importnumpyasnpimportpandasaspdfromsklearn.model_selectionimporttrain_test_splitdefTrainDataset(data_path,test_size=0.2,random_state=42):data=pd.read_csv(data_path)X_train,X_test,y_train,y_test=train_test_split(data.drop('label',axis=1),data['label'],test_size=test_size,random_state=ran...
parser.add_argument('--upload_dataset', nargs='?', const=True, default=False,help='Upload data, "val" option') 1 解析:用于上传数据集,默认关闭 命令行使用方法:python train.py --upload_dataset False 注: 1,如果命令行未使用’–upload_dataset’参数,则默认值为default=False,表示不上传数据集。
if batch_idx % 100 == 0: print('Train Epoch:{} [{}/{} ({:.0f}%)]\tLoss:{:.6f}'.format( epoch,batch_idx*len(data),len(train_loader.dataset),100.*batch_idx/len(train_loader),loss.item())) test_loss = 0 correct = 0 for data, target in test_loader: data = data.view...
DataLoader(dataset=test_data, batch_size=64, shuffle=True) cnn = torch.load("model/mnist_model.pkl") cnn = cnn.cuda() # loss # eval/test loss_test = 0 accuracy = 0 import cv2 # pip install opencv-python -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun....
return dataset 2. 网络模型 class CLDNN(nn.Cell): def __init__(self): # CNN super(CLDNN,self).__init__() self.model = SequentialCell( Conv1d(in_channels=2, out_channels=64, kernel_size=3, stride=1, pad_mode='same'), ReLU(), MaxPool1d(kernel_size=2, stride=2)...
然后设置随机种子,下一行的torch_distributed_zero_first(LOCAL_RANK)与分布式训练相关的,如果不进行分布式训练则不执行,利用check_dataset会进行数据集检查读取操作,获取训练集和测试集图片路径。接着利用nc获取数据集的种类,names会进行类的种数以及类的名称是否相同的判断,不相同会进行报错处理,然后保存类别数量以及类...
The dataset will contain the inputs in the two-dimensional array x and outputs in the one-dimensional array y:Python >>> x = np.arange(1, 25).reshape(12, 2) >>> y = np.array([0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0]) >>> x array([[ 1, 2], [ 3, 4], [ ...
python3 preprocess.py --corpus_path $CORPUS_PATH --spm_model_path $LLaMA_PATH/tokenizer.model \ --dataset_path $OUTPUT_DATASET_PATH --data_processor lm --seq_length 512 其中,--seq_length 用来指定文本长度,越长则训练时消耗的GPU内存越多。
def distributed_training(training_parameters, num_workers: int, use_gpu: bool): logger = du.create_logger() logger.info('Initializing Ray.') initialize_ray() train_data, test_data, load_time_sec = du.get_ray_dataset(training_parameters)# Scaling configuration scaling_config =...
python tools/train.py configs/faster_rcnn_r50_fpn_1x.py 所以呢,我们就可以知道,build_detection()就是将py配置文件里的数据,加载到建立的模型中,然后根据py配置文件中的数据集路径,执行build_dataset()加载数据集模型,最后进行训练train_detector()。