pytorch p12 简化版本气温预测

Posted 2022-02-05 bohu83

数据准备工作跟之前的 一样， 主要是构建 神经网络 模块 变得更简化了，使用 了torch.nn模块。

包括构建模型，使用torch.nn.MseLoss作为损失值, 使用torch.optim.Adam构造优化器 。

完整 的代码 如下：

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')

features = pd.read_csv('temps.csv')

# 可视化图形
print(features.head(5))

print("数据维度:", features.shape)
print("数据类型:", type(features))

# 处理时间数据
import datetime

# 分别得到年/月/日
years = features['year']
months = features['month']
days = features['day']

# 处理成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]



#  编码转换
features = pd.get_dummies(features)
print(features.head())

# 标签
labels = np.array(features['actual'])

# 去掉便签后的数据
features = features.drop('actual',axis=1)

features_list = list(features.columns)

# 将train转成合适的格式
features = np.array(features)

# 对特征进行标准化操作
from sklearn import preprocessing

input_feature = preprocessing.StandardScaler().fit_transform(features)


import torch
import torch.nn as nn

input_size = input_feature.shape[1]
hidden_size = 128
output_size = 1
batch_size = 16
epochs = 1000

my_nn = nn.Sequential(
    torch.nn.Linear(input_size, hidden_size),
    torch.nn.Sigmoid(),
    torch.nn.Linear(hidden_size, output_size)
)

x = torch.tensor(input_feature, dtype=torch.float)
y = torch.tensor(labels, dtype=torch.float)

cost = torch.nn.MSELoss(reduction="mean")
optimizer = torch.optim.Adam(my_nn.parameters(), lr=0.001)
#训练网络
for i in range(epochs):
    batch_mean = []
    for start in range(0, len(input_feature), batch_size):
        end = start + batch_size if start + batch_size < len(input_feature) else len(input_feature)
        xx = x[start:end]
        yy = y[start:end]
        predict = my_nn(xx)
        loss = cost(yy, predict)
        optimizer.zero_grad()
        loss.backward(retain_graph=True)
        optimizer.step()
        batch_mean.append(loss.data.numpy())
    if i % 100 == 0:
        print(np.mean(batch_mean))


for i in my_nn.parameters():
    print(i.data.numpy())

x = torch.tensor(input_feature, dtype=torch.float)
predict = np.squeeze(my_nn(x).data.numpy())

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]

true_data = pd.DataFrame(data = "date":dates, "actual":labels)

predict_data = pd.DataFrame(data= "date":dates, "predict":predict)

plt.plot(true_data["date"], true_data["actual"], "b-", label='actual')
plt.plot(predict_data['date'], predict_data['predict'], 'ro', label='predict')
plt.xticks(rotation=60)
plt.title("Temperature Predict")
plt.xlabel("Date")
plt.ylabel("Temperature")
plt.show()