用pytorch 写出以下代码:长度为54 × 1024的矢量通过五个transformerencoder到108的长度
时间: 2023-09-03 13:27:48 浏览: 157
### 回答1:
import torch
vector_length = 54*1024
transformer_encoders = 5
output_length = 108
x = torch.randn(vector_length)
for i in range(transformer_encoders):
x = torch.nn.functional.relu(torch.nn.Linear(vector_length, output_length)(x))
vector_length = output_length print(x)
### 回答2:
使用PyTorch编写代码,将长度为54 × 1024的矢量通过五个TransformerEncoder层转换为长度为108的矢量。代码如下:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class TransformerEncoder(nn.Module):
def __init__(self, input_dim, hidden_dim):
super(TransformerEncoder, self).__init__()
self.attention = nn.MultiheadAttention(input_dim, num_heads=8)
self.feed_forward = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, input_dim)
)
self.layer_norm1 = nn.LayerNorm(input_dim)
self.layer_norm2 = nn.LayerNorm(input_dim)
def forward(self, x):
residual = x
x = self.layer_norm1(x)
x = self.attention(x, x, x)[0] + residual
residual = x
x = self.layer_norm2(x)
x = self.feed_forward(x) + residual
return x
class VectorEncoder(nn.Module):
def __init__(self, input_dim, hidden_dim, num_transformer_layers, output_dim):
super(VectorEncoder, self).__init__()
self.transformer_layers = nn.ModuleList([TransformerEncoder(input_dim, hidden_dim) for _ in range(num_transformer_layers)])
self.linear = nn.Linear(input_dim, output_dim)
def forward(self, x):
for transformer in self.transformer_layers:
x = transformer(x)
x = self.linear(x)
return x
# 创建模型
input_dim = 54 * 1024
hidden_dim = 256
num_transformer_layers = 5
output_dim = 108
model = VectorEncoder(input_dim, hidden_dim, num_transformer_layers, output_dim)
# 将矢量输入模型进行转换
input_vector = torch.rand(1, input_dim)
output_vector = model(input_vector)
print(output_vector.shape)
```
这段代码创建了一个TransformerEncoder类,包含了self.attention和self.feed_forward两个子模块,还有LayerNorm用于将输入进行归一化。
然后通过VectorEncoder类,使用nn.ModuleList包含了多个TransformerEncoder层,利用for循环将输入向量通过多个TransformerEncoder层进行转换。
最终通过线性层将转换后的向量输出为指定长度的矢量。
### 回答3:
import torch
import torch.nn as nn
import torch.nn.functional as F
class TransformerEncoder(nn.Module):
def __init__(self, input_size, output_size):
super(TransformerEncoder, self).__init__()
self.linear = nn.Linear(input_size, output_size)
def forward(self, x):
x = self.linear(x)
return x
class Model(nn.Module):
def __init__(self, input_size, output_size, num_encoders):
super(Model, self).__init__()
self.encoders = nn.ModuleList([TransformerEncoder(input_size, output_size) for _ in range(num_encoders)])
def forward(self, x):
for encoder in self.encoders:
x = encoder(x)
return x
input_size = 54 * 1024
output_size = 108
num_encoders = 5
model = Model(input_size, output_size, num_encoders)
input_vector = torch.randn(1, input_size) # 生成一个随机输入矢量
output_vector = model(input_vector)
print(output_vector.size())
在上述代码中,我们首先定义了一个TransformerEncoder类,它是一个简单的线性层(Linear)包装器,将输入大小转换为输出大小。然后,我们定义了一个Model类,其中包含了多个TransformerEncoder实例,并使用ModuleList将它们放在了一个列表中。
在forward方法中,我们使用for循环遍历所有的TransformerEncoder,并且将上一个Encoder的输出作为下一个Encoder的输入,最终输出最后一个Encoder的输出。
最后,我们定义了输入和输出的大小,并实例化了Model类。通过将输入向量传递给模型的forward方法,我们可以得到最终的输出向量。打印输出向量的大小,应该为[1, 108]。
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