TensorFlow 从入门到精通（2）—— 波士顿房价预测

import tensorflow as

• 防止过拟合的一个办法是设置验证集，当模型在训练集上训练完之后，利用验证集对模型进行调参优化，最终用测试集测试模型性能

print(tf.__version__)

2.5.1

一、准备数据集

import pandas as

df = pd.read_csv('boston.csv')
df.head()

df.tail()

df.describe()

data = df.values
data.shape

(506, 13)

x_data = data[:,:12]
y_data = data[:,12]

from sklearn.preprocessing import scale
# 划分训练集，测试集，验证集
train_num = 300 # 训练集
valid_num = 100 # 验证集

# 训练集
x_train = x_data[:train_num]
y_train = y_data[:train_num]

# 验证集
x_valid = x_data[train_num:train_num+valid_num]
y_valid = y_data[train_num:train_num+valid_num]

# 测试集
x_test = x_data[train_num+valid_num:]
y_test = y_data[train_num+valid_num:]


# 转换数据类型 
'''
tf.cast可以直接将numpy数组转换为tensor
'''
x_train = tf.cast(scale(x_train),dtype=tf.float32)
x_valid = tf.cast(scale(x_valid),dtype=tf.float32)
x_test = tf.cast(scale(x_test),dtype=tf.float32)

二、模型

import numpy as np
def line_model(x,w,b):
    return tf.matmul(x,w) +

三、训练

W = tf.Variable(tf.random.normal(shape=(12,1)))
B = tf.Variable(tf.zeros(1),dtype=tf.float32)

def loss(x,y,w,b):
    loss_ = tf.square(line_model(x,w,b) - y)
    return tf.reduce_mean(loss_)
    
def grand(x,y,w,b):
    with tf.GradientTape() as tape:
        loss_ = loss(x,y,w,b)
    return tape.gradient(loss_,[w,b])

training_epochs = 50
learning_rate = 0.001
batch_size = 10 # 训练一次的样本数
optimizer = tf.keras.optimizers.SGD(learning_rate)

total_steps = train_num // batch_size
loss_train_list = []
loss_valid_list = []
for epoch in range(training_epochs):
    for step in range(total_steps):
        xs = x_train[step*batch_size:(step+1)*batch_size]
        ys = y_train[step*batch_size:(step+1)*batch_size]
        
        grads = grand(xs,ys,W,B)
        optimizer.apply_gradients(zip(grads,[W,B]))
    loss_train = loss(x_train,y_train,W,B).numpy()
    loss_valid = loss(x_valid,y_valid,W,B).numpy()
    loss_train_list.append(loss_train)
    loss_valid_list.append(loss_valid)
    print(f'{epoch+1}次 loss_train:{loss_train} loss_valid:{loss_valid}')

1次 loss_train:665.7977905273438 loss_valid:477.3428039550781
2次 loss_train:593.6646728515625 loss_valid:413.1649475097656
3次 loss_train:533.6004028320312 loss_valid:362.72601318359375
4次 loss_train:482.3865661621094 loss_valid:321.768310546875
5次 loss_train:438.0271911621094 loss_valid:287.7205810546875
6次 loss_train:399.220703125 loss_valid:258.9722900390625
7次 loss_train:365.0664367675781 loss_valid:234.4657745361328
8次 loss_train:334.9011535644531 loss_valid:213.4668426513672
9次 loss_train:308.20770263671875 loss_valid:195.4357147216797
10次 loss_train:284.56365966796875 loss_valid:179.9535675048828
11次 loss_train:263.6124572753906 loss_valid:166.6811065673828
12次 loss_train:245.04588317871094 loss_valid:155.3340301513672
13次 loss_train:228.59408569335938 loss_valid:145.66885375976562
14次 loss_train:214.01869201660156 loss_valid:137.47401428222656
15次 loss_train:201.10826110839844 loss_valid:130.56398010253906
16次 loss_train:189.67495727539062 loss_valid:124.77549743652344
17次 loss_train:179.55177307128906 loss_valid:119.96448516845703
18次 loss_train:170.58995056152344 loss_valid:116.00358581542969
19次 loss_train:162.65756225585938 loss_valid:112.78060913085938
20次 loss_train:155.6372833251953 loss_valid:110.1964340209961
21次 loss_train:149.42471313476562 loss_valid:108.16374969482422
22次 loss_train:143.92759704589844 loss_valid:106.60588836669922
23次 loss_train:139.0637664794922 loss_valid:105.45535278320312
24次 loss_train:134.7605743408203 loss_valid:104.65306091308594
25次 loss_train:130.95376586914062 loss_valid:104.14726257324219
26次 loss_train:127.58616638183594 loss_valid:103.89268493652344
27次 loss_train:124.60723114013672 loss_valid:103.84986114501953
28次 loss_train:121.97221374511719 loss_valid:103.98436737060547
29次 loss_train:119.64164733886719 loss_valid:104.26632690429688
30次 loss_train:117.58037567138672 loss_valid:104.66973876953125
31次 loss_train:115.75743103027344 loss_valid:105.17220306396484
32次 loss_train:114.14533233642578 loss_valid:105.7542495727539
33次 loss_train:112.71986389160156 loss_valid:106.39917755126953
34次 loss_train:111.45946502685547 loss_valid:107.09259796142578
35次 loss_train:110.34514617919922 loss_valid:107.82219696044922
36次 loss_train:109.360107421875 loss_valid:108.57747650146484
37次 loss_train:108.4894790649414 loss_valid:109.3494873046875
38次 loss_train:107.72004699707031 loss_valid:110.13068389892578
39次 loss_train:107.04024505615234 loss_valid:110.91461181640625
40次 loss_train:106.43974304199219 loss_valid:111.69599914550781
41次 loss_train:105.909423828125 loss_valid:112.47042846679688
42次 loss_train:105.44117736816406 loss_valid:113.23422241210938
43次 loss_train:105.0279312133789 loss_valid:113.9843978881836
44次 loss_train:104.66329193115234 loss_valid:114.7186508178711
45次 loss_train:104.34174346923828 loss_valid:115.43498992919922
46次 loss_train:104.05831146240234 loss_valid:116.13207244873047
47次 loss_train:103.80862426757812 loss_valid:116.80872344970703
48次 loss_train:103.58882141113281 loss_valid:117.46420288085938
49次 loss_train:103.39546966552734 loss_valid:118.0979995727539
50次 loss_train:103.22554779052734 loss_valid:118.70991516113281

# 可视化损失
import matplotlib.pyplot as plt
%matplotlib inline
plt.plot(loss_train_list)
plt.plot(loss_valid_list)

[<matplotlib.lines.Line2D at 0x7efbb0f696d8>]

四、预测

W.numpy()

array([[ 0.04078581],
       [ 0.42111152],
       [-1.266555  ],
       [ 0.7909989 ],
       [-0.7962972 ],
       [ 1.6725844 ],
       [-0.23721075],
       [-0.900601  ],
       [ 0.45604745],
       [-0.51313245],
       [-2.141279  ],
       [-0.713085  ]], dtype=float32)

B.numpy()

array([24.265219], dtype=float32)

# 从测试集里随机挑一个
house_id = np.random.randint(0,x_data.shape[0]-train_num-valid_num)
# 真实值
y = y_test[house_id]
# 预测值
y_pre = int(line_model(x_test,W.numpy(),B.numpy())[house_id])

print(f'真实值：{y}，预测值：{y_pre}')

真实值：10.2，预测值：24

plt.plot(y_test,'r')
plt.plot(line_model(x_test,W.numpy(),B.numpy()),'g')

[<matplotlib.lines.Line2D at 0x7efbb069a0b8>]

plt.plot(y_train,'r')
plt.plot(line_model(x_train,W.numpy(),B.numpy()),'g')

[<matplotlib.lines.Line2D at 0x7efbb0643828>]

plt.plot(y_valid,'r')
plt.plot(line_model(x_valid,W.numpy(),B.numpy()),'g')

[<matplotlib.lines.Line2D at 0x7efbb0610550>]

import sklearn.metrics as sm # 模型性能评价模块
print('R2_score:', sm.r2_score(y_test,line_model(x_test,W.numpy(),B.numpy())))

R2_score: -2.399917872887695

预测效果不是很好…略微无奈，模型本身问题，解决不了房价预测问题。由此可见，选择模型非常非常重要，如果你模型选的不对，你调多好的参数都没用。