最近在网上找到了一个使用LSTM 网络解决 世界银行中各国 GDP预测的一个问题，感觉比较实用，毕竟这是找到的唯一一个可以正确运行的程序。
#encoding:UTF-8import pandas as pd
from pandas_datareader import wbimport torch
import torch.nn
import torch.optim#读取数据
countries = ['BR', 'CA', 'CN', 'FR', 'DE', 'IN', 'IL', 'JP', 'SA', 'GB', 'US',]
dat = wb.download(indicator='NY.GDP.PCAP.KD',
country=countries, start=1970, end=2016)df = dat.unstack().T
df.index = df.index.droplevel(0).astype(int)
#print(df)#搭建神经网络
class Net(torch.nn.Module): def __init__(self, input_size, hidden_size):
super(Net, self).__init__()
self.rnn = torch.nn.LSTM(input_size, hidden_size)
self.fc = torch.nn.Linear(hidden_size, 1) def forward(
self, x):
x = x[:, :, None]
x, _ = self.rnn(x)
x = self.fc(x)
x = x[:, :, 0]
return xnet = Net(input_size=1, hidden_size=5)
#print(net)#训练神经网络
# 数据归一化
df_scaled = df / df.loc[2000]# 确定训练集和测试集
years = df.index
train_seq_len = sum((years >= 1971) & (years <= 2000))
test_seq_len = sum(years > 2000)print ('训练集长度 = {}, 测试集长度 = {}'.format(
train_seq_len, test_seq_len))# 确定训练使用的特征和标签
inputs = torch.tensor(df_scaled.iloc[:-1].values, dtype=torch.float32)
labels = torch.tensor(df_scaled.iloc[1:].values, dtype=torch.float32)# 训练网络
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(net.parameters())
for step in range(10001):
if step:
optimizer.zero_grad()
train_loss.backward()
optimizer.step() preds = net(inputs)
train_preds = preds[:train_seq_len]
train_labels = labels[:train_seq_len]
train_loss = criterion(train_preds, train_labels) test_preds = preds[-test_seq_len]
test_labels = labels[-test_seq_len]
test_loss = criterion(test_preds, test_labels) if step % 500 == 0:
print ('第{}次迭代: loss (训练集) = {}, loss (测试集) = {}'.format(
step, train_loss, test_loss))preds = net(inputs)
df_pred_scaled = pd.DataFrame(preds.detach().numpy(),
index=years[1:], columns=df.columns)
df_pred = df_pred_scaled * df.loc[2000]
df_pred.loc[2001:]
