深度学习框架tensorflow二实战（回归问题：天气预报）
此前没了解过tensorflow2的魅力的朋友可以先了解一下该网站：Click
首先准备一个小型数据集，2019年的某个地方的天气情况，各位同学也可以自行伪造一份符合正态分布的数据集。
这里提供了一份下载的链接。点击直通temp.csv。
废话不多说，直接加载我们的数据集看一下。Click：为某地方一年的温度
读取数据展示：
features = pd.read_csv('temps.csv')
print(features.head())#显示读取的文件的前面几行,默认是前五行
print('数据维度:', features.shape)
输出结果：
362行，小型数据集，可以在CPU或者GPU版本都可以运行
数据表中
year,moth,day,week分别表示的具体的时间
temp_2：前天的最高温度值
temp_1：昨天的最高温度值
average：在历史中，每年这一天的平均最高温度值
actual：这就是我们的标签值了，当天的真实最高温度
random：这一列是凑热闹的，随机猜测的值
year month day week temp_2 temp_1 average actual random
0 2019 1 1 Fri 5.0 5.3 5.6 5 -15
1 2019 1 2 Sat 4.4 5.2 5.7 4 -16
2 2019 1 3 Sun 5.0 4.0 5.8 1 -15
3 2019 1 4 Mon 4.0 1.0 5.9 0 -16
4 2019 1 5 Tues 1.0 0.0 6.0 4 -19
数据维度: (362, 9)
处理时间数据：
# 分别得到年，月，日
years = features['year']
months = features['month']
days = features['day']
# datetime格式
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
#print(dates)
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]
print(dates)
输出结果：
[datetime.datetime(2019, 1, 1, 0, 0), datetime.datetime(2019, 1, 2, 0, 0), datetime.datetime(2019, 1, 3, 0, 0), datetime.datetime(2019, 1, 4, 0, 0), datetime.datetime(2019, 1, 5, 0, 0), datetime.datetime(2019, 1, 6, 0, 0), dat...省略了
展示温度图像：
# 准备画图
# 指定默认风格
plt.style.use('fivethirtyeight')
# 设置布局
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (10,10))
fig.autofmt_xdate(rotation = 45)#设置x坐标的标志为四十五度
# 标签值
ax1.plot(dates, features['actual'])
ax1.set_xlabel(''); ax1.set_ylabel('Temperature'); ax1.set_title('True Temp')
# 昨天
ax2.plot(dates, features['temp_1'])
ax2.set_xlabel(''); ax2.set_ylabel('Temperature'); ax2.set_title('Yesterday Temp')
# 前天
ax3.plot(dates, features['temp_2'])
ax3.set_xlabel('Date'); ax3.set_ylabel('Temperature'); ax3.set_title('Yesterday of Yesterday Temp')
# 一个随机预测的温度
ax4.plot(dates, features['random'])
ax4.set_xlabel('Date'); ax4.set_ylabel('Temperature'); ax4.set_title('A random Temp')
plt.tight_layout(pad=2)#自动紧凑布局
plt.show()
输出结果：
数据预处理
第一步首先是把星期几使用onehot编码进行量化
第二步把features的真实标签保存为labels
第三步把输入的真实值以及随机预测值都给剔除
第四步则是对输入数据进行预处理，无量纲化
print(features.head().shape)
# 独热编码
features = pd.get_dummies(features) #one-hot编码，对不是数字的会进行one-hot编码
print(features.head(1).shape)
# 标签，真实的温度
labels = np.array(features['actual'])
# 在特征中去掉标签
features= features.drop('actual', axis = 1)
features= features.drop('random', axis = 1)
#print(features.columns)
# 保存标头，即每列数据对应的含义保存一下
feature_list = list(features.columns)#
# 转换成合适的格式，只保留数据了
features = np.array(features)
print(features)
print(features.shape)
#preprocessing 为预处理库，里面封装了很多预处理操作
#无量纲化：
#标准化：（x-列均值）/ 列标准差
from sklearn import preprocessing
input_features = preprocessing.StandardScaler().fit_transform(features)
#print(input_features)
#print(input_features.shape)
数据预处理完，接下来就是Tensorflow2的新展示了，使用keras的API
#模型堆叠
model = tf.keras.Sequential()
model.add(layers.Dense(16)) #全连接层，16个神经元
model.add(layers.Dense(32)) #全连接层，32个神经元
model.add(layers.Dense(1)) #全连接层，输出层
#compile相当于对网络进行配置，指定好优化器和损失函数等
model.compile(optimizer=tf.keras.optimizers.SGD(0.001),
loss='mean_squared_error')
model.fit(input_features, labels, validation_split=0.15, epochs=10, batch_size=64)#validation_split指的是验证集比例
model.summary()
输出结果
可以看到训练集已经基本收敛，但测试集的损失值还是比较大的。
Train on 271 samples, validate on 91 samples
Epoch 1/10
271/271 [==============================] - 0s 2ms/sample - loss: 821.1113 - val_loss: 285.1090
Epoch 2/10
271/271 [==============================] - 0s 48us/sample - loss: 677.4314 - val_loss: 356.1234
Epoch 3/10
271/271 [==============================] - 0s 43us/sample - loss: 375.2623 - val_loss: 632.0933
Epoch 4/10
271/271 [==============================] - 0s 44us/sample - loss: 108.9708 - val_loss: 691.7151
Epoch 5/10
271/271 [==============================] - 0s 44us/sample - loss: 50.2127 - val_loss: 598.7165
Epoch 6/10
271/271 [==============================] - 0s 79us/sample - loss: 40.9568 - val_loss: 514.2609
Epoch 7/10
271/271 [==============================] - 0s 72us/sample - loss: 36.6869 - val_loss: 454.2624
Epoch 8/10
271/271 [==============================] - 0s 65us/sample - loss: 34.8708 - val_loss: 472.3111
Epoch 9/10
271/271 [==============================] - 0s 49us/sample - loss: 33.7877 - val_loss: 409.8574
Epoch 10/10
271/271 [==============================] - 0s 49us/sample - loss: 32.2860 - val_loss: 347.9267
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) multiple 224
_________________________________________________________________
dense_1 (Dense) multiple 544
_________________________________________________________________
dense_2 (Dense) multiple 33
=================================================================
Total params: 801
Trainable params: 801
Non-trainable params: 0
_________________________________________________________________
解决办法：
非常非常多，我们可以把迭代次数调到30次，就可以完美收敛了。
Train on 271 samples, validate on 91 samples
Epoch 1/30
271/271 [==============================] - 1s 2ms/sample - loss: 814.3073 - val_loss: 247.3884
Epoch 2/30
271/271 [==============================] - 0s 47us/sample - loss: 677.9860 - val_loss: 229.9150
Epoch 3/30
271/271 [==============================] - 0s 43us/sample - loss: 416.3268 - val_loss: 267.2840
Epoch 4/30
271/271 [==============================] - 0s 43us/sample - loss: 158.0090 - val_loss: 227.4088
Epoch 5/30
271/271 [==============================] - 0s 43us/sample - loss: 56.7685 - val_loss: 196.2745
Epoch 6/30
271/271 [==============================] - 0s 43us/sample - loss: 35.3764 - val_loss: 169.8626
Epoch 7/30
271/271 [==============================] - 0s 43us/sample - loss: 31.4925 - val_loss: 150.9355
Epoch 8/30
271/271 [==============================] - 0s 42us/sample - loss: 29.9851 - val_loss: 139.6017
Epoch 9/30
271/271 [==============================] - 0s 43us/sample - loss: 29.0805 - val_loss: 136.3649
Epoch 10/30
271/271 [==============================] - 0s 42us/sample - loss: 29.9907 - val_loss: 112.3794
Epoch 11/30
271/271 [==============================] - 0s 42us/sample - loss: 28.2350 - val_loss: 102.6311
Epoch 12/30
271/271 [==============================] - 0s 43us/sample - loss: 29.1364 - val_loss: 107.4938
Epoch 13/30
271/271 [==============================] - 0s 43us/sample - loss: 27.1924 - val_loss: 100.6108
Epoch 14/30
271/271 [==============================] - 0s 43us/sample - loss: 26.5309 - val_loss: 96.0920
Epoch 15/30
271/271 [==============================] - 0s 63us/sample - loss: 26.0549 - val_loss: 88.7254
Epoch 16/30
271/271 [==============================] - 0s 69us/sample - loss: 25.7222 - val_loss: 84.9858
Epoch 17/30
271/271 [==============================] - 0s 72us/sample - loss: 25.2887 - val_loss: 82.5212
Epoch 18/30
271/271 [==============================] - 0s 87us/sample - loss: 25.0895 - val_loss: 80.9025
Epoch 19/30
271/271 [==============================] - 0s 75us/sample - loss: 25.6907 - val_loss: 75.7480
Epoch 20/30
271/271 [==============================] - 0s 80us/sample - loss: 25.4387 - val_loss: 74.5455
Epoch 21/30
271/271 [==============================] - 0s 56us/sample - loss: 24.7823 - val_loss: 69.4842
Epoch 22/30
271/271 [==============================] - 0s 50us/sample - loss: 24.4951 - val_loss: 66.2443
Epoch 23/30
271/271 [==============================] - 0s 50us/sample - loss: 24.9533 - val_loss: 64.3459
Epoch 24/30
271/271 [==============================] - 0s 47us/sample - loss: 24.7001 - val_loss: 62.5857
Epoch 25/30
271/271 [==============================] - 0s 45us/sample - loss: 24.9280 - val_loss: 55.9608
Epoch 26/30
271/271 [==============================] - 0s 43us/sample - loss: 24.1338 - val_loss: 60.5551
Epoch 27/30
271/271 [==============================] - 0s 57us/sample - loss: 24.5145 - val_loss: 57.6269
Epoch 28/30
271/271 [==============================] - 0s 55us/sample - loss: 24.2341 - val_loss: 52.8076
Epoch 29/30
271/271 [==============================] - 0s 72us/sample - loss: 23.7565 - val_loss: 48.5504
Epoch 30/30
271/271 [==============================] - 0s 90us/sample - loss: 23.8610 - val_loss: 50.4723
一些别的解决思路：
"""
#更改初始化方法
model.add(layers.Dense(16,kernel_initializer='random_normal'))
model.add(layers.Dense(32,kernel_initializer='random_normal'))
model.add(layers.Dense(1,kernel_initializer='random_normal'))
#加入正则化惩罚项
model.add(layers.Dense(16,kernel_initializer='random_normal',kernel_regularizer=tf.keras.regularizers.l2(0.03)))
model.add(layers.Dense(32,kernel_initializer='random_normal',kernel_regularizer=tf.keras.regularizers.l2(0.03)))
model.add(layers.Dense(1,kernel_initializer='random_normal',kernel_regularizer=tf.keras.regularizers.l2(0.03)))
"""
预测值与真实值展示：
predict = model.predict(input_features) #把输入传进去，得到我们的预测结果
print(predict.shape)
# 转换日期格式
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]
# 创建一个表格来存日期和其对应的标签数值
true_data = pd.DataFrame(data = {'date': dates, 'actual': labels})
# 同理，再创建一个来存日期和其对应的模型预测值
months = features[:, feature_list.index('month')]
days = features[:, feature_list.index('day')]
years = features[:, feature_list.index('year')]
test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
test_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]
predictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': predict.reshape(-1)})
# 真实值
plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'actual')
# 预测值
plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'prediction')
plt.xticks(rotation = '60')
plt.legend()
# 图名
plt.xlabel('Date'); plt.ylabel('Maximum Temperature (F)'); plt.title('Actual and Predicted Values')
plt.show()
输出结果：
完整代码：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.keras.layers as layers
import tensorflow.keras
import warnings
warnings.filterwarnings("ignore")
# 处理时间数据
import datetime
features = pd.read_csv('temps.csv')
print(features.head())#显示读取的文件的前面几行,默认是前五行
print('数据维度:', features.shape)
# 处理时间数据
import datetime
# 分别得到年，月，日
years = features['year']
months = features['month']
days = features['day']
# datetime格式
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
#print(dates)
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]
print(dates)
# 准备画图
# 指定默认风格
plt.style.use('fivethirtyeight')
# 设置布局
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (10,10))
fig.autofmt_xdate(rotation = 45)#设置x坐标的标志为四十五度
# 标签值
ax1.plot(dates, features['actual'])
ax1.set_xlabel(''); ax1.set_ylabel('Temperature'); ax1.set_title('True Temp')
# 昨天
ax2.plot(dates, features['temp_1'])
ax2.set_xlabel(''); ax2.set_ylabel('Temperature'); ax2.set_title('Yesterday Temp')
# 前天
ax3.plot(dates, features['temp_2'])
ax3.set_xlabel('Date'); ax3.set_ylabel('Temperature'); ax3.set_title('Yesterday of Yesterday Temp')
# 一个随机预测的温度
ax4.plot(dates, features['random'])
ax4.set_xlabel('Date'); ax4.set_ylabel('Temperature'); ax4.set_title('A random Temp')
plt.tight_layout(pad=2)#自动紧凑布局
plt.show()
print(features.head().shape)
# 独热编码
features = pd.get_dummies(features) #one-hot编码，对不是数字的会进行one-hot编码
print(features.head(1).shape)
# 标签，真实的温度
labels = np.array(features['actual'])
# 在特征中去掉标签
features= features.drop('actual', axis = 1)
features= features.drop('random', axis = 1)
#print(features.columns)
# 保存标头，即每列数据对应的含义保存一下
feature_list = list(features.columns)#
# 转换成合适的格式，只保留数据了
features = np.array(features)
print(features)
print(features.shape)
#preprocessing 为预处理库，里面封装了很多预处理操作
#无量纲化：
#标准化：（x-列均值）/ 列标准差
from sklearn import preprocessing
input_features = preprocessing.StandardScaler().fit_transform(features)
#print(input_features)
#print(input_features.shape)
#数据预处理完，接下来就是Tensorflow2的新展示了，使用keras的API
#模型堆叠
model = tf.keras.Sequential()
model.add(layers.Dense(16)) #全连接层，16个神经元
model.add(layers.Dense(32)) #全连接层，32个神经元
model.add(layers.Dense(1)) #全连接层，输出层
"""
#更改初始化方法
model.add(layers.Dense(16,kernel_initializer='random_normal'))
model.add(layers.Dense(32,kernel_initializer='random_normal'))
model.add(layers.Dense(1,kernel_initializer='random_normal'))
#加入正则化惩罚项
model.add(layers.Dense(16,kernel_initializer='random_normal',kernel_regularizer=tf.keras.regularizers.l2(0.03)))
model.add(layers.Dense(32,kernel_initializer='random_normal',kernel_regularizer=tf.keras.regularizers.l2(0.03)))
model.add(layers.Dense(1,kernel_initializer='random_normal',kernel_regularizer=tf.keras.regularizers.l2(0.03)))
"""
#compile相当于对网络进行配置，指定好优化器和损失函数等
model.compile(optimizer=tf.keras.optimizers.SGD(0.001),
loss='mean_squared_error')
model.fit(input_features, labels, validation_split=0.15, epochs=30, batch_size=64)#validation_split指的是验证集比例
model.summary()#展示网络层参数
predict = model.predict(input_features) #把输入传进去，得到我们的预测结果
print(predict.shape)
# 转换日期格式
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]
# 创建一个表格来存日期和其对应的标签数值
true_data = pd.DataFrame(data = {'date': dates, 'actual': labels})
# 同理，再创建一个来存日期和其对应的模型预测值
months = features[:, feature_list.index('month')]
days = features[:, feature_list.index('day')]
years = features[:, feature_list.index('year')]
test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
test_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]
predictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': predict.reshape(-1)})
# 真实值
plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'actual')
# 预测值
plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'prediction')
plt.xticks(rotation = '60')
plt.legend()
# 图名
plt.xlabel('Date'); plt.ylabel('Maximum Temperature (F)'); plt.title('Actual and Predicted Values')
plt.show()
Over～～～～～～～～