python语言实现multi-head-self-attention示例的代码：

### 回答1：
以下是一个 Python 代码示例，用于实现 multi-head self-attention：

import torch
import torch.nn as nn

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        self.depth = d_model // num_heads

        self.query_linear = nn.Linear(d_model, d_model)
        self.key_linear = nn.Linear(d_model, d_model)
        self.value_linear = nn.Linear(d_model, d_model)

        self.output_linear = nn.Linear(d_model, d_model)

    def forward(self, query, key, value, mask=None):
        batch_size = query.size()

        # Linear transformations
        query = self.query_linear(query)
        key = self.key_linear(key)
        value = self.value_linear(value)

        # Split into heads
        query = query.view(batch_size * self.num_heads, -1, self.depth)
        key = key.view(batch_size * self.num_heads, -1, self.depth)
        value = value.view(batch_size * self.num_heads, -1, self.depth)

        # Transpose for matrix multiplication
        query = query.transpose(1, 2)
        key = key.transpose(1, 2)
        value = value.transpose(1, 2)

        # Calculate scores
        scores = torch.matmul(query, key.transpose(-2, -1))
        scores = scores / torch.sqrt(torch.tensor(self.depth).float())

        # Apply mask (if provided)
        if mask is not None:
            mask = mask.unsqueeze(1)
            scores = scores.masked_fill(mask == , -1e9)

        # Softmax
        attention_weights = nn.Softmax(dim=-1)(scores)

        # Dropout
        attention_weights = nn.Dropout(p=.1)(attention_weights)

        # Multiply by values
        context = torch.matmul(attention_weights, value)

        # Reshape and concatenate
        context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.num_heads * self.depth)

        # Linear transformation
        output = self.output_linear(context)

        return output

希望对你有所帮助！

### 回答2：
下面是使用Python语言实现multi-head self-attention的一个示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class MultiHeadSelfAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super(MultiHeadSelfAttention, self).__init__()
        self.num_heads = num_heads
        self.d_head = d_model // num_heads

        self.fc_query = nn.Linear(d_model, d_model)
        self.fc_key = nn.Linear(d_model, d_model)
        self.fc_value = nn.Linear(d_model, d_model)
        self.fc_concat = nn.Linear(d_model, d_model)

    def forward(self, x):
        batch_size, seq_len, d_model = x.size()
        h = self.num_heads
        
        # Split input into multiple heads
        query = self.fc_query(x).view(batch_size, seq_len, h, self.d_head)
        key = self.fc_key(x).view(batch_size, seq_len, h, self.d_head)
        value = self.fc_value(x).view(batch_size, seq_len, h, self.d_head)
        
        # Compute attention scores
        scores = torch.matmul(query, key.transpose(-2, -1)) / (self.d_head ** 0.5)
        attn_weights = F.softmax(scores, dim=-1)
        
        # Apply attention weights to value vectors
        attended_values = torch.matmul(attn_weights, value)
        attended_values = attended_values.transpose(1, 2).contiguous().view(batch_size, seq_len, -1)
        
        # Concatenate and linearly transform attended values
        output = self.fc_concat(attended_values)
        return output

# 使用示例
d_model = 128
num_heads = 8
seq_len = 10
batch_size = 4

input_tensor = torch.randn(batch_size, seq_len, d_model)
attention = MultiHeadSelfAttention(d_model, num_heads)
output = attention(input_tensor)

print("Input Shape: ", input_tensor.shape)
print("Output Shape: ", output.shape)

上述代码定义了一个`MultiHeadSelfAttention`的类，其中`forward`函数实现了multi-head self-attention的计算过程。在使用示例中，我们输入一个大小为`(batch_size, seq_len, d_model)`的张量，经过multi-head self-attention计算后输出一个大小为`(batch_size, seq_len, d_model)`的张量。其中`d_model`表示输入的特征维度，`num_heads`表示attention头的数量。

### 回答3：
下面是使用Python实现multi-head self-attention示例的代码：

import torch
import torch.nn as nn

class MultiHeadSelfAttention(nn.Module):
    def __init__(self, embed_size, num_heads):
        super(MultiHeadSelfAttention, self).__init__()
        self.embed_size = embed_size
        self.num_heads = num_heads
        self.head_size = embed_size // num_heads
        
        self.query = nn.Linear(embed_size, embed_size)
        self.key = nn.Linear(embed_size, embed_size)
        self.value = nn.Linear(embed_size, embed_size)
        self.out = nn.Linear(embed_size, embed_size)
        
    def forward(self, x):
        batch_size, seq_len, embed_size = x.size()
        
        # Split the embedding into num_heads and reshape
        x = x.view(batch_size, seq_len, self.num_heads, self.head_size)
        x = x.permute(0, 2, 1, 3)
        
        # Apply linear transformations to obtain query, key, and value
        query = self.query(x)
        key = self.key(x)
        value = self.value(x)
        
        # Compute scaled dot product attention scores
        scores = torch.matmul(query, key.permute(0, 1, 3, 2))
        scores = scores / self.head_size**0.5
        
        # Apply softmax to obtain attention probabilities
        attn_probs = nn.Softmax(dim=-1)(scores)
        
        # Apply attention weights to value and sum across heads
        attended = torch.matmul(attn_probs, value)
        attended = attended.permute(0, 2, 1, 3)
        attended = attended.contiguous().view(batch_size, seq_len, self.embed_size)
        
        # Apply output linear transformation
        output = self.out(attended)
        
        return output

上述代码中定义了一个名为MultiHeadSelfAttention的类，继承自nn.Module，可以通过指定嵌入大小（embed_size）和头部数量（num_heads）来创建多头自注意力层。在前向传播方法forward中，先通过线性变换将输入张量分别变换为查询（query）、键（key）和值（value）张量。然后计算缩放点积注意力得分，将其作为注意力概率经过softmax函数进行归一化。通过注意力概率权重对值进行加权求和，并应用线性变换得到最终的输出张量。最后返回输出张量。