import torch
import torch.nn as nn
from transformers import AutoModel
class Autoformer(nn.Module):
def __init__(self, d_model=512, nhead=8, num_encoder_layers=6):
super(Autoformer, self).__init__()
self.encoder_layer = nn.TransformerEncoderLayer(d_model, nhead)
self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_encoder_layers)
self.linear = nn.Linear(d_model, 1)
def forward(self, src):
memory = self.transformer_encoder(src)
output = self.linear(memory)
return output
class FEDformer(nn.Module):
def __init__(self, d_model=512, nhead=8, num_encoder_layers=6):
super(FEDformer, self).__init__()
self.transformer = AutoModel.from_pretrained('google/electra-small-discriminator', output_loading=True)
self.linear = nn.Linear(d_model, 1)
def forward(self, src):
memory = self.transformer(src)[0] # Transformer output
output = self.linear(memory)
return output
class PatchTS(nn.Module):
def __init__(self, d_model=512, nhead=8, num_encoder_layers=6):
super(PatchTS, self).__init__()
self.transformer = AutoModel.from_pretrained('google/electra-small-discriminator', output_loading=True)
self.linear = nn.Linear(d_model, 1)
def forward(self, src):
memory = self.transformer(src)[0] # Transformer output
output = self.linear(memory)
return output
# 示例：
# 假设 `src` 是一个Tensor，表示输入序列。
src = torch.randn(10, 8, 512) # 假设batch size为10，序列长度为8，embedding大小为512
autoformer = Autoformer()
fedformer = FEDformer()
patchts = PatchTS()
# 预测股价
autoformer_output = autoformer(src)
fedformer_output = fedformer(src)
patchts_output = patchts(src)在这个例子中，我们定义了三个类，分别代表Autoformer、FEDformer和PatchTS模型。每个类的__init__方法定义了模型的结构，forward方法定义了模型的前向传播过程。这里使用了预训练的Transformer模型google/electra-small-discriminator作为基础模型。在forward方法中，我们通过调用预训练的Transformer模型，获取输入序列的表示，然后将其传递给全连接层进行股价预测。
注意：这个例子假设你已经有了一个预训练的Transformer模型，并且你知道如何加载和使用它。在实际应用中，你可能需要对模型进行适配，或者进行一些其他的预处理步骤。