08 2023 档案
摘要：# python多线程 # 多线程 threading，利用CPU和IO可以同时执行的原理 # 多进程 multiprocessing，利用多核CPU的能力，真正的并行执行任务 # 异步IO asyncio，在单线程利用CPU和IO同时执行的原理，实现函数异步执行 * 使用Lock对资源加锁，防止冲
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摘要：import torch from torch import nn from torch.nn import functional as F from d2l import torch as d2l class Inception(nn.Module): # c1-c4是每条路径的输出通道数 def
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摘要：import torch from torch import nn from d2l import torch as d2l def nin_block(in_channels,out_channels,kernel_size,strides,padding): return nn.Sequenti
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摘要：import torch from torch import nn from d2l import torch as d2l def vgg_block(num_convs,in_channels,out_channels): layers = [] for _ in range(num_convs
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摘要：import torch from torch import nn from d2l import torch as d2l net = nn.Sequential( # (224-11+1+2)/4=54 nn.Conv2d(1,96,kernel_size=11,stride=4,padding
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摘要：import torch from torch import nn from d2l import torch as d2l class Reshape(torch.nn.Module): def forward(self,x): # 批量大小默认，输出通道为1 return x.view(-1,1
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摘要：import torch from torch import nn from d2l import torch as d2l # 实现池化层的正向传播 def pool2d(x,pool_size,mode='max'): # 获取窗口大小 p_h,p_w=pool_size # 获取偏移量 y=t
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摘要：import torch from d2l import torch as d2l from torch import nn # 多输入通道互相关运算 def corr2d_multi_in(x,k): # zip对每个通道配对,返回一个可迭代对象,其中每个元素是一个(x,k)元组,表示一个输入通道
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摘要：import torch from torch import nn def comp_conv2d(conv2d,x): # 在维度前面加上通道数和批量大小数1 x=x.reshape((1,1)+x.shape) # 得到4维 y=conv2d(x) # 把前面两维去掉 return y.resh
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摘要：import torch from torch import nn from d2l import torch as d2l def corr2d(x,k): """计算二维互相关运算""" # 获取卷积核的高和宽 h,w=k.shape # 输出的高和宽 y=torch.zeros((x.shap
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摘要：import os os.environ['KMP_DUPLICATE_LIB_OK']='True' import hashlib import tarfile import zipfile import requests import numpy as np import pandas as p
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