tensorflow线性回归

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import numpy as np
import os
import matplotlib.pyplot as plt
os.environ["CUDA_VISIBLE_DEVICES"]="0"
learning_rate=0.01
training_epochs=1000
display_step=50
train_X=np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y=np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples=train_X.shape[0]
X=tf.placeholder("float")
Y=tf.placeholder("float")
W=tf.Variable(np.random.randn(),name="weight")
b=tf.Variable(np.random.randn(),name=‘bias‘)
pred=tf.add(tf.multiply(X,W),b)
cost=tf.reduce_sum(tf.pow(pred-Y,2))/(2*n_samples)
optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
init=tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    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+1) % display_step == 0:
                c = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
                print("Epoch:" , ‘%04d‘ % (epoch + 1), "cost=", "{:.9f}".format(c), "W=", sess.run(W), "b=", sess.run(
                    b))
                print("Optimization Finished!")
                training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
                print("Train cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b))
                plt.plot(train_X, train_Y, ‘ro‘, label=‘Original data‘)
                plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label="Fitting line")
                plt.legend()
                plt.show()