9863 AG_INT0怎么配置

9863 AG_INT0是指Alcor Micro AU9863 USB转串口芯片的中断0引脚。配置该引脚需要编写相关的驱动程序。下面是一些基本的步骤：

1. 确认芯片的引脚定义和中断编号。

2. 在操作系统中注册中断处理程序，并将其关联到该中断编号。

3. 在驱动程序中配置芯片的中断控制寄存器，使其在中断事件发生时触发中断。

4. 在中断处理程序中读取芯片的状态寄存器，以确定中断事件的类型。

5. 根据中断类型执行相应的操作，如读取数据、发送数据或者进行其他处理。

需要注意的是，具体的配置方法可能因操作系统和芯片型号而异。建议查阅相关的文档和资料，以获取更详细的信息。

相关问题

import torch import torch.nn as nn from torchtext.datasets import AG_NEWS from torchtext.data.utils import get_tokenizer from torchtext.vocab import build_vocab_from_iterator # 数据预处理 tokenizer = get_tokenizer('basic_english') train_iter = AG_NEWS(split='train') counter = Counter() for (label, line) in train_iter: counter.update(tokenizer(line)) vocab = build_vocab_from_iterator([counter], specials=["<unk>"]) word2idx = dict(vocab.stoi) # 设定超参数 embedding_dim = 64 hidden_dim = 128 num_epochs = 10 batch_size = 64 # 定义模型 class RNN(nn.Module): def __init__(self, vocab_size, embedding_dim, hidden_dim): super(RNN, self).__init__() self.embedding = nn.Embedding(vocab_size, embedding_dim) self.rnn = nn.RNN(embedding_dim, hidden_dim, batch_first=True) self.fc = nn.Linear(hidden_dim, 4) def forward(self, x): x = self.embedding(x) out, _ = self.rnn(x) out = self.fc(out[:, -1, :]) return out # 初始化模型、优化器和损失函数 model = RNN(len(vocab), embedding_dim, hidden_dim) optimizer = torch.optim.Adam(model.parameters()) criterion = nn.CrossEntropyLoss() # 定义数据加载器 train_iter = AG_NEWS(split='train') train_data = [] for (label, line) in train_iter: label = torch.tensor([int(label)-1]) line = torch.tensor([word2idx[word] for word in tokenizer(line)]) train_data.append((line, label)) train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True) # 开始训练 for epoch in range(num_epochs): total_loss = 0.0 for input, target in train_loader: model.zero_grad() output = model(input) loss = criterion(output, target.squeeze()) loss.backward() optimizer.step() total_loss += loss.item() * input.size(0) print("Epoch: {}, Loss: {:.4f}".format(epoch+1, total_loss/len(train_data)))改错

在代码中出现了一个错误：缺少 `Counter` 的导入语句。需要在开头添加 `from collections import Counter`。

另外，在模型训练时，需要将模型设置为训练模式（`model.train()`），以启用 dropout 和 batch normalization 等功能。同时，还需要将输入和目标数据转移到 GPU 上进行计算，以加速训练过程。具体修改如下：

import torch
import torch.nn as nn
from torchtext.datasets import AG_NEWS
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from collections import Counter

# 数据预处理
tokenizer = get_tokenizer('basic_english')
train_iter = AG_NEWS(split='train')
counter = Counter()
for (label, line) in train_iter:
    counter.update(tokenizer(line))
vocab = build_vocab_from_iterator([counter], specials=["<unk>"])
word2idx = dict(vocab.stoi)

# 设定超参数
embedding_dim = 64
hidden_dim = 128
num_epochs = 10
batch_size = 64

# 定义模型
class RNN(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(RNN, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.rnn = nn.RNN(embedding_dim, hidden_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, 4)

    def forward(self, x):
        x = self.embedding(x)
        out, _ = self.rnn(x)
        out = self.fc(out[:, -1, :])
        return out

# 初始化模型、优化器和损失函数
model = RNN(len(vocab), embedding_dim, hidden_dim)
optimizer = torch.optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss()

# 将模型设置为训练模式
model.train()

# 将数据转移到 GPU 上
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

train_iter = AG_NEWS(split='train')
train_data = []
for (label, line) in train_iter:
    label = torch.tensor([int(label)-1]).to(device)
    line = torch.tensor([word2idx[word] for word in tokenizer(line)]).to(device)
    train_data.append((line, label))
train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True)

# 开始训练
for epoch in range(num_epochs):
    total_loss = 0.0
    for input, target in train_loader:
        optimizer.zero_grad()
        output = model(input)
        loss = criterion(output, target.squeeze())
        loss.backward()
        optimizer.step()
        total_loss += loss.item() * input.size(0)
    print("Epoch: {}, Loss: {:.4f}".format(epoch+1, total_loss/len(train_data)))

试写一算法，判断以邻接表方式存储的有向图中是否存在由顶点Vi到顶点Vj的路径（i-->j）。 【输入形式】 顶点个数：n 边的条数：m 边的有向顶点对： （a,b）…… 待判断定点i，j 【输出形式】 True 或 False 【样例输入】 5 4 1 2 1 3 2 4 3 5 1 5 【样例输出】 True 【样例说明】 【评分标准】 【代码框架】 #include<stdio.h> #include<malloc.h> #define OK 1 #define ERROR 0 #define MAX_VEX_NUM 100 //最大顶点数量 typedef int Status; typedef enum{AG,AN,DG,DN} GKind; //图类型定义 typedef struct ArcNode{ int adjvex; //邻接点数组下标（从0开始） struct ArcNode *nextarc; int weight; }; typedef struct { int vertex; //顶点编号，从1开始 ArcNode *firstarc; }VNode,AdjList[MAX_VEX_NUM]; typedef struct{ AdjList vertices; int vexnum; int arcnum; GKind kind; }ALGraph; Status InitALGraph(ALGraph &G) { } //创建图的邻接表存储结构 //n: 顶点数 //vertices[]:顶点数组 //edges[][]:边数组 //edgesSize：边数目 Status CreateALGraph(ALGraph &G, int n, int vertices[ ], int edges[20][2], int edgesSize) { } //连通图的深度优先搜索 //v0: 起点的数组下标（从0开始） //visited[ ]:访问标志数组 void DFS(ALGraph G, int v0, int visited[]) { } //图的深度优先搜索 int DFSTraverse(ALGraph G) { } // 判断图的两个顶点是否连通，如果连通，返回true, 否则返回false //v: 起点的编号（从1开始） //w:终点的编号（从1开始） bool isConnect(ALGraph G, int v, int w) { }

#include<stdio.h>
#include<malloc.h>

#define OK 1
#define ERROR 0
#define MAX_VEX_NUM 100 //最大顶点数量

typedef int Status;
typedef enum{AG,AN,DG,DN} GKind; //图类型定义

typedef struct ArcNode{
    int adjvex;      //邻接点数组下标（从0开始）
    struct ArcNode *nextarc;
    int weight;
}ArcNode;

typedef struct VNode{
    int vertex;  //顶点编号，从1开始
    ArcNode *firstarc;
}VNode,AdjList[MAX_VEX_NUM];

typedef struct{
    AdjList vertices;
    int vexnum;
    int arcnum;
    GKind kind;
}ALGraph;

Status InitALGraph(ALGraph &G){
    G.vexnum = 0;
    G.arcnum = 0;
    G.kind = DG;    //默认为有向图
    for(int i = 0; i < MAX_VEX_NUM; i++){
        G.vertices[i].vertex = 0;
        G.vertices[i].firstarc = NULL;
    }
    return OK;
}

Status CreateALGraph(ALGraph &G, int n, int vertices[],  int edges[20][2], int edgesSize){
    G.vexnum = n;
    G.arcnum = edgesSize;
    for(int i = 0; i < n; i++){
        G.vertices[i].vertex = vertices[i];
    }
    for(int i = 0; i < edgesSize; i++){
        int v1 = edges[i][0]-1;
        int v2 = edges[i][1]-1;
        ArcNode *arc = (ArcNode*)malloc(sizeof(ArcNode));
        arc->adjvex = v2;
        arc->nextarc = G.vertices[v1].firstarc;
        G.vertices[v1].firstarc = arc;
    }
    return OK;
}

void DFS(ALGraph G, int v0, int visited[]){
    visited[v0] = 1;
    ArcNode *p = G.vertices[v0].firstarc;
    while(p != NULL){
        int w = p->adjvex;
        if(visited[w] == 0){
            DFS(G, w, visited);
        }
        p = p->nextarc;
    }
}

int DFSTraverse(ALGraph G){
    int visited[MAX_VEX_NUM] = {0};
    int count = 0;
    for(int i = 0; i < G.vexnum; i++){
        if(visited[i] == 0){
            count++;
            DFS(G, i, visited);
        }
    }
    return count;
}

bool isConnect(ALGraph G, int v, int w){
    int visited[MAX_VEX_NUM] = {0};
    int v0 = v-1, w0 = w-1;
    DFS(G, v0, visited);
    if(visited[w0] == 1){
        return true;
    }
    return false;
}

int main(){
    ALGraph G;
    int n, m, v, w;
    int vertices[MAX_VEX_NUM];
    int edges[20][2];
    scanf("%d%d", &n, &m);
    for(int i = 0; i < n; i++){
        scanf("%d", &vertices[i]);
    }
    for(int i = 0; i < m; i++){
        scanf("%d%d", &edges[i][0], &edges[i][1]);
    }
    InitALGraph(G);
    CreateALGraph(G, n, vertices, edges, m);
    scanf("%d%d", &v, &w);
    if(isConnect(G, v, w)){
        printf("True\n");
    }
    else{
        printf("False\n");
    }
    return 0;
}