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()