唐宇迪课程学习笔记
回归问题预测
- Tensordlow2版本中将大量使用keras的简介建模方法
import numpy as np
import pandas as pd
import marplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers
import tensorflow.keras
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
features = pd.read_csv('temps.csv')
features.head()
数据表中
- year,month,day,week 分别表示具体的时间
- temp_2：前天的最高气温
- temp_1：昨天的最高气温
- 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
- friend：这一列可能是凑热闹的，你的朋友猜测的可能值，咱们不管它就好
print('数据维度:', features.shape)
数据维度：（348, 9）
import datetime
years = features['year']
months = features['month']
days = features['day']
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for years, month, day in zip(years, months, days)]
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]
dates[:5]
[datatime.datetime(2016, 1, 1, 0, 0),
datatime.datetime(2016, 1, 2, 0, 0),
datatime.datetime(2016, 1, 3, 0, 0),
datatime.datetime(2016, 1, 4, 0, 0),
datatime.datetime(2016, 1, 5, 0, 0)]
plt.style.use('fivethirtyeight')
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, cols=2, figsize = (10, 10))
fig.autofmt_xdate(rotation = 45)
ax1.plot(dates, features['actual'])
ax1.set_xlabel('');ax1.set_ylabel('Temperature');ax1.set_title('MAX Temp')
ax2.plot(dates, features['tenp_1'])
ax2.set_xlabel('');ax2.set_ylabel('Temperature');ax2.set_title('Previous Max Temp')
ax3.plot(dates, features['temp_2'])
ax3.set_xlabel('Date');ax3.set_ylabel('Temperature');ax3.set_title('Two Days Prior Max Temp')
ax4.plot(dates, features['friend'])
ax4.set_xlabel('Date');ax4.set_ylabel('Temperature');ax4,set_title('Friend Estimate')
plt.tight_layout(pad=2)
features = pd.get_dumies(features)
features.head(5)
labels = np.array(features['actual'])
features = features.drop('actual', axis=1)
feature_list = list(features.columns)
features = np.array(features)
features,shape
(348, 14)
from sklearn import preprocessing
input_features = preprocessing.StandardScaler().fit_transform(features)
基于Keras构建网络模型
一些常用参数已经列出，如下所示：
- activation：激活函数的选择，一般常用relu
- kernel_initializer, bias_initializer：权重与偏置参数的初始化方法，有时候不收敛换种初始化突然好使了…玄学
- kernel_regularizer, bias_regularizer：要不要加入正则化
- inputs：输入，可以自己制定，也可以让网络自动选
- units：神经元个数
按顺序构造网络模型
model = tf.keras.Sequential()
model.add(layers.Dense(16))
model.add(layers.Dense(32))
model.add(layers.Dense(1))
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=0.25, epochs=10, batch_size=64)
似乎存在一些问题，模型还没有完全收敛，能不能调些参数呢
model.summary()
更改初始化方法后
model = tf.keras.Sequential()
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'))
model.compile(optimizer=tf.keras.optimizers.SGD(0.001),
loss='mean_squared_error')
model.fit(input_features, labels, validation_split=0.25, epochs=100, batch_size=64)
加入正则化惩罚项
model = tf.keras.Sequential()
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)))
model.compile(optimizer=tf.keras.optimizers.SGD(0.001),
loss='mean_squared_error')
model.fit(input_features, labels, validation_split=0.25, epochs=100, batch_size=64)
加入正则项，可以使W更加平滑
预测模型结果
predict = model.predict(input_features)
predict.shape
(348, 1)
测试结果并进行展示
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for years, month, day in zip(years, months, days)]
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]
true_data = pd.DataFrame(data = {'date':dates, 'actual':labels})
months = features[:, feature_list.index('month')]
days = features[:, feature_list.index('day')]
year = features[:, feature_list.index('year')]
test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for years, month, day in zip(years, months, days)]
tset_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]
predications_data = pd.DataFrame(data = {'date':dates, 'actual':labels})
plt.plot(ture_data['date'], true_data['actual'], 'b-', label='actual')
plt.plot(predictions_data['data'], predictions_data['prediction'], 'ro', label ='prediction')
plt.xticks(rotation = '60')
plt.legend()
plt.xlabel('Date);plt.ylabel('Maximum Temperature (F)');plt.title('Actual and Predicted Values');
主要看验证集和测试集上的 loss 值
Original: https://blog.csdn.net/qq_51491920/article/details/124830049
Author: #NAME?
Title: 搭建神经网络进行气温预测
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