nn.Linear(embed_dim, embed_dim // 2, bias=False)
时间: 2024-09-25 22:10:40 浏览: 7
`nn.Linear` 是PyTorch库中的一个类,用于创建线性层(全连接层)。它接受两个参数:输入维度(`embed_dim`)和输出维度。在这个特定的例子中,`nn.Linear(embed_dim, embed_dim // 2, bias=False)` 表示从一个嵌入维度大小的向量降维到其一半的大小,且不使用偏置项(`bias=False`)。
让我们通过代码来演示它的作用:
```python
import torch.nn as nn
import torch
# 假设我们有一个嵌入维度为10的输入
input_size = 10
hidden_size = input_size // 2
# 创建一个全连接层,将输入10维降为5维
linear_layer = nn.Linear(input_size, hidden_size, bias=False)
input_tensor = torch.randn((1, input_size)) # 创建一个随机的1x10形状的张量作为输入
# 运行前向传播并查看输出
output = linear_layer(input_tensor)
print("Output shape after Linear layer:", output.shape) # 输出应该是 (1, 5)
# 注意这里由于bias=False,所以输出不会因为添加额外的偏置项而改变大小
```
相关问题
class TransformerLayer(nn.Module): # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) def __init__(self, c, num_heads): super().__init__() self.q = nn.Linear(c, c, bias=False) self.k = nn.Linear(c, c, bias=False) self.v = nn.Linear(c, c, bias=False) self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) self.fc1 = nn.Linear(c, c, bias=False) self.fc2 = nn.Linear(c, c, bias=False) def forward(self, x): x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x x = self.fc2(self.fc1(x)) + x return x
这是一个Transformer中的一个层,它由三个线性层(self.q、self.k、self.v)和一个Multihead Attention层(self.ma)组成。Multihead Attention层将输入张量x通过self.q、self.k、self.v线性变换映射到key、query和value张量,然后计算注意力权重,最后输出一个经过加权平均的张量。在这个层中,Multihead Attention层的输出通过加上输入张量x来实现残差连接,并经过两个全连接层(self.fc1和self.fc2)进行变换。这个层可以在Transformer模型中重复多次来进行特征提取和转换。
resnet18+多头注意力机制代码
以下是使用ResNet18和多头注意力机制的代码示例:
```
import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
def __init__(self, embed_dim, num_heads):
super(MultiHeadAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.q_linear = nn.Linear(embed_dim, embed_dim)
self.k_linear = nn.Linear(embed_dim, embed_dim)
self.v_linear = nn.Linear(embed_dim, embed_dim)
self.fc = nn.Linear(embed_dim, embed_dim)
def forward(self, x):
batch_size = x.size(0)
# Project inputs to query, key, and value tensors
q = self.q_linear(x).view(batch_size, self.num_heads, self.head_dim)
k = self.k_linear(x).view(batch_size, self.num_heads, self.head_dim)
v = self.v_linear(x).view(batch_size, self.num_heads, self.head_dim)
# Compute self-attention scores
scores = torch.matmul(q, k.transpose(-2, -1)) / self.embed_dim**0.5
# Apply softmax activation function
scores = nn.functional.softmax(scores, dim=-1)
# Compute weighted sum of values using attention scores
weighted_values = torch.matmul(scores, v)
# Concatenate multi-head attention outputs
concat_heads = weighted_values.transpose(1, 2).contiguous().view(batch_size, -1, self.embed_dim)
# Apply fully connected layer to concatenated outputs
output = self.fc(concat_heads)
return output
class ResNet18(nn.Module):
def __init__(self, num_classes, embed_dim, num_heads):
super(ResNet18, self).__init__()
self.num_classes = num_classes
self.embed_dim = embed_dim
self.num_heads = num_heads
# Define ResNet18 layers
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
)
self.layer2 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
)
self.layer3 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
)
self.layer4 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * self.embed_dim, self.num_classes)
# Define multi-head attention layer
self.multi_head_attention = MultiHeadAttention(self.embed_dim, self.num_heads)
def forward(self, x):
# ResNet18 layers
x = self.conv1(x)
x = self.bn1(x)
x = nn.functional.relu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
# Multi-head attention layer
x = self.multi_head_attention(x)
# Fully connected layer
x = self.fc(x)
return x
```
在此代码中,我们首先定义了一个名为`MultiHeadAttention`的多头注意力机制模块。该模块接受一个张量作为输入,并计算该张量的自注意力表示。我们还定义了一个名为`ResNet18`的ResNet18模型,该模型使用多头注意力机制以更好地捕获输入数据中的关键信息。该模型在ResNet18的基础上添加了一个多头注意力层,该层将ResNet18的输出作为输入,并在完全连接层之前对其进行处理。