pytorch informer

PyTorch Informer是一个基于PyTorch框架实现的时间序列预测模型。它是一种基于Transformer注意力机制和自回归机制融合的模型，被广泛应用于各种时间序列预测场景，如财务预测、交通预测、气象预测等。

PyTorch Informer的核心思想是将时序数据映射为矩阵形式，利用注意力机制来建立全局与局部之间的联系。通过利用多头注意力机制，能够更好地获取时序数据中的关键特征，从而提高预测的准确率。此外，PyTorch Informer还能够同时对多个时间序列进行预测，能够更好地应对实际应用中的需求。

PyTorch Informer具有许多优点，如更高的预测精度、更快的训练速度和更高的数据处理效率。此外，PyTorch Informer还具有良好的可扩展性，能够方便地对模型进行修改和优化。

总之，PyTorch Informer是一个具有强大预测能力和良好可扩展性的时间序列预测模型，已经得到了广泛的应用和研究。

相关问题

pytorch实现informer

Informer是一种用于时间序列预测的模型，它可以处理多变量和多步预测问题。下面是使用PyTorch实现Informer模型的步骤：

1. 安装依赖库

!pip install torch
!pip install tqdm
!pip install numpy
!pip install pandas
!pip install matplotlib
!pip install sklearn

2. 导入必要的库

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pandas as pd
from tqdm import tqdm
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler

3. 定义Informer模型

class Encoder(nn.Module):
    def __init__(self, input_size, hidden_size, num_heads, seq_len):
        super(Encoder, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.seq_len = seq_len
        self.multihead_attn = nn.MultiheadAttention(hidden_size, num_heads)
        self.layer_norm1 = nn.LayerNorm(hidden_size)
        self.pos_ffn = nn.Linear(hidden_size, hidden_size * 4)
        self.layer_norm2 = nn.LayerNorm(hidden_size)

    def forward(self, x):
        residual = x
        x = x.permute(1, 0, 2)
        attn_output, _ = self.multihead_attn(x, x, x)
        x = self.layer_norm1(residual + attn_output.permute(1, 0, 2))
        residual = x
        x = self.pos_ffn(x)
        x = F.gelu(x)
        x = self.pos_ffn(x)
        x = self.layer_norm2(residual + x)
        return x

class Decoder(nn.Module):
    def __init__(self, input_size, hidden_size, num_heads, seq_len):
        super(Decoder, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.seq_len = seq_len
        self.masked_multihead_attn = nn.MultiheadAttention(hidden_size, num_heads)
        self.layer_norm1 = nn.LayerNorm(hidden_size)
        self.multihead_attn = nn.MultiheadAttention(hidden_size, num_heads)
        self.layer_norm2 = nn.LayerNorm(hidden_size)
        self.pos_ffn = nn.Linear(hidden_size, hidden_size * 4)
        self.layer_norm3 = nn.LayerNorm(hidden_size)

    def forward(self, x, encoder_output):
        residual = x
        x = x.permute(1, 0, 2)
        attn_output, _ = self.masked_multihead_attn(x, x, x, attn_mask=self._get_mask(x))
        x = self.layer_norm1(residual + attn_output.permute(1, 0, 2))
        residual = x
        x = self.multihead_attn(x, encoder_output, encoder_output)
        x = self.layer_norm2(residual + x)
        residual = x
        x = self.pos_ffn(x)
        x = F.gelu(x)
        x = self.pos_ffn(x)
        x = self.layer_norm3(residual + x)
        return x

    def _get_mask(self, x):
        mask = torch.ones(self.seq_len, self.seq_len).to(x.device)
        mask = torch.tril(mask)
        return mask

class Informer(nn.Module):
    def __init__(self, input_size, output_size, hidden_size, num_encoder_layers, num_decoder_layers, num_heads, seq_len):
        super(Informer, self).__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.hidden_size = hidden_size
        self.num_encoder_layers = num_encoder_layers
        self.num_decoder_layers = num_decoder_layers
        self.num_heads = num_heads
        self.seq_len = seq_len

        self.encoder_layers = nn.ModuleList()
        self.decoder_layers = nn.ModuleList()
        for i in range(num_encoder_layers):
            self.encoder_layers.append(Encoder(input_size, hidden_size, num_heads, seq_len))
        for i in range(num_decoder_layers):
            self.decoder_layers.append(Decoder(input_size, hidden_size, num_heads, seq_len))
        self.linear = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        encoder_output = x
        for encoder_layer in self.encoder_layers:
            encoder_output = encoder_layer(encoder_output)
        decoder_output = x[:, -1:, :]
        for decoder_layer in self.decoder_layers:
            decoder_output = decoder_layer(decoder_output, encoder_output)
        output = self.linear(decoder_output[:, -1:, :])
        return output

4. 定义数据预处理函数

def prepare_data(data, seq_len, train_ratio):
    data = data.values
    scaler = StandardScaler()
    data = scaler.fit_transform(data)
    data_x = []
    data_y = []
    for i in range(len(data) - seq_len):
        data_x.append(data[i:i+seq_len])
        data_y.append(data[i+seq_len])
    data_x = np.array(data_x)
    data_y = np.array(data_y)
    train_size = int(len(data_x) * train_ratio)
    train_x = data_x[:train_size, :, :]
    train_y = data_y[:train_size, :]
    test_x = data_x[train_size:, :, :]
    test_y = data_y[train_size:, :]
    return train_x, train_y, test_x, test_y, scaler

5. 定义训练函数

def train(model, train_x, train_y, test_x, test_y, epochs, lr):
    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
    loss_func = nn.MSELoss()
    train_loss_list = []
    test_loss_list = []
    for epoch in tqdm(range(epochs)):
        model.train()
        train_loss = 0
        for i in range(train_x.shape[0]):
            optimizer.zero_grad()
            x = torch.Tensor(train_x[i]).unsqueeze(0)
            y = torch.Tensor(train_y[i]).unsqueeze(0)
            output = model(x)
            loss = loss_func(output, y)
            loss.backward()
            optimizer.step()
            train_loss += loss.item()
        train_loss /= train_x.shape[0]
        train_loss_list.append(train_loss)

        model.eval()
        test_loss = 0
        with torch.no_grad():
            for i in range(test_x.shape[0]):
                x = torch.Tensor(test_x[i]).unsqueeze(0)
                y = torch.Tensor(test_y[i]).unsqueeze(0)
                output = model(x)
                loss = loss_func(output, y)
                test_loss += loss.item()
        test_loss /= test_x.shape[0]
        test_loss_list.append(test_loss)

        print('Epoch [{}/{}], train loss: {:.4f}, test loss: {:.4f}'.format(epoch+1, epochs, train_loss, test_loss))
    return train_loss_list, test_loss_list

6. 载入数据集并调用训练函数

data = pd.read_csv('data.csv', index_col=0)
train_x, train_y, test_x, test_y, scaler = prepare_data(data, seq_len=96, train_ratio=0.8)
model = Informer(input_size=train_x.shape[-1], output_size=train_y.shape[-1], hidden_size=256, num_encoder_layers=2, num_decoder_layers=2, num_heads=8, seq_len=96)
train_loss_list, test_loss_list = train(model, train_x, train_y, test_x, test_y, epochs=50, lr=0.001)

7. 绘制损失曲线

plt.plot(train_loss_list, label='train loss')
plt.plot(test_loss_list, label='test loss')
plt.legend()
plt.show()

这样就可以使用PyTorch实现Informer模型了。

informer pytorch

对于问题中提到的"informer pytorch"，很抱歉，我无法根据提供的引用内容提供相关的答案。请提供更多背景信息或具体问题以帮助我更好地回答你的问题。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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