一个线性模型
d = tf.Variable(np.random.rand(1))
k = tf.Variable(np.random.rand(1))
y = k*x_data + d
loss = tf.losses.mean_squared_error(y_data, y)
optimizer = tf.train.GradientDescentOptimizer(0.3)
train = optimizer.minimize(loss)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for i in range(201):
        sess.run(train)
        if i % 20 == 0:
            print(i, sess.run([k, d]))
    y_pred = sess.run(y)
    plt.scatter(x_data, y_data)  # scatter是画点
    plt.plot(x_data, y_pred, 'r-', lw=3)  # plot是划线
    plt.show()