import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.optim as optim
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
features = pd.read_csv('temps.csv')
#看看数据长什么样子
features.head()
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
- friend:这一列可能是凑热闹的,你的朋友猜测的可能值,咱们不管它就好了
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# 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)] dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in 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) # 标签值 ax1.plot(dates, features['actual']) ax1.set_xlabel(''); ax1.set_ylabel('Temperature'); ax1.set_title('Max Temp') # 昨天 ax2.plot(dates, features['temp_1']) ax2.set_xlabel(''); ax2.set_ylabel('Temperature'); ax2.set_title('Previous Max Temp') # 前天 ax3.plot(dates, features['temp_2']) ax3.set_xlabel('Date'); ax3.set_ylabel('Temperature'); ax3.set_title('Two Days Prior Max Temp') # 我的逗逼朋友 ax4.plot(dates, features['friend']) ax4.set_xlabel('Date'); ax4.set_ylabel('Temperature'); ax4.set_title('Friend Estimate') plt.tight_layout(pad=2)
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# 独热编码 features = pd.get_dummies(features) features.head(5)
# 标签 labels = np.array(features['actual']) # 在特征中去掉标签 features= features.drop('actual', axis = 1) # 名字单独保存一下,以备后患 feature_list = list(features.columns) # 转换成合适的格式 features = np.array(features)
from sklearn import preprocessing input_features = preprocessing.StandardScaler().fit_transform(features)
构建网络模型
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x = torch.tensor(input_features, dtype = float) y = torch.tensor(labels, dtype = float) # 权重参数初始化 weights = torch.randn((14, 128), dtype = float, requires_grad = True) biases = torch.randn(128, dtype = float, requires_grad = True) weights2 = torch.randn((128, 1), dtype = float, requires_grad = True) biases2 = torch.randn(1, dtype = float, requires_grad = True) learning_rate = 0.001 losses = [] for i in range(1000): # 计算隐层 hidden = x.mm(weights) + biases # 加入激活函数 hidden = torch.relu(hidden) # 预测结果 predictions = hidden.mm(weights2) + biases2 # 通计算损失 loss = torch.mean((predictions - y) ** 2) losses.append(loss.data.numpy()) # 打印损失值 if i % 100 == 0: print('loss:', loss) #返向传播计算 loss.backward() #更新参数 weights.data.add_(- learning_rate * weights.grad.data) biases.data.add_(- learning_rate * biases.grad.data) weights2.data.add_(- learning_rate * weights2.grad.data) biases2.data.add_(- learning_rate * biases2.grad.data) # 每次迭代都得记得清空 weights.grad.data.zero_() biases.grad.data.zero_() weights2.grad.data.zero_() biases2.grad.data.zero_()
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更简单的构建网络模型
input_size = input_features.shape[1] hidden_size = 128 output_size = 1 batch_size = 16 my_nn = torch.nn.Sequential( torch.nn.Linear(input_size, hidden_size), torch.nn.Sigmoid(), torch.nn.Linear(hidden_size, output_size), ) cost = torch.nn.MSELoss(reduction='mean') optimizer = torch.optim.Adam(my_nn.parameters(), lr = 0.001)
# 训练网络 losses = [] for i in range(1000): batch_loss = [] # MINI-Batch方法来进行训练 for start in range(0, len(input_features), batch_size): end = start + batch_size if start + batch_size < len(input_features) else len(input_features) xx = torch.tensor(input_features[start:end], dtype = torch.float, requires_grad = True) yy = torch.tensor(labels[start:end], dtype = torch.float, requires_grad = True) prediction = my_nn(xx) loss = cost(prediction, yy) optimizer.zero_grad() loss.backward(retain_graph=True) optimizer.step() batch_loss.append(loss.data.numpy()) # 打印损失 if i % 100==0: losses.append(np.mean(batch_loss)) print(i, np.mean(batch_loss))
预测训练结果
x = torch.tensor(input_features, dtype = torch.float) predict = my_nn(x).data.numpy() # 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');
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