基于pytorch 构建神经网络进行气温预测

import numpy as np import pandas as pd import matplotlib.pyplot as plt import torch import warnings warnings.filterwarnings('ignore') %matplotlib inline path = 'E:/nlp课件/test_data/temps.csv' features = pd.read_csv(path) features.head() yearmonthdayweektemp_2temp_1averageactualfriend0201611Fri454545.645291201612Sat444545.744612201613Sun454445.841563201614Mon444145.940534201615Tues414046.04441

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：标签值，当天的真实最高温度 print('数据维度：', features.shape) 数据维度： (348, 9) # 处理时间 years = features['year'] month = features['month'] day = features['day'] dates = [str(int(years)) + '-' + str(int(month)) + '-' + str(int(day)) for years, month, day in zip(years, month, day)] from datetime import datetime dates = [datetime.strptime(date, '%Y-%m-%d') for date in dates] dates[:5] [datetime.datetime(2016, 1, 1, 0, 0), datetime.datetime(2016, 1, 2, 0, 0), datetime.datetime(2016, 1, 3, 0, 0), datetime.datetime(2016, 1, 4, 0, 0), datetime.datetime(2016, 1, 5, 0, 0)] # 生成图像 # 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)

# one-hot features = pd.get_dummies(features) features[:5] yearmonthdaytemp_2temp_1averageactualfriendweek_Friweek_Monweek_Satweek_Sunweek_Thursweek_Tuesweek_Wed0201611454545.6452910000001201612444545.7446100100002201613454445.8415600010003201614444145.9405301000004201615414046.044410000010 # 目标值 labels = np.array(features['actual']) # 在特征之中去掉标签 features = features.drop('actual', axis = 1) # 保存列名 features_list = list(features.columns) # 转换格式 features = np.array(features) features.shape (348, 14) from sklearn.preprocessing import StandardScaler input_features = StandardScaler().fit_transform(features) input_features[0] array([ 0. , -1.5678393 , -1.65682171, -1.48452388, -1.49443549, -1.3470703 , -1.98891668, 2.44131112, -0.40482045, -0.40961596, -0.40482045, -0.40482045, -0.41913682, -0.40482045])

构建网络模型

x = torch.tensor(input_features, dtype = float) y = torch.tensor(labels, dtype = float) # 权重参数初始化 [348,14] * [14, 128] * [128] * [128, 1] * [1] 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 # 激活函数 hidden = torch.relu(hidden) # 预测结果 predictions = hidden.mm(weights2) + biases2 # 计算损失 - MSE loss = torch.mean((predictions - y)**2) losses.append(loss.data.numpy) # 打印损失 if i % 100 == 0: print('loss:', loss) # 反向传播 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) biases2.data.add_(- learning_rate * biases2) # 更新后梯度置0，否则会累加 weights.grad.data.zero_() biases.grad.data.zero_() weights2.grad.data.zero_() biases2.grad.data.zero_() loss: tensor(4769.2916, dtype=torch.float64, grad_fn=<MeanBackward0>) loss: tensor(168.6445, dtype=torch.float64, grad_fn=<MeanBackward0>) loss: tensor(152.0681, dtype=torch.float64, grad_fn=<MeanBackward0>) loss: tensor(147.8071, dtype=torch.float64, grad_fn=<MeanBackward0>) loss: tensor(146.4026, dtype=torch.float64, grad_fn=<MeanBackward0>) loss: tensor(146.3492, dtype=torch.float64, grad_fn=<MeanBackward0>) loss: tensor(147.1898, dtype=torch.float64, grad_fn=<MeanBackward0>) loss: tensor(148.8380, dtype=torch.float64, grad_fn=<MeanBackward0>) loss: tensor(151.3747, dtype=torch.float64, grad_fn=<MeanBackward0>) loss: tensor(154.9829, dtype=torch.float64, grad_fn=<MeanBackward0>)

序列化容器构建网络模型

import torch.nn as nn from torch.optim import Adam input_size = input_features.shape[1] hidden_size = 128 output_size = 1 batch_size = 16 my_nn = nn.Sequential( nn.Linear(input_size, hidden_size), nn.Sigmoid(), nn.Linear(hidden_size, output_size) ) cost = nn.MSELoss(reduction= 'mean') optimizer = Adam(my_nn.parameters(), lr = learning_rate) # 训练网络 losses = [] for i in range(1000): batch_loss = [] # mini_batch 方式进行训练 for start in range(0, len(input_features), batch_size): end = start + batch_size if batch_size + start < 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) # 前向传播 predictions = my_nn(xx) # 计算损失 loss = cost(predictions, yy) # 梯度置0 optimizer.zero_grad() # 反向传播 loss.backward(retain_graph = True) # 更新参数 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)) 0 3980.642 100 37.847748 200 35.684933 300 35.318283 400 35.14371 500 35.006382 600 34.884396 700 34.761875 800 34.633102 900 34.49755

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(years)) + '-' + str(int(month)) + '-' + str(int(day)) for years, month, day in zip(years, month, day)] dates = [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[:, features_list.index('month')] days = features[:, features_list.index('day')] years = features[:, features_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.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');