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2024年1月15日发(作者：linux文件夹所有者更改)
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
import as nn
import onal as F
class Encoder():
def __init__(self, input_size, d_model, n_heads, e_layers,
d_ff, dropout):
super().__init__()
_size = input_size
self.d_model = d_model
self.n_heads = n_heads
self.e_layers = e_layers
self.d_ff = d_ff
t = dropout
= (input_size, d_model)
- 1 -
= List([EncoderLayer(d_model,
n_heads, d_ff, dropout) for _ in range(e_layers)])
= orm(d_model)
def forward(self, x):
x = (x)
for i in range(self.e_layers):
x = [i](x)
return (x)
class EncoderLayer():
def __init__(self, d_model, n_heads, d_ff, dropout):
super().__init__()
= MultiHeadAttention(d_model, n_heads, dropout)
1 = orm(d_model)
= PositionwiseFeedForward(d_model, d_ff,
dropout)
2 = orm(d_model)
t = t(dropout)
def forward(self, x):
x = x + t((x))
x = 1(x)
x = x + t((x))
x = 2(x)
- 2 -
return x
class MultiHeadAttention():
def __init__(self, d_model, n_heads, dropout):
super().__init__()
self.d_model = d_model
self.n_heads = n_heads
_size = d_model // n_heads
= (d_model, d_model)
= (d_model, d_model)
= (d_model, d_model)
t = t(dropout)
def forward(self, x):
batch_size, seq_len, _ = ()
q = (x).view(batch_size, seq_len, self.n_heads,
_size).transpose(1, 2)
k = (x).view(batch_size, seq_len, self.n_heads,
_size).transpose(1, 2)
v = (x).view(batch_size, seq_len, self.n_heads,
_size).transpose(1, 2)
attn_scores = (q, ose(-2, -1)) /
_size ** 0.5
attn_probs = x(attn_scores, dim=-1)
- 3 -
attn_probs = t(attn_probs)
x = (attn_probs, v)
x = ose(1, 2).contiguous().view(batch_size,
seq_len, self.d_model)
return x
class PositionwiseFeedForward():
def __init__(self, d_model, d_ff, dropout):
super().__init__()
1 = (d_model, d_ff)
2 = (d_ff, d_model)
t = t(dropout)
def forward(self, x):
x = t((1(x)))
x = 2(x)
return x
class Informer():
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__()
_size = input_size
_size = output_size
- 4 -
_in = enc_in
_in = dec_in
self.c_out = c_out
= factor
self.d_model = d_model
self.n_heads = n_heads
self.e_layers = e_layers
self.d_ff = d_ff
t = dropout
r = Encoder(input_size, d_model, n_heads,
e_layers, d_ff, dropout)
_linear = (d_model, c_out)
_encoder = PositionalEncoding(d_model, dropout)
r = Decoder(output_size, dec_in, d_model,
n_heads, e_layers, d_ff, dropout)
_linear = (d_model, output_size)
t = t(dropout)
def forward(self, x):
enc_in = x[:, :_in, :]
dec_in = x[:, _in:_in + _in, :]
dec_out = x[:, _in + _in:, :]
enc_in = _linear(enc_in)
- 5 -
enc_in = _encoder(enc_in)
enc_out = r(enc_in)
dec_in = _encoder(dec_in)
dec_out = r(dec_in, enc_out)
dec_out = _linear(dec_out)
dec_out = dec_out[:, -_size * :, :]
dec_out = dec_(-1, _size, ,
self.c_out)
dec_out = dec_(dim=-2)
return dec_out
class Decoder():
def __init__(self, output_size, dec_in, d_model, n_heads,
e_layers, d_ff, dropout):
super().__init__()
_size = output_size
_in = dec_in
self.d_model = d_model
self.n_heads = n_heads
self.e_layers = e_layers
self.d_ff = d_ff
t = dropout
= (output_size, d_model)
- 6 -
= List([DecoderLayer(d_model,
n_heads, d_ff, dropout) for _ in range(e_layers)])
= orm(d_model)
def forward(self, x, enc_out):
x = (x)
for i in range(self.e_layers):
x = [i](x, enc_out)
return (x)
class DecoderLayer():
def __init__(self, d_model, n_heads, d_ff, dropout):
super().__init__()
1 = MultiHeadAttention(d_model, n_heads,
dropout)
1 = orm(d_model)
2 = MultiHeadAttention(d_model, n_heads,
dropout)
2 = orm(d_model)
= PositionwiseFeedForward(d_model, d_ff,
dropout)
3 = orm(d_model)
t = t(dropout)
def forward(self, x, enc_out):
- 7 -
x = x + t(1(x))
x = 1(x)
x = x + t(2(x, kv=enc_out))
x = 2(x)
x = x + t((x))
x = 3(x)
return x
class PositionalEncoding():
def __init__(self, d_model, dropout, max_len=5000):
super().__init__()
t = t(dropout)
pe = (max_len, d_model)
position = (0, max_len,
dtype=).unsqueeze(1)
div_term = ((0, d_model, 2).float()
* (-(10000.0) / d_model))
pe[:, 0::2] = (position * div_term)
pe[:, 1::2] = (position * div_term)
pe = eze(0).transpose(0, 1)
er_buffer('pe', pe)
def forward(self, x):
x = x + [:(0), :]
- 8 -
return t(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。使用这个代码可以更方便地构建和训练Informer模型，提高时序预测任务的准确性。
- 9 -
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