from time import time @cuda.jit def gpu_add(a, b, result, n): idx = cuda.threadIdx.x + cuda.blockDim.x * cuda.blockIdx.x if idx < n : result[idx] = a[idx] + b[idx] def main(): n = 20000000 x = np.arange(n).astyp
for data in testloader: images, labels = data if CUDA: images = images.cuda() labels = labels.cuda() outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum() print('Accuracy on the test set: %d %%' % ...
import torch from joblib import dump, load import torch.utils.data as Data import numpy as np import pandas as pd import torch import torch.nn as nn # 参数与配置 torch.manual_seed(100) # 设置随机种子,以使实验结果具有可重复性 device = torch.device("cuda" if torch.cuda.is_available() ...
defdetect()# Initializeset_logging()# device=select_device(device)device=torch.device("cuda:0"iftorch.cuda.is_available()else"cpu")#若有gpu可用则用gpu # half&=device.type!='cpu'# half precision only supported onCUDAw=weights[0]ifisinstance(weights,list)elseweights classify,pt,onnx=False,...
device=torch.device('cuda'iftorch.cuda.is_available()else'cpu')net=net.to(device)# 简单通过.to(device),数据或模型就可以转移至GPU#训练模型print('training on: ',device)deftest():net.eval()acc=0.0sum=0.0loss_sum=0forbatch,(data,target)inenumerate(test_loader):data,target=data.to(device...
model.fc = nn.Linear(num_ftrs,10)# 将模型移动到GPU(如果可用)device = torch.device("cuda:0"iftorch.cuda.is_available()else"cpu") model = model.to(device) 步骤4:定义损失函数和优化器 我们选择交叉熵损失函数(Cross Entropy Loss)作为模型训练的损失函数,并使用Adam优化器进行优化。
1. CUDA的安装 (a) 进入下载地址:CUDA下载链接 请注意对于win11而言,CUDA11.4后才有针对win11的版本,因此我们基于参考文献【1】,选择了CUDA 11.5.2版本。 (b) 点开CUDA Toolkit 11.5.2,依次点击windows--->x86_64--->11--->exe(local),下载得到zip压缩包。
(), ) )ifargs.dry_run:breakdeftest(model, device, test_loader): model.eval() test_loss =0correct =0withtorch.no_grad():fordata, targetintest_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss( output, target, reduction="...
importtorchfromjoblibimportdump, loadimporttorch.utils.data asDataimportnumpyasnpimportpandasaspdimporttorchimporttorch.nnasnn# 参数与配置torch.manual_seed(100)# 设置随机种子,以使实验结果具有可重复性device = torch.device("cuda"iftorch.cuda.is_available()else"cpu")# 有GPU先用GPU训练# 加载数据集def...
x = x.reshape((0, -1)) x = F.tanh(self.fc1(x)) x = F.tanh(self.fc2(x)) return xnet = Net()# 初始化与优化器定义# set the context on GPU is available otherwise CPUctx = [mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()]...