head -> num =0如何用python语言实现

假设你定义了一个叫做`head`的链表节点结构体，其中有一个叫做`num`的属性，表示节点存储的数字。在Python中，可以使用类来模拟结构体。

以下是将`head`节点的`num`属性设置为0的Python代码示例：

class ListNode:
    def __init__(self, num):
        self.num = num
        self.next = None

head = ListNode(0)

在这个示例中，我们先定义了一个`ListNode`类，其中`__init__`方法用于初始化节点的`num`属性和`next`属性。然后，我们创建了一个`head`节点，将其`num`属性设置为0。

需要注意的是，在Python中，链表节点的`next`属性通常用于指向下一个节点，因此我们在这里将其初始化为None。如果需要向链表中添加更多的节点，可以通过创建新节点并将其附加到链表中来实现。

相关问题

本关任务：给定程序中已建立一个带有头结点的单向链表，在 main 函数中将多次调用 DeleteTail 函数，每调用一次 DeleteTail函数，释放该该链表的尾结点，使链表缩短。 相关知识 为了完成本关任务，你需要掌握：1.如何定义和调用函数；2.链表的相关知识。 编程要求 请勿改动主函数 main 和其他函数中的任何内容，根据提示，在右侧编辑器的Begin-End区域内补充代码。然后调试运行使程序得到正确的结果。 测试说明 平台会对你编写的代码进行测试，比对你输出的数值与实际正确数值，只有所有数据全部计算正确才能通过测试。 输入：第一行是一个整数n(1<n<80)，第二行是n个整数，第三行是一个整数m(m<n)。 输出：一个带有HEAD的长度为n-m的链表。 测试输入： 5 1 5 9 6 8 2 预期输出： Head->1->5->9->End 测试输入： 10 1 2 3 4 5 6 7 8 9 10 5 预期输出： Head->1->2->3->4->5->End

class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next

def DeleteTail(head):
    if head.next is None:
        return head
    cur = head
    while cur.next.next is not None:
        cur = cur.next
    cur.next = None
    return head

def printList(head):
    cur = head.next
    res = []
    while cur is not None:
        res.append(str(cur.val))
        cur = cur.next
    print('Head->' + '->'.join(res) + '->End')

if __name__ == '__main__':
    n = int(input())
    nums = list(map(int, input().split()))
    m = int(input())

    head = ListNode()
    cur = head
    for num in nums:
        cur.next = ListNode(num)
        cur = cur.next

    for _ in range(m):
        head = DeleteTail(head)

    printList(head)

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。