训练常用命令nohup python train.py > outLog.log &nohup挂后台运行，退出Xshell连接无影响，防止断网> outLog.log将输出输出到outLog.log文件&追加模式一般运行该命令后会提示如下：(base) root@d9dcc1730df7:~/data1/fmujie/dpl/cal_fgvc# nohup python train_distributed.py > outNA2245628.log & [1] 14131 (base) root@d9dcc1730df7:~/data1/fmujie/dpl/cal_fgvc# nohup: ignoring input and redirecting stderr to stdout如果忘记这个进程号也没关系ps -...

R-Drop: Regularized Dropout for Neural NetworksDropout is a powerful and widely used technique to regularize the training of deep neural networks. Dropout在训练和推理时存在不一致的问题（集成学习）R 对每个子模型的分布做一个KL散度import numpy as np def train_r_drop(ratio, x, w1, b1, w2, b2): # 输入复制一份 x = torch.cat([x, x], dim=0) layer1 = np.maximum(0, np.dot(w1, x) + b1) mask1 = np.random.binomial(1, 1...

home\.cache\torch\checkpoints有什么用，遇到就知道了，有时候集群网不好，可以本地下载后传到集群上的指定目录，就不用在集群上龟速下载了。

NN.DROPOUTCLASStorch.nn.Dropout(p=0.5, inplace=False)Parametersp (float) – probability of an element to be zeroed. Default: 0.5inplace (bool) – If set to True, will do this operation in-place. Default: FalseShape:Input: (∗)(∗). Input can be of any shapeOutput: (∗)(∗). Output is of the same shape as inputm = nn.Dropout(p=0.2) input = torch.randn(20, 16) output = m(input)如何判断当前是否为Trai...

Saving and loading models for inference in PyTorchThere are two approaches for saving and loading models for inference in PyTorch.The first is saving and loading the state_dictand the second is saving and loading the entire model. def state_dict(self, *args, destination=None, prefix='', keep_vars=False): r"""Returns a dictionary containing references to the wh...