TensorFlow——LinearRegression简单模型代码

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

train_x = np.linspace(-5, 3, 50)
train_y = train_x * 5 + 10 + np.random.random(50) * 10 - 5

plt.plot(train_x, train_y, 'r.')
plt.grid(True)
plt.show()

X = tf.placeholder(dtype=tf.float32)
Y = tf.placeholder(dtype=tf.float32)

w = tf.Variable(tf.random.truncated_normal([1]), name='Weight')
b = tf.Variable(tf.random.truncated_normal([1]), name='bias')

z = tf.multiply(X, w) + b

cost = tf.reduce_mean(tf.square(Y - z))
learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

init = tf.global_variables_initializer()

training_epochs = 20
display_step = 2


with tf.Session() as sess:
    sess.run(init)
    loss_list = []
    for epoch in range(training_epochs):
        for (x, y) in zip(train_x, train_y):
            sess.run(optimizer,feed_dict={X:x, Y:y})

        if epoch % display_step == 0:
            loss = sess.run(cost, feed_dict={X:x, Y:y})
            loss_list.append(loss)
            print('Iter: ', epoch, ' Loss: ', loss)

    w_, b_ = sess.run([w, b], feed_dict={X: x, Y: y})
    print(" Finished ")
    print("W: ", w_, " b: ", b_, " loss: ", loss)
    plt.plot(train_x, train_x*w_ + b_, 'g-', train_x, train_y, 'r.')
    plt.grid(True)
    plt.show()