informer模型代码
    Informer模型是一种用于时序预测任务的深度学习模型，可以用于预测多个时间步长的未来值。它采用自注意力机制来建模输入序列的长期依赖关系，并结合了一些新的技术来增强其性能。
    Informer模型代码可以使用Python编写，并使用TensorFlow或PyTorch等深度学习框架实现。下面是一个使用PyTorch实现Informer模型的代码示例：
    ```
    import torch
    as nn
    functional as F
    class Encoder(nn.Module):
    def __init__(self, input_size, d_model, n_heads, e_layers, d_ff, dropout):
decoder    super().__init__()
    self.input_size = input_size
    self.d_model = d_model
    self.n_heads = n_heads
    self.e_layers = e_layers
    self.d_ff = d_ff
    self.dropout = dropout
    bed = nn.Linear(input_size, d_model)
    self.layers = nn.ModuleList([EncoderLayer(d_model, n_heads, d_ff, dropout) for _ in range(e_layers)])
    = nn.LayerNorm(d_model)
    def forward(self, x):
    x = bed(x)
    for i in range(self.e_layers):
    x = self.layers[i](x)
    (x)
    class EncoderLayer(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, dropout):
    super().__init__()
    self.attn = MultiHeadAttention(d_model, n_heads, dropout)
    1 = nn.LayerNorm(d_model)
    self.mlp = PositionwiseFeedForward(d_model, d_ff, dropout)
    2 = nn.LayerNorm(d_model)
    self.dropout = nn.Dropout(dropout)
    def forward(self, x):
    x = x + self.dropout(self.attn(x))
    x = 1(x)
    x = x + self.dropout(self.mlp(x))
    x = 2(x)
    return x
    class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, n_heads, dropout):
    super().__init__()
    self.d_model = d_model
    self.n_heads = n_heads
    self.head_size = d_model // n_heads
    self.query = nn.Linear(d_model, d_model)
    self.key = nn.Linear(d_model, d_model)
    self.value = nn.Linear(d_model, d_model)
    self.dropout = nn.Dropout(dropout)
    def forward(self, x):
    batch_size, seq_len, _ = x.size()
    q = self.query(x).view(batch_size, seq_len, self.n_heads, self.head_size).transpose(1, 2)
    k = self.key(x).view(batch_size, seq_len, self.n_heads, self.head_size).transpose(1, 2)
    v = self.value(x).view(batch_size, seq_len, self.n_heads, self.head_size).transpose(1, 2)
    attn_scores = torch.matmul(q, k.transpose(-2, -1)) / self.head_size ** 0.5
    attn_probs = F.softmax(attn_scores, dim=-1)
    attn_probs = self.dropout(attn_probs)
    x = torch.matmul(attn_probs, v)
    x = x.transpose(1, 2).contiguous().view(batch_size, seq_len, self.d_model)
    return x
    class PositionwiseFeedForward(nn.Module):
    def __init__(self, d_model, d_ff, dropout):
    super().__init__()
    self.fc1 = nn.Linear(d_model, d_ff)
    self.fc2 = nn.Linear(d_ff, d_model)
    self.dropout = nn.Dropout(dropout)
    def forward(self, x):
    x = self.lu(self.fc1(x)))
    x = self.fc2(x)
    return x
    class Informer(nn.Module):
    def __init__(self, input_size, output_size, enc_in, dec_in, c_out, factor, d_model, n_heads, e_layers, d_ff, dropout):
    super().__init__()
    self.input_size = input_size
    self.output_size = output_size
    _in = enc_in
    self.dec_in = dec_in
    self.c_out = c_out
    self.factor = factor
    self.d_model = d_model
    self.n_heads = n_heads
    self.e_layers = e_layers
    self.d_ff = d_ff
    self.dropout = dropout
    der = Encoder(input_size, d_model, n_heads, e_layers, d_ff, dropout)
    _linear = nn.Linear(d_model, c_out)

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