x = self.pool1(x) # output(16, 14, 14) x = F.relu(self.conv2(x)) # output(32, 10, 10) ;池化层只改变矩阵的高和宽,不会影响深度(28-2)/2+1 x = self.pool2(x) # output(32, 5, 5) x = x.view(-1, 32*5*5) # output(32*5*5) x = F.relu
Hi, by executing this python3 train.py --name cifar10-100_500 --dataset cifar10 --model_type ViT-B_16 --pretrained_dir checkpoint/ViT-B_16.npz I encounter the error: Traceback (most recent call last): File "train.py", line 17, in <module...
from mindspore import common, dataset, mindrecord, train, log, amp File "/modelarts/authoring/notebook-conda/envs/mindaspore_work/lib/python3.7/site-packages/mindspore/mindrecord/__init__.py", line 30, in <module> from .tools.cifar10_to_mr import Cifar10ToMR File "/modelarts/authorin...
~/anaconda3/lib/python3.7/site-packages/torchvision/datasets/__init__.py in <module> ---> 1 from .lsun import LSUN, LSUNClass 2 from .folder import ImageFolder, DatasetFolder 3 from .coco import CocoCaptions, CocoDetection 4 from .cifar import CIFAR10, CIFAR100 5 from .stl10 import STL...
第八步:创建类别DataSet,实例化dataset.train和dataset.val,创建.next_batch函数, 第九步:next_batch函数说明:使用一个变量self._epoch_index 对start和end进行递增循环,如果end > self._num_image, 将start置为0, self._epoch_index置为batch_size。
start = time.time()for i in range(10):list_1 = np.array(np.arange(1,10000))list_1 = np.sin(list_1)print("使用Numpy用时{}s".format(time.time()-start)) 从如下运行结果,可以看到使用 Numpy 库的速度快于纯 Python 编写的代码: ...