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 torchimport torch.nn as nnimport torch.nn.functional as Fclass Encoder(nn.Module):def __init__(self, input_size, d_model, n_heads, e_layers, d_ff, dropout):super().__init__()self.input_size = input_sizeself.d_model = d_modelself.n_heads = n_headsself.e_layers = e_layersself.d_ff = d_ffself.dropout = dropoutself.embed = nn.Linear(input_size, d_model)yers = nn.ModuleList([EncoderLayer(d_model,n_heads, d_ff, dropout) for _ in range(e_layers)])self.norm = yerNorm(d_model)def forward(self, x):x = self.embed(x)for i in range(self.e_layers):x = yers[i](x)return self.norm(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) self.norm1 = yerNorm(d_model)self.mlp = PositionwiseFeedForward(d_model, d_ff, dropout)self.norm2 = yerNorm(d_model)self.dropout = nn.Dropout(dropout)def forward(self, x):x = x + self.dropout(self.attn(x))x = self.norm1(x)x = x + self.dropout(self.mlp(x))x = self.norm2(x)return xclass MultiHeadAttention(nn.Module):def __init__(self, d_model, n_heads, dropout):super().__init__()self.d_model = d_modelself.n_heads = n_headsself.head_size = d_model // n_headsself.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 ** 0.5attn_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 xclass 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.dropout(F.relu(self.fc1(x)))x = self.fc2(x)return xclass 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_sizeself.output_size = output_sizeself.enc_in = enc_inself.dec_in = dec_inself.c_out = c_outself.factor = factorself.d_model = d_modelself.n_heads = n_headsself.e_layers = e_layersself.d_ff = d_ffself.dropout = dropoutself.encoder = Encoder(input_size, d_model, n_heads,e_layers, d_ff, dropout)self.enc_linear = nn.Linear(d_model, c_out)self.pos_encoder = PositionalEncoding(d_model, dropout) self.decoder = Decoder(output_size, dec_in, d_model,n_heads, e_layers, d_ff, dropout)self.dec_linear = nn.Linear(d_model, output_size)self.dropout = nn.Dropout(dropout)def forward(self, x):enc_in = x[:, :self.enc_in, :]dec_in = x[:, self.enc_in:self.enc_in + self.dec_in, :] dec_out = x[:, self.enc_in + self.dec_in:, :]enc_in = self.enc_linear(enc_in)enc_in = self.pos_encoder(enc_in)enc_out = self.encoder(enc_in)dec_in = self.pos_encoder(dec_in)dec_out = self.decoder(dec_in, enc_out)dec_out = self.dec_linear(dec_out)dec_out = dec_out[:, -self.output_size * self.factor:, :] dec_out = dec_out.view(-1, self.output_size, self.factor, self.c_out)dec_out = dec_out.mean(dim=-2)return dec_outclass Decoder(nn.Module):def __init__(self, output_size, dec_in, d_model, n_heads, e_layers, d_ff, dropout):super().__init__()self.output_size = output_sizeself.dec_in = dec_inself.d_model = d_modelself.n_heads = n_headsself.e_layers = e_layersself.d_ff = d_ffself.dropout = dropoutself.embed = nn.Linear(output_size, d_model)yers = nn.ModuleList([DecoderLayer(d_model,n_heads, d_ff, dropout) for _ in range(e_layers)])self.norm = yerNorm(d_model)def forward(self, x, enc_out):x = self.embed(x)for i in range(self.e_layers):x = yers[i](x, enc_out)return self.norm(x)class DecoderLayer(nn.Module):def __init__(self, d_model, n_heads, d_ff, dropout): super().__init__()self.attn1 = MultiHeadAttention(d_model, n_heads, dropout)self.norm1 = yerNorm(d_model)self.attn2 = MultiHeadAttention(d_model, n_heads, dropout)self.norm2 = yerNorm(d_model)self.mlp = PositionwiseFeedForward(d_model, d_ff, dropout)self.norm3 = yerNorm(d_model)self.dropout = nn.Dropout(dropout)def forward(self, x, enc_out):x = x + self.dropout(self.attn1(x))x = self.norm1(x)x = x + self.dropout(self.attn2(x, kv=enc_out))x = self.norm2(x)x = x + self.dropout(self.mlp(x))x = self.norm3(x)return xclass PositionalEncoding(nn.Module):def __init__(self, d_model, dropout, max_len=5000):super().__init__()self.dropout = nn.Dropout(dropout)pe = torch.zeros(max_len, d_model)position = torch.arange(0, max_len,dtype=torch.float).unsqueeze(1)div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))pe[:, 0::2] = torch.sin(position * div_term)pe[:, 1::2] = torch.cos(position * div_term)pe = pe.unsqueeze(0).transpose(0, 1)self.register_buffer('pe', pe)def forward(self, x):x = x + self.pe[:x.size(0), :]return self.dropout(x)```这段代码实现了Informer模型的所有组件，包括编码器、解码器、多头注意力、前馈网络和位置编码等。
