Descubre la Divizia A de Baloncesto de Rumanía: El Escenario Perfecto para los Aficionados del Baloncesto
La Divizia A de baloncesto en Rumanía es una liga vibrante que atrae a fanáticos de todo el mundo con su intensidad y talento excepcional. Cada partido es una oportunidad para ver jugar a los mejores jugadores rumano<|repo_name|>henryjyoon/OS<|file_sep|>/lab5/Makefile
all: main.o
g++ main.o -o lab5
main.o: main.cpp
g++ -c main.cpp
clean:
rm *.o lab5
<|repo_name|>henryjyoon/OS<|file_sep|>/lab2/lab2.cpp
#include "lab2.h"
#include
#include
#include
#include
#include
#include
#include
int main(int argc, char *argv[]) {
int i;
int status;
pid_t pid;
if (argc != 3) {
fprintf(stderr, "Usage: %s command filen", argv[0]);
exit(1);
}
pid = fork();
if (pid == 0) {
// child process
// open the file
int fd = open(argv[2], O_RDONLY);
if (fd == -1) {
fprintf(stderr, "Cannot open the filen");
exit(1);
}
// replace stdin with the file
dup2(fd, STDIN_FILENO);
// close the file descriptor
close(fd);
// execute the command
execlp(argv[1], argv[1], NULL);
// in case execlp fails
fprintf(stderr, "Command not foundn");
exit(1);
}
else if (pid > 0) {
// parent process
waitpid(pid, &status, 0);
}
else {
// fork failed
fprintf(stderr, "Fork failedn");
exit(1);
}
return 0;
}
<|repo_name|>henryjyoon/OS<|file_sep|>/lab4/Makefile
all: lab4
lab4: lab4.o
g++ lab4.o -o lab4
lab4.o: lab4.cpp
g++ -c lab4.cpp
clean:
rm *.o lab4
<|repo_name|>henryjyoon/OS<|file_sep|>/lab7/lab7.c
#include
#include
#include
#include
#include
int main(int argc, char *argv[]) {
int i;
int status;
pid_t pid;
if (argc != 3) {
fprintf(stderr, "Usage: %s num-processes num-iterationsn", argv[0]);
exit(1);
}
int num_processes = atoi(argv[1]);
int num_iterations = atoi(argv[2]);
pid_t pids[num_processes];
for (i = 0; i < num_processes; i++) {
pids[i] = fork();
if (pids[i] == 0) {
srand(time(NULL));
for (int j = 0; j < num_iterations; j++) {
printf("Child %d iteration %dn", getpid(), j+1);
sleep(rand() % 3 + 1);
}
exit(0);
}
else if (pids[i] == -1) {
fprintf(stderr, "Fork failedn");
exit(1);
}
}
for (i = 0; i < num_processes; i++) {
waitpid(pids[i], &status, 0);
}
return 0;
}
<|file_sep|>#include "lab6.h"
#include
#include
#include
#include
#include
#include
int main(int argc, char *argv[]) {
int i;
int status;
pid_t pid;
if (argc != 3) {
fprintf(stderr, "Usage: %s command filen", argv[0]);
exit(1);
}
pid = fork();
if (pid == 0) {
// child process
// open the file for reading and writing and truncate it to zero length.
int fd = open(argv[2], O_RDWR | O_TRUNC | O_CREAT, S_IRUSR | S_IWUSR | S_IRGRP | S_IROTH);
// duplicate the read end of the pipe to the standard input.
dup2(fd, STDIN_FILENO);
// duplicate the write end of the pipe to the standard output.
dup2(fd, STDOUT_FILENO);
// close the file descriptor.
close(fd);
// execute the command.
execlp(argv[1], argv[1], NULL);
// in case execlp fails.
fprintf(stderr, "Command not found.n");
exit(1);
}
else if (pid > 0) {
// parent process.
// wait for the child process to terminate.
waitpid(pid, &status, 0);
}
else {
fprintf(stderr, "Fork failed.n");
exit(1);
}
return 0;
}
<|file_sep|>#include "lab5.h"
#include
#include
void printLine(int numSpaces) {
for (int i = 0; i < numSpaces; i++) printf(" ");
printf("| ");
}
void printNode(Node *node, int level) {
int i;
if (node == NULL)
return;
printNode(node->leftChild, level + 1);
for (i = level; i > 0; i--) printLine(i * TAB_SIZE);
printLine(TAB_SIZE); printf("+- ");
printNodeName(node->name); printf(" ");
printLine(TAB_SIZE); printf("+- ");
printNodeAddress(node->address); printf(" ");
printLine(TAB_SIZE); printf("| ");
if (node->rightChild != NULL)
printf("(R)");
else
printf(" ");
printf("n");
printNode(node->rightChild, level + 1);
}
void printTree(Node *root) {
if (root == NULL)
return;
printNode(root, 0);
}
Node* newNode(char* name, unsigned long address) {
Node* node = malloc(sizeof(Node));
node->name = name;
node->address = address;
node->leftChild = NULL;
node->rightChild = NULL;
return node;
}
void freeTree(Node* root) {
if (root == NULL)
return;
freeTree(root->leftChild);
freeTree(root->rightChild);
free(root);
}
int main(int argc, char *argv[]) {
Node* root = newNode("main", GET_ADDRESS(main));
root->leftChild = newNode("printLine", GET_ADDRESS(printLine));
root->rightChild = newNode("printNode", GET_ADDRESS(printNode));
root->leftChild->leftChild = newNode("printTree", GET_ADDRESS(printTree));
root->leftChild->rightChild = newNode("newNode", GET_ADDRESS(newNode));
root->rightChild->leftChild = newNode("freeTree", GET_ADDRESS(freeTree));
printTree(root);
freeTree(root);
return 0;
}
<|repo_name|>henryjyoon/OS<|file_sep|>/lab7/Makefile
all: lab7
lab7: lab7.c
gcc lab7.c -o lab7
clean:
rm *.o lab7
<|file_sep|>#include "lab5.h"
#define TAB_SIZE 8
void printNodeName(char* name) { printf("%s", name); }
void printNodeAddress(unsigned long address) { printf("%lx", address); }
unsigned long GET_ADDRESS(void *addr)
{
unsigned long ret;
char **ppc;
ppc=(char**)&ret;
*ppc=(char*)addr;
return ret;
}
<|repo_name|>henryjyoon/OS<|file_sep|>/lab6/lab6.c
#include "lab6.h"
#include
#include
#include
#include
#include
int main(int argc, char *argv[]) {
if (argc != 3) {
fprintf(stderr, "Usage: %s command filen", argv[0]);
exit(1);
}
int fd[2];
int status;
pid_t pid;
if (pipe(fd)) { /* create a pipe */
fprintf(stderr,"Pipe Failed" );
return -1 ;
}
pid=fork();
if(pid==-1){
/* fork error */
perror("fork error");
exit(-1);
}else if(pid==0){ /* child process */
/* close read end of pipe */
close(fd[0]);
/* redirect standard output to write end of pipe */
dup2(fd[1],STDOUT_FILENO);
/* execute command */
execlp(argv[1],argv[1],NULL);
/* in case execlp fails */
perror("execl error");
exit(-1);
}else{ /* parent process */
/* close write end of pipe */
close(fd[1]);
/* open a file for writing */
int out_fd=open(argv[2],O_WRONLY | O_CREAT | O_TRUNC,S_IRUSR | S_IWUSR | S_IRGRP | S_IROTH );
if(out_fd==-1){
perror("open error");
exit(-1);
}
/* redirect standard input to read end of pipe */
dup2(fd[0],STDIN_FILENO);
/* redirect standard output to output file */
dup2(out_fd ,STDOUT_FILENO );
/* execute cat command */
execlp("/bin/cat","cat",(char *)NULL);
/* in case execlp fails */
perror("execl error");
exit(-1);
}
return 0 ;
}
<|file_sep|># -*- coding:utf-8 -*-
# @Time :2020/10/14 下午10:24
# @Author :WangXiaoMing
# @File :utils.py
# @Software: PyCharm
import torch.nn as nn
def init_weights(model):
'''
初始化权重，均为正态分布，方差为模型的输入维度的倒数的平方根。
Parameters:
model(nn.Module): 模型
Returns:
None
'''
for module in model.modules():
# print(module.__class__.__name__)
# if isinstance(module,(nn.Linear)):
# nn.init.normal_(module.weight,std=module.in_features**-0.5)
# 因为在上面的代码中，我们使用了relu激活函数，所以这里需要将偏置参数设置为负数。
# nn.init.constant_(module.bias,-np.sqrt((module.in_features)**-1))
# 如果不使用relu，则不用设置偏置参数。
# elif isinstance(module,(nn.Conv2d)):
# nn.init.normal_(module.weight,std=module.in_channels**-0.5)
class SavePath(object):
class BaseTrainer(object):
class BaseDataset(torch.utils.data.Dataset):
class BaseDataLoader(torch.utils.data.DataLoader):
def get_optimizer(args,model):
def get_scheduler(args,model):
def get_data_loader(args):
def get_model(args):
def train(args):
def test(args):
def evaluate(args):
if __name__=="__main__":
***** Tag Data *****
ID: 3
description: The function `init_weights` initializes weights for neural network modules,
using advanced initialization techniques like normal distribution with variance based
on input features.
start line: 11
end line: 39
dependencies:
- type: Function
name: init_weights
start line: 11
end line: 39
context description: This snippet is part of a utility function used to initialize
neural network weights. It's an advanced technique often used in deep learning models.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 3
advanced coding concepts: 4
interesting for students: 5
self contained: Y
*************
## Suggestions for complexity
1. **Adaptive Initialization Based on Layer Type**: Modify the code to adaptively initialize weights differently based on more specific layer types beyond `nn.Linear` and `nn.Conv2d`, such as `nn.BatchNorm`, `nn.LSTM`, etc., with custom initialization strategies for each.
2. **Conditional Initialization Based on Model Depth**: Add logic to adjust weight initialization parameters based on the depth or position of layers within the model hierarchy.
3. **Dynamic Standard Deviation Calculation**: Implement a more sophisticated method for calculating standard deviation that considers additional parameters or metrics from previous layers or training data statistics.
4. **Integration with Mixed Precision Training**: Enhance the code to support mixed precision training by ensuring that weight initialization is compatible with both float16 and float32 precision levels.
5. **Custom Initialization for Custom Layers**: Allow users to define their own custom layers and specify unique initialization strategies for these user-defined layers within the same function.
## Conversation
<|user|>I'm working with some advanced weight initialization techniques for neural networks. Here's a piece of code I have so far:
python
def init_weights(model):
'''
初始化权重，均为正态分布，方差为模型的输入维度的倒数的平方根。
Parameters:
model(nn.Module): 模型
Returns:
None
'''
for module in model.modules():
# print(module.__class__.__name__)
# if isinstance(module,(nn.Linear)):
# nn.init.normal_(module.weight,std=module.in_features**-0.5)
# 因为在上面的代码中，我们使用了relu激活函数，所以这里需要将偏置参数设置为负数。
# nn.init.constant_(module.bias,-np.sqrt((module.in_features)**-1))
# 如果不使用relu，则不用设置偏置参数。
# elif isinstance(module,(nn.Conv2d)):
# nn.init.normal_(module.weight,std=module.in_channels**-0.5)
Could you help me enhance this code to adaptively initialize weights differently based on more specific layer types beyond `nn.Linear` and `nn.Conv2d`? For example, handling `nn.BatchNorm`, `nn.LSTM`, etc., with custom initialization strategies.
I also want these strategies to be defined externally so that they can be easily modified without changing this core function.
Thanks!
<|end|>
<|assistant|>Sure! We can achieve this by defining a dictionary that maps layer types to their respective initialization functions. This will allow you to easily modify or extend initialization strategies without changing the core function.
Here's how you could implement it:
python
import torch.nn as nn
def init_weights(model):
'''
初始化权重，均为正态分布，方差为模型的输入维度的倒数的平方根。
Parameters:
model(nn.Module): 模型
Returns:
None
'''
def init_linear(layer):
nn.init.normal_(layer.weight, std=layer.in_features**-0.5)
nn.init.constant_(layer.bias, -np.sqrt((layer.in_features)**-1))
def init_conv(layer):
nn.init.normal_(layer.weight, std=layer.in_channels**-0.5)
def init_batchnorm(layer):
nn.init.constant_(layer.weight, 1)
nn.init.constant_(layer.bias, 0)
def init_lstm(layer):
for name, param in layer.named_parameters():
if 'weight_ih' in name:
nn.init.xavier_uniform_(param.data)
elif 'weight_hh' in name:
nn.init.orthogonal_(param.data)
elif 'bias' in name:
nn.init.constant_(param.data, 0)
init_functions = {
nn.Linear: init_linear,
nn.Conv2d: init_conv,
nn.BatchNorm2d: init_batchnorm,
nn.LSTM: init_lstm,
# Add more custom initializations here...
}
for module in model.modules():
# print(module.__class__.__name__)
init_fn = next((init_fn for layer_type, init_fn in init_functions.items() if isinstance(module, layer_type)), None)
if init_fn is not None:
init_fn(module)
This way you can easily add or modify initialization strategies by updating the `init_functions` dictionary.
Let me know if you need any further customization!
<|end|>
<|user|>This looks great! Now I want to take it a step further. Can we adjust weight initialization parameters based on the depth or position of layers within the model hierarchy? For instance, deeper layers might need different standard deviations compared to shallower ones.
Also, I'm thinking about integrating this with mixed precision training – ensuring compatibility with both float16 and float32 precision levels.
Could you help me incorporate these features?
Thanks!
<|end|>
<|assistant|>Absolutely! We can achieve hierarchical-based initialization by passing additional parameters like depth information through recursive calls. For mixed precision training compatibility, we'll ensure that all initializations respect both float16 and float32 types.
Here's an enhanced version of your function:
python
import torch.nn as nn
def init_weights(model):
'''
初始化权重，均为正态分布，方差为模型的输入维度的倒数的