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TensorFlow经典案例3:实现线性回归
TensorFlow实现线性回归
#实现线性回归 import tensorflow as tf import numpy as np import matplotlib.pyplot as plt rng = np.random learn_rate = 0.01 training_epochs = 1000 display_step = 50 #生成训练数据 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] #创建图 X = tf.placeholder("float") Y = tf.placeholder("float") W = tf.Variable(rng.randn(),name="weigth") b = tf.Variable(rng.randn(),name="bias") prediction = tf.add(tf.multiply(X,W),b) cost = tf.reduce_sum(tf.pow(prediction-Y,2) / (2*n_samples)) train_step = tf.train.GradientDescentOptimizer(learn_rate).minimize(cost) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) for i in range(training_epochs): for(x,y) in zip(train_X,train_Y): sess.run(train_step,feed_dict={X:x,Y:y}) if (i + 1) % display_step == 0: c = sess.run(cost,feed_dict={X:train_X,Y:train_Y}) print("Epoch:", ‘%04d‘ % (i + 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("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), ‘\n‘) plt.plot(train_X,train_Y,‘ro‘,label="origal data") plt.plot(train_X,sess.run(W) * train_X + sess.run(b),label="fit line") plt.legend() plt.show() test_X = np.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1]) test_Y = np.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03]) print("Testing... (Mean square loss Comparison)") testing_cost = sess.run( tf.reduce_sum(tf.pow(prediction - Y, 2)) / (2 * test_X.shape[0]), feed_dict={X: test_X, Y: test_Y}) print("Testing cost=", testing_cost) print("Absolute mean square loss difference:", abs( training_cost - testing_cost)) plt.plot(test_X, test_Y, ‘bo‘, label=‘Testing data‘) plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label=‘Fitted line‘) plt.legend() plt.show()
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TensorFlow经典案例3:实现线性回归
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