Tensorflow2.0学习（5）：基于加利福尼亚房价预测的回归问题基本步骤

回归问题：输出/标签为连续值

• 导入、打印包的信息

import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)
print(sys.version_info)
for module in mpl, np ,pd, sklearn, tf, keras:
print(module.__name__, module.__version__)

2.0.0
sys.version_info(major=3, minor=7, micro=6, releaselevel='final', serial=0)
matplotlib 3.1.3
numpy 1.18.1
pandas 1.0.0
sklearn 0.22.1
tensorflow 2.0.0
tensorflow_core.keras 2.2.4-tf

• 加载、打印数据集信息

from sklearn.datasets import fetch_california_housing

housing = fetch_california_housing()
# 数据集的描述
print(housing.DESCR)
print(housing.data.shape)
print(housing.target.shape)

.. _california_housing_dataset:

California Housing dataset
--------------------------

**Data Set Characteristics:**

:Number of Instances: 20640

:Number of Attributes: 8 numeric, predictive attributes and the target

:Attribute Information:
- MedInc        median income in block
- HouseAge      median house age in block
- AveRooms      average number of rooms
- AveBedrms     average number of bedrooms
- Population    block population
- AveOccup      average house occupancy
- Latitude      house block latitude
- Longitude     house block longitude

:Missing Attribute Values: None

This dataset was obtained from the StatLib repository.
http://lib.stat.cmu.edu/datasets/

The target variable is the median house value for California districts.

This dataset was derived from the 1990 U.S. census, using one row per census
block group. A block group is the smallest geographical unit for which the U.S.
Census Bureau publishes sample data (a block group typically has a population
of 600 to 3,000 people).

It can be downloaded/loaded using the
:func:`sklearn.datasets.fetch_california_housing` function.

.. topic:: References

- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
Statistics and Probability Letters, 33 (1997) 291-297

(20640, 8)
(20640,)

import pprint
# 而pprint()采用分行打印输出
# 所以对于数据结构比较复杂、数据长度较长的数据，适合采用pprint()打印方式。
pprint.pprint(housing.data[0:5])
pprint.pprint(housing.target[0:5])

array([[ 8.32520000e+00,  4.10000000e+01,  6.98412698e+00,
1.02380952e+00,  3.22000000e+02,  2.55555556e+00,
3.78800000e+01, -1.22230000e+02],
[ 8.30140000e+00,  2.10000000e+01,  6.23813708e+00,
9.71880492e-01,  2.40100000e+03,  2.10984183e+00,
3.78600000e+01, -1.22220000e+02],
[ 7.25740000e+00,  5.20000000e+01,  8.28813559e+00,
1.07344633e+00,  4.96000000e+02,  2.80225989e+00,
3.78500000e+01, -1.22240000e+02],
[ 5.64310000e+00,  5.20000000e+01,  5.81735160e+00,
1.07305936e+00,  5.58000000e+02,  2.54794521e+00,
3.78500000e+01, -1.22250000e+02],
[ 3.84620000e+00,  5.20000000e+01,  6.28185328e+00,
1.08108108e+00,  5.65000000e+02,  2.18146718e+00,
3.78500000e+01, -1.22250000e+02]])
array([4.526, 3.585, 3.521, 3.413, 3.422])

• 分割数据集为训练集、验证集、测试集

from sklearn.model_selection import train_test_split

x_train_all, x_test, y_train_all, y_test = train_test_split(
housing.data, housing.target, random_state = 7)
x_train, x_valid, y_train, y_valid = train_test_split(
x_train_all, y_train_all, random_state = 11)
print(x_train.shape,y_train.shape)
print(x_valid.shape,y_valid.shape)
print(x_test.shape,y_test.shape)

(11610, 8) (11610,)
(3870, 8) (3870,)
(5160, 8) (5160,)

• 归一化输入数据

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
x_train_scaled = scaler.fit_transform(x_train)
x_valid_scaled = scaler.transform(x_valid)
x_test_scaled = scaler.transform(x_test)

• 构建模型、定义训练指标与方法

model = keras.models.Sequential([
keras.layers.Dense(30,activation='relu',
input_shape=x_train.shape[1:]),
keras.layers.Dense(1)
])
model.summary()
model.compile(loss='mean_squared_error',optimizer="sgd")
callbacks = [keras.callbacks.EarlyStopping(
patience=5,min_delta=1e-2)]

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense (Dense)                (None, 30)                270
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 31
=================================================================
Total params: 301
Trainable params: 301
Non-trainable params: 0
_________________________________________________________________

• 训练数据

history = model.fit(x_train_scaled, y_train,
validation_data = (x_valid_scaled, y_valid),
epochs =100,
callbacks = callbacks)

Train on 11610 samples, validate on 3870 samples
Epoch 1/100
11610/11610 [==============================] - 2s 146us/sample - loss: 1.0878 - val_loss: 1.1014
Epoch 2/100
11610/11610 [==============================] - 1s 64us/sample - loss: 1.0323 - val_loss: 0.5181
Epoch 3/100
11610/11610 [==============================] - 1s 63us/sample - loss: 0.4491 - val_loss: 0.4698
Epoch 4/100
11610/11610 [==============================] - 1s 63us/sample - loss: 0.4440 - val_loss: 0.5120
Epoch 5/100
11610/11610 [==============================] - 1s 67us/sample - loss: 0.4084 - val_loss: 0.4171
Epoch 6/100
11610/11610 [==============================] - 1s 70us/sample - loss: 0.3905 - val_loss: 0.4072
Epoch 7/100
11610/11610 [==============================] - 1s 66us/sample - loss: 0.3799 - val_loss: 0.3934
Epoch 8/100
11610/11610 [==============================] - 1s 66us/sample - loss: 0.3732 - val_loss: 0.3878
Epoch 9/100
11610/11610 [==============================] - 1s 79us/sample - loss: 0.3746 - val_loss: 0.3847
Epoch 10/100
11610/11610 [==============================] - 1s 74us/sample - loss: 0.3696 - val_loss: 0.3798
Epoch 11/100
11610/11610 [==============================] - 1s 66us/sample - loss: 0.3616 - val_loss: 0.3777
Epoch 12/100
11610/11610 [==============================] - 1s 73us/sample - loss: 0.3785 - val_loss: 0.3768
Epoch 13/100
11610/11610 [==============================] - 1s 70us/sample - loss: 0.3590 - val_loss: 0.3733
Epoch 14/100
11610/11610 [==============================] - 1s 66us/sample - loss: 0.3601 - val_loss: 0.3719
Epoch 15/100
11610/11610 [==============================] - 1s 67us/sample - loss: 0.3593 - val_loss: 0.3755

• 训练结果

def plot_learning_curves(history):
pd.DataFrame(history.history).plot(figsize=(8, 5))
plt.grid(True)
plt.gca().set_ylim(0, 1)
plt.show()
plot_learning_curves(history)

• 用测试集通过模型做评估

model.evaluate(x_test_scaled, y_test, verbose=0)

0.3796711852384168

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