使用TensorFlow完成线性回归

TensorFlow是由Google开发的一个开源的机器学习框架。它可以让开发者更加轻松地构建和训练深度学习模型，从而解决各种自然语言处理、计算机视觉、语音识别、推荐系统等领域的问题。

TensorFlow的主要特点是灵活性和可伸缩性。它实现了一种基于数据流图的计算模型，使得用户可以定义自己的计算图，控制模型的计算过程。同时，TensorFlow支持分布式计算，使得用户可以在多台机器上运行大规模计算任务，从而提高计算效率。

TensorFlow包含了许多高级API，例如Keras和Estimator，使得用户可以更加轻松地构建和训练深度学习模型。Keras提供了一个易于使用的高级API，使得用户可以在不需要深入了解TensorFlow的情况下，构建和训练深度学习模型。Estimator则提供了一种更加低级的API，使得用户可以更加灵活地定义模型的结构和训练过程。

TensorFlow还提供了一个交互式开发环境，称为TensorBoard，可以帮助用户可视化模型的计算图、训练过程和性能指标，从而更加直观地理解和调试深度学习模型。

由于TensorFlow的灵活性和可伸缩性，它已经被广泛应用于各个领域，包括自然语言处理、计算机视觉、语音识别、推荐系统等。例如，在自然语言处理领域，TensorFlow被用于构建和训练各种强大的模型，例如机器翻译模型、文本分类模型、语言生成模型等。

总的来说，TensorFlow是一个强大的机器学习框架，可以帮助用户更加轻松地构建和训练深度学习模型。随着深度学习技术的不断发展，TensorFlow将继续发挥重要的作用，推动各个领域的发展和创新。

1. 导入TensorFlow库

# 导入相关库
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

2. 构造数据集

# 产出样本点个数
n_observations = 100
# 产出-3~3之间的样本点
xs = np.linspace(-3, 3, n_observations) 
# sin扰动
ys = np.sin(xs) + np.random.uniform(-0.5, 0.5, n_observations) 

xs

   array([-3.        , -2.93939394, -2.87878788, -2.81818182, -2.75757576,-2.6969697 , -2.63636364, -2.57575758, -2.51515152, -2.45454545,-2.39393939, -2.33333333, -2.27272727, -2.21212121, -2.15151515,-2.09090909, -2.03030303, -1.96969697, -1.90909091, -1.84848485,-1.78787879, -1.72727273, -1.66666667, -1.60606061, -1.54545455,-1.48484848, -1.42424242, -1.36363636, -1.3030303 , -1.24242424,-1.18181818, -1.12121212, -1.06060606, -1.        , -0.93939394,-0.87878788, -0.81818182, -0.75757576, -0.6969697 , -0.63636364,-0.57575758, -0.51515152, -0.45454545, -0.39393939, -0.33333333,-0.27272727, -0.21212121, -0.15151515, -0.09090909, -0.03030303,0.03030303,  0.09090909,  0.15151515,  0.21212121,  0.27272727,0.33333333,  0.39393939,  0.45454545,  0.51515152,  0.57575758,0.63636364,  0.6969697 ,  0.75757576,  0.81818182,  0.87878788,0.93939394,  1.        ,  1.06060606,  1.12121212,  1.18181818,1.24242424,  1.3030303 ,  1.36363636,  1.42424242,  1.48484848,1.54545455,  1.60606061,  1.66666667,  1.72727273,  1.78787879,1.84848485,  1.90909091,  1.96969697,  2.03030303,  2.09090909,2.15151515,  2.21212121,  2.27272727,  2.33333333,  2.39393939,2.45454545,  2.51515152,  2.57575758,  2.63636364,  2.6969697 ,2.75757576,  2.81818182,  2.87878788,  2.93939394,  3.        ])

ys

   array([-0.62568008,  0.01486274, -0.29232541, -0.05271084,
-0.53407957,-0.37199581, -0.40235236, -0.80005504, -0.2280913 , -0.96111433,-0.58732159, -0.71310851, -1.19817878, -0.93036437, -1.02682804,-1.33669261, -1.36873043, -0.44500172, -1.38769079, -0.52899793,-0.78090929, -1.1470421 , -0.79274726, -0.95139505, -1.3536293 ,-1.15097615, -1.04909201, -0.89071026, -0.81181765, -0.70292996,-0.49732344, -1.22800179, -1.21280414, -0.59583172, -1.05027515,-0.56369191, -0.68680323, -0.20454038, -0.32429566, -0.84640122,-0.08175012, -0.76910728, -0.59206189, -0.09984673, -0.52465978,-0.30498277,  0.08593627, -0.29488864,  0.24698113, -0.07324925,0.12773032,  0.55508531,  0.14794648,  0.40155342,  0.31717698,0.63213964,  0.35736413,  0.05264068,  0.39858619,  1.00710311,0.73844747,  1.12858026,  0.59779567,  1.22131999,  0.80849061,0.72796849,  1.0990044 ,  0.45447096,  1.15217952,  1.31846002,1.27140258,  0.65264777,  1.15205186,  0.90705463,  0.82489198,0.50572125,  1.47115594,  0.98209434,  0.95763951,  0.50225094,1.40415029,  0.74618984,  0.90620692,  0.40593222,  0.62737999,1.05236579,  1.20041249,  1.14784273,  0.54798933,  0.18167682,0.50830766,  0.92498585,  0.9778136 ,  0.42331405,  0.88163729,0.67235809, -0.00539421, -0.06219493,  0.26436412,  0.51978602])

# 可视化图长和宽
plt.rcParams["figure.figsize"] = (6,4)
# 绘制散点图
plt.scatter(xs, ys) 
plt.show()

3. 定义基本模型

# 占位
X = tf.placeholder(tf.float32, name='X')
Y = tf.placeholder(tf.float32, name='Y')

# 随机采样出变量
W = tf.Variable(tf.random_normal([1]), name='weight') 
b = tf.Variable(tf.random_normal([1]), name='bias')

# 手写y = wx+b
Y_pred = tf.add(tf.multiply(X, W), b) 

# 定义损失函数mse
loss = tf.square(Y - Y_pred, name='loss') 

# 学习率
learning_rate = 0.01
# 优化器，就是tensorflow中梯度下降的策略
# 定义梯度下降,申明学习率和针对那个loss求最小化
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) 

4. 训练模型

# 去样本数量
n_samples = xs.shape[0]
init = tf.global_variables_initializer()
with tf.Session() as sess:# 记得初始化所有变量sess.run(init) writer = tf.summary.FileWriter('../graphs/linear_reg', sess.graph)# 训练模型for i in range(50):#初始化损失函数total_loss = 0for x, y in zip(xs, ys):# 通过feed_dic把数据灌进去_, l = sess.run([optimizer, loss], feed_dict={X: x, Y:y}) #_是optimizer的返回，在这没有用就省略total_loss += l #统计每轮样本的损失print('Epoch {0}: {1}'.format(i, total_loss/n_samples)) #求损失平均# 关闭writerwriter.close() # 取出w和b的值W, b = sess.run([W, b]) 

Epoch 0: [0.48447946]
Epoch 1: [0.20947962]
Epoch 2: [0.19649307]
Epoch 3: [0.19527708]
Epoch 4: [0.19514856]
Epoch 5: [0.19513479]
Epoch 6: [0.19513334]
Epoch 7: [0.19513316]
Epoch 8: [0.19513315]
Epoch 9: [0.19513315]
Epoch 10: [0.19513315]
Epoch 11: [0.19513315]
Epoch 12: [0.19513315]
Epoch 13: [0.19513315]
Epoch 14: [0.19513315]
Epoch 15: [0.19513315]
Epoch 16: [0.19513315]
Epoch 17: [0.19513315]
Epoch 18: [0.19513315]
Epoch 19: [0.19513315]
Epoch 20: [0.19513315]
Epoch 21: [0.19513315]
Epoch 22: [0.19513315]
Epoch 23: [0.19513315]
Epoch 24: [0.19513315]
Epoch 25: [0.19513315]
Epoch 26: [0.19513315]
Epoch 27: [0.19513315]
Epoch 28: [0.19513315]
Epoch 29: [0.19513315]
Epoch 30: [0.19513315]
Epoch 31: [0.19513315]
Epoch 32: [0.19513315]
Epoch 33: [0.19513315]
Epoch 34: [0.19513315]
Epoch 35: [0.19513315]
Epoch 36: [0.19513315]
Epoch 37: [0.19513315]
Epoch 38: [0.19513315]
Epoch 39: [0.19513315]
Epoch 40: [0.19513315]
Epoch 41: [0.19513315]
Epoch 42: [0.19513315]
Epoch 43: [0.19513315]
Epoch 44: [0.19513315]
Epoch 45: [0.19513315]
Epoch 46: [0.19513315]
Epoch 47: [0.19513315]
Epoch 48: [0.19513315]
Epoch 49: [0.19513315]

print(W,b)
print("W:"+str(W[0]))
print("b:"+str(b[0]))

[0.23069778] [-0.12590201]
W:0.23069778
b:-0.12590201

5. 线性回归图

# 线性回归图
plt.plot(xs, ys, 'bo', label='Real data')
plt.plot(xs, xs * W + b, 'r', label='Predicted data')
plt.legend()
plt.show()

附：系列文章

序号	文章目录	直达链接
1	波士顿房价预测	https://want595.blog.csdn.net/article/details/132181950
2	鸢尾花数据集分析	https://want595.blog.csdn.net/article/details/132182057
3	特征处理	https://want595.blog.csdn.net/article/details/132182165
4	交叉验证	https://want595.blog.csdn.net/article/details/132182238
5	构造神经网络示例	https://want595.blog.csdn.net/article/details/132182341
6	使用TensorFlow完成线性回归	https://want595.blog.csdn.net/article/details/132182417
7	使用TensorFlow完成逻辑回归	https://want595.blog.csdn.net/article/details/132182496
8	TensorBoard案例	https://want595.blog.csdn.net/article/details/132182584
9	使用Keras完成线性回归	https://want595.blog.csdn.net/article/details/132182723
10	使用Keras完成逻辑回归	https://want595.blog.csdn.net/article/details/132182795
11	使用Keras预训练模型完成猫狗识别	https://want595.blog.csdn.net/article/details/132243928
12	使用PyTorch训练模型	https://want595.blog.csdn.net/article/details/132243989
13	使用Dropout抑制过拟合	https://want595.blog.csdn.net/article/details/132244111
14	使用CNN完成MNIST手写体识别(TensorFlow)	https://want595.blog.csdn.net/article/details/132244499
15	使用CNN完成MNIST手写体识别(Keras)	https://want595.blog.csdn.net/article/details/132244552
16	使用CNN完成MNIST手写体识别(PyTorch)	https://want595.blog.csdn.net/article/details/132244641
17	使用GAN生成手写数字样本	https://want595.blog.csdn.net/article/details/132244764
18	自然语言处理	https://want595.blog.csdn.net/article/details/132276591