基于TensorFlow实现一元线性回归

最新推荐文章于 2024-12-05 00:00:00 发布

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最新推荐文章于 2024-12-05 00:00:00 发布

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分类专栏： TensorFlow

本文链接：https://blog.youkuaiyun.com/opencv_857310866/article/details/79602601

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本文通过使用TensorFlow实现线性回归模型来预测数据趋势，并展示了如何训练模型以最小化预测误差，同时提供了完整的代码实例和图表分析。

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

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]

#创建图
X = tf.placeholder('float32')
Y = tf.placeholder('float32')
W = tf.Variable(rng.randn(),name='weight')
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(learning_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('进入第',i+1,'轮','cost=','{:.9f}'.format(c),
                  'W=',sess.run(W),'b=',sess.run(b))
    print('优化结束..')
    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,'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...')
    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()

运行结果：