深度学习 循环神经网络 LSTM 示例

 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-8

import pandas as pd
from pandas_datareader import wb

import 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 x

net = 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:]