Tensorflow快速入门——线性回归

分手后的思念是犯贱 2023-06-27 04:29 56阅读 0赞

# Tensorflow快速入门二 #

## 线性回归 ##

*  运用Tensorflow进行线性回归

#--*coding--:utf-8
    import tensorflow as tf
    import numpy
    import matplotlib.pyplot as plt
    rng = numpy.random

### 参数设定 ###

learning_rate = 0.01
    training_epochs = 10000
    display_step = 50

# 训练数据 #

train_X = numpy.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 = numpy.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]
    print( "train_X:",train_X)
    print( "train_Y:",train_Y)

> train\_X: \[ 3.3 4.4 5.5 6.71 6.93 4.168 9.779 6.182 7.59 2.167  
> 7.042 10.791 5.313 7.997 5.654 9.27 3.1 \]  
> train\_Y: \[1.7 2.76 2.09 3.19 1.694 1.573 3.366 2.596 2.53 1.221 2.827 3.465  
> 1.65 2.904 2.42 2.94 1.3 \]

#### 设置placeholder ####

X = tf.placeholder("float")
    Y = tf.placeholder("float")

#### 设置模型的权重和偏置 ####

W = tf.Variable(rng.randn(), name="weight")
    b = tf.Variable(rng.randn(), name="bias")

#### 设置线性回归的方程 ####

pred = tf.add(tf.multiply(X, W), b)

#### 设置cost为均方差 ####

cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)

#### 梯度下降 ####

*  注意，minimize() 可以自动修正w和b，因为默认设置Variables的trainable=True

optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

#### 初始化所有variables ####

init = tf.global_variables_initializer()

### 开始训练 ###

with tf.Session() as sess:
        sess.run(init)
    
    
        # 灌入所有训练数据
        for epoch in range(training_epochs):
            for (x, y) in zip(train_X, train_Y):
                sess.run(optimizer, feed_dict={ X: x, Y: y})
    
            # 打印出每次迭代的log日志
            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("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')
    
        # 作图
        plt.plot(train_X, train_Y, 'ro', label='Original data')
        plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
        plt.legend()
        plt.show()
        
        # 测试样本
        test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
        test_Y = numpy.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(pred - Y, 2)) / (2 * test_X.shape[0]),
            feed_dict={ X: test_X, Y: test_Y})  # same function as cost above
        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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