Today when people talk about Red Cross Organization, they shake their heads and don’t trust this organization. People react for it originated from about four years ago, at that time, a girl showed off her luxury in the public media, she told people that her father was a member of the Red Cross Organization. More and more people knew her, they thought they were cheated by the organization, the money they donated was not used in the right way. The incident has a great negative influence on the Red Cross Organization, people don’t trust it any more, they are not willing to donate their money. As for me, I will not donate money to the organization, I choose to give money to the person who is in need of help, so the money won’t be taken by others, make sure the person get the real money.统计这段文字中所有字符的相对频率。假设这些相对频率就是这些字符的概率，请计算其分布的熵

分析句子成分"I think it answers the question that most of us have posed in our own heads for many years.”

- 我 (主语) - think (谓语动词) - it (宾语) - answers (谓语动词) - the question (宾语) - that (引导定语从句) - most of us (定语从句的主语) - have posed (定语从句的...- for many years (介词短语作时间状语)

def flops(self, N, T, temporal=False): # calculate flops for 1 window with token length of N flops = 0 # qkv = self.qkv(x) # flops += N * self.dim * 3 * self.dim if temporal: flops += self.dim * N * T * T * 2 else: # attn = (q @ k.transpose(-2, -1)) flops += self.num_heads * N * (self.dim // self.num_heads) * N * T # x = (attn @ v) flops += self.num_heads * N * N * (self.dim // self.num_heads) * T # x = self.proj(x) # flops += N * self.dim * self.dim return flops

这段代码计算了一个窗口内的FLOPS（浮点操作数），其中N为令牌长度，T为时间步数。如果temporal为True，则计算的是temporal self-attention的FLOPS，否则计算的是普通的self-attention的FLOPS。具体来说，计算FLOPS...

if use_temporal: self.num_ttokens = num_ttokens self.temporal_position_bias_table = nn.Parameter(torch.zeros(2 * num_ttokens - 1, num_heads)) trunc_normal_(self.temporal_position_bias_table, std=.02) t_coords = torch.arange(num_ttokens) t_relative_coords = t_coords[:, None] - t_coords[None, :] t_relative_coords += num_ttokens - 1 t_relative_coords = t_relative_coords.view(-1) self.register_buffer("t_relative_coords", t_relative_coords)

接下来，t_coords表示时间序列的坐标，而t_relative_coords则表示时间序列中每个时间点与其他时间点之间的相对距离，t_relative_coords的形状为(num_ttokens, num_ttokens)，其中每个元素都是一个相对距离值。...

举例调用下面的方法： class MultiHeadAttention(tf.keras.layers.Layer): def init(self, d_model, num_heads): super(MultiHeadAttention, self).init() self.num_heads = num_heads self.d_model = d_model assert d_model % self.num_heads == 0 self.depth = d_model // self.num_heads self.query_dense = tf.keras.layers.Dense(units=d_model) self.key_dense = tf.keras.layers.Dense(units=d_model) self.value_dense = tf.keras.layers.Dense(units=d_model) self.dense = tf.keras.layers.Dense(units=d_model)

self.multi_head_attention = MultiHeadAttention(d_model=64, num_heads=8) def call(self, inputs): # 调用 MultiHeadAttention 层 x = self.multi_head_attention(inputs) return x # 初始化模型 model = ...

error: failed to push some refs to 'https://gitlab.topviewclub.cn/topview-studio/restructure-archive-system-backend.git' To https://gitlab.topviewclub.cn/topview-studio/restructure-archive-system-backend.git hint: Updates were rejected because a pushed branch tip is behind its remote ! refs/heads/master:refs/heads/origin/email [rejected] (non-fast-forward) hint: counterpart. Check out this branch and integrate the remote changes Done hint: (e.g. 'git pull ...') before pushing again. hint: See the 'Note about fast-forwards' in 'git push --help' for details.

这个错误提示说明在推送分支时出现了问题。错误消息中提到，你的本地分支的提交历史落后于远程分支，因此无法直接推送。为了解决这个问题，你可以尝试执行以下步骤： 1. 首先，确保你的本地仓库是最新的，可以使用...

import torch import torch.nn as nn import torch.nn.functional as F import numpy as np from utils import * from Network import * %matplotlib notebook import matplotlib.pyplot as plt #hyperparams enc_seq_len = 6 dec_seq_len = 2 output_sequence_length = 1 dim_val = 10 dim_attn = 5 lr = 0.002 epochs = 20 n_heads = 3 n_decoder_layers = 3 n_encoder_layers = 3 batch_size = 15 #init network and optimizer t = Transformer(dim_val, dim_attn, 1,dec_seq_len, output_sequence_length, n_decoder_layers, n_encoder_layers, n_heads) optimizer = torch.optim.Adam(t.parameters(), lr=lr) #keep track of loss for graph losses = []

接下来，定义了Transformer模型的一些参数，如头数（n_heads）、解码器和编码器层数（n_decoder_layers和n_encoder_layers）等。然后，定义了批次大小（batch_size）。接着，初始化了一个Transformer...

Fetching upstream changes from git@github. com:a792883583/treeHoleScore.git > /usr/bin/git --version # timeout=10 > git --version # 'git version 1.8.3.1' using GIT _ASKPASS to set credentials github > /usr/bin/git fetch --tags --progress git@github. com:a792883583/treeHoleScore.git +refs/heads/:refs/remotes/origin/ # timeout=10 ERROR: Error fetching remote repo 'origin' hudson. plugins.git.GitException: Failed to fetch from git@github. com:a792883583/treeHoleScore.git at hudson. plugins . git .GitSCM. fetchFrom(GitSCM. java:1003) at hudson. plugins .git .GitSCM. retrieveChanges(GitSCM. java:1245) at hudson.plugins.git.GitsCM. checkout(GitSCM. java:1309) at hudson.scm. SCM. checkout(SCM. java:540) at hudson. mode1. AbstractProject . checkout(AbstractProject . java:1240) at hudson. model AbstractBuild$AbstractBuildExecution. def aultCheckout (AbstractBuild. java:649) at jenkins .scm. SCMCheckoutStrategy . checkout(SCMCheckoutStrategy . java:85) at hudson . model. AbstractBuild$AbstractBuildExecution. run(AbstractBuild. java:521) at hudson.model . Run. execute(Run. java:1900) at hudson.model. FreeSty1eBuild.run(FreeStyleBuild.java:44) at hudson. model. ResourceController . execute(ResourceController . java:101) at hudson. model. Executor .run(Executor. java:442) Caused by: hudson. plugins.git .GitException: Command "/usr/bin/git fetch --tags --progress git@github . com: a792883583/treeHoleScore.git +refs/heads/* :refs/remotes/origin/*" returned status code 128: stdout: stderr: Host key verification failed. fatal: Could not read from remote repository. Please make sure you have the correct access rights and the repository exists. at org.jenkinsci .plugins.gitclient .CliGitAPIImp1.1aunchCommandIn(CliGitAPImp1.java:2734) at org.jenkinsci .plugins.gitclient .CliGitAPIImp1.1aunchCommandWithCredentials(CliGitAPIImpl.java:2111) at org.jenkinsci.plugins . gitclient .CliGitAPIImp1$1. execute(CliGitAPIImp1.java:623) at hudson.p1ugins . git .GitSCM. fetchFrom (GitSCM. java:1001) 11 more ERROR: Error fetching remote repo ' origin' Finished: FAILURE

"Fetching upstream changes from git@github.com:a792883583/treeHoleScore.git" 意思是从 "git@github.com:a792883583/treeHoleScore.git" 这个地址获取最新的更改。 "/usr/bin/git --version" 意思是在本地执行 ...

def add(self, server_link): # 新增节点及依赖关系 a, b = server_link.split("-") if a not in self.servers: self.servers[a] = Node(a) else: # a依赖b，但a之前是头节点，需要从头节点列表中移除 if self.servers[a] in self.heads: self.heads.remove(self.servers[a])

这是 Server 类中的一个方法 add，用于向服务器链表中添加新的节点及其依赖关系。具体来说，这个方法会将一个形如 "a-b" 的字符串解析为两个节点，其中 a 依赖 b，然后将这两个节点添加到服务器列表中，并建立依赖...

hint: Updates were rejected because the remote contains work that you do ! refs/heads/master:refs/heads/master [rejected] (fetch first) hint: not have locally. This is usually caused by another repository pushing Done hint: to the same ref. You may want to first integrate the remote changes hint: (e.g., 'git pull ...') before pushing again. hint: See the 'Note about fast-forwards' in 'git push --help' for details.

这是一个 Git 提示信息，通常出现在你试图将本地代码推送到远程仓库时，远程仓库已经有了更新。 Git 建议你先将远程仓库的更新拉取到本地仓库，解决冲突之后再进行推送。你可以使用命令 git pull 将远程仓库的更新...

import math import torch from torch import nn from d2l import torch as d2l def transpose_qkv(X,num_heads): X = X.reshape(X.shape[0], X.shape[1], num_heads, -1) X = X.permute(0, 2, 1, 3) return X.reshape(-1, X.shape[2], X.shape[3]) def transpose_output(X,num_heads): X = X.reshape(-1, num_heads, X.shape[1], X.shape[2]) X = X.permute(0, 2, 1, 3) return X.reshape(X.shape[0], X.shape[1], -1) class MultiHeadAttention(nn.Module): def init(self,key_size,query_size,value_size,num_hiddens, num_heads,dropout,bias=False,kwargs): super(MultiHeadAttention,self).init(kwargs) self.num_heads = num_heads self.attention = d2l.DotProductAttention(dropout) self.W_q = nn.Linear(query_size,num_hiddens,bias=bias) self.W_k = nn.Linear(key_size,num_hiddens,bias=bias) self.W_v = nn.Linear(value_size,num_hiddens,bias=bias) self.W_o = nn.Linear(num_hiddens,num_hiddens,bias=bias) def forward(self,queries,keys,values,valid_lens): queries = transpose_qkv(self.W_q(queries), self.num_heads) keys = transpose_qkv(self.W_k(keys), self.num_heads) values = transpose_qkv(self.W_v(values), self.num_heads) if valid_lens is not None: valid_lens = torch.repeat_interleave(valid_lens, repeats=self.num_heads, dim=0) output = self.attention(queries,keys,values,valid_lens) output_concat = transpose_output(output,self.num_heads) return self.W_o(output_concat)

这段代码实现了多头注意力机制（Multi-Head Attention）的模块。多头注意力机制是用于处理序列数据的深度学习模型中常的组件，它可以并行地对输入序列进行不同位置的关注。在这段代码中，MultiHeadAttention 类...

if temporal: relative_pos_bias = self.temporal_position_bias_table[self.t_relative_coords].view(self.num_ttokens, self.num_ttokens, -1).permute(2, 0, 1).contiguous() attn = attn + relative_pos_bias.unsqueeze(0) attn = self.softmax(attn) else: relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # WhWw,WhWw,nH relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, WhWw, WhWw attn = attn + relative_position_bias.unsqueeze(0) if mask is not None: nW = mask.shape[0] attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) attn = attn.view(-1, self.num_heads, N, N) attn = self.softmax(attn) else: attn = self.softmax(attn)

这段代码主要是实现了注意力机制中的相对位置编码，用于捕捉序列中不同位置之间的依赖关系。如果temporal为True，则使用时间维度的相对位置编码，否则使用空间维度的相对位置编码。在加入相对位置编码后，还进行了...

def init(self, dim, num_heads, kernel_size=3, padding=1, stride=1, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): super().init() head_dim = dim // num_heads self.num_heads = num_heads self.kernel_size = kernel_size self.padding = padding self.stride = stride self.scale = qk_scale or head_dim-0.5 self.v = nn.Linear(dim, dim, bias=qkv_bias) self.attn = nn.Linear(dim, kernel_size4 * num_heads) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.unfold = nn.Unfold(kernel_size=kernel_size, padding=padding, stride=stride) self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride, ceil_mode=True) def forward(self, x): B, H, W, C = x.shape v = self.v(x).permute(0, 3, 1, 2) h, w = math.ceil(H / self.stride), math.ceil(W / self.stride) v = self.unfold(v).reshape(B, self.num_heads, C // self.num_heads, self.kernel_size * self.kernel_size, h * w).permute(0, 1, 4, 3, 2) # B,H,N,kxk,C/H attn = self.pool(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1) attn = self.attn(attn).reshape( B, h * w, self.num_heads, self.kernel_size * self.kernel_size, self.kernel_size * self.kernel_size).permute(0, 2, 1, 3, 4) # B,H,N,kxk,kxk attn = attn * self.scale attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).permute(0, 1, 4, 3, 2).reshape( B, C * self.kernel_size * self.kernel_size, h * w) x = F.fold(x, output_size=(H, W), kernel_size=self.kernel_size, padding=self.padding, stride=self.stride) x = self.proj(x.permute(0, 2, 3, 1)) x = self.proj_drop(x) return x

其中，dim表示输入特征的维度，num_heads表示头的数量，kernel_size表示卷积核的大小，padding表示填充的大小，stride表示步长，qkv_bias表示是否使用偏置，qk_scale表示缩放因子，attn_drop表示注意力机制的dropout...

class GAT(nn.Module): def init(self, in_dim, out_dim, num_heads): super(GAT, self).init() self.num_heads = num_heads self.attentions = nn.ModuleList([nn.Linear(in_dim, out_dim) for _ in range(num_heads)]) # self.linear = nn.Linear(512, 128) self.out_att = nn.Linear(in_dim*num_heads, out_dim) def forward(self, x, adj): x = x.unsqueeze(1) x = x.transpose(2,0) x = torch.cat([att(x) for att in self.attentions], dim=1) alpha = F.softmax(torch.matmul(x, x.transpose(1, 2)) / self.num_heads, dim=-1) alpha = torch.where(alpha>0, alpha, torch.zeros_like(alpha)) # alpha = torch.where(adj.unsqueeze(-1).bool(), alpha, torch.zeros_like(alpha)) alpha = alpha / alpha.sum(dim=-2, keepdim=True) out = torch.matmul(alpha, x).squeeze(1) out=out.reshape(-1,128) # out = self.linear(out) out = F.elu(self.out_att(out)) return out这段代码为什么一直报错mat1 and mat2 shapes cannot be multiplied (512x768 and 512x128)，我应该怎么修改呢？它是怎么在x = torch.cat([att(x) for att in self.attentions], dim=1)这一步将输入（24，1，16）变成（512，768）的形状的，在这一步总是报错和（512，128）的全连接层不匹配，怎么修改呢。这是pytorch代码，本意是构建两层GAT用点注意力机制对形状为（16992，307，12，2）的数据集按第二列度特征相同的节点计算注意力系数和隐变量，怎么完善呢

然后你对这个矩阵进行矩阵乘法，但是你需要将它变形成(batch_size, num_heads, num_nodes, out_dim)，即(24, 8, 512, 96)，其中8为你定义的num_heads，96为每个头的特征维度(out_dim=768/num_heads)。你可以使用以下...

Coin is heads with probability 1/3 and tails with probability 2/3. The expectation of independent flips until first heads is _____.

The expected number of flips until the first heads is given by the formula: E(X) = 1/p where p is the probability of getting heads on any given flip. In this case, the probability of getting heads ...

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