搭建PyTorch神经网络进行气温预测

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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_()

更简单的构建网络模型

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  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

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  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');

https://gitee.com/code-wenjiahao/neural-network-practical-classification-and-regression-tasks