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When model uses specific layers/part-Dropouts Layers, BatchNorm Layers etc, it can be turned on during the train mode, but it needs to be turned off during the evaluation mode. model.train() model.eval() Sets model in training mode: activates specific layers/part-Dropout layers etc normalisation layers1 use per-batch statistics Sets model in evaluation (inference) mode: de-activates Dropout laye..

view It merely creates a view of the original tensor. Same function as reshape in numpy. -1 : It means I am not sure so I will let pytorch to decide the dimension. torch.FloatTensor([[[1.,2.],[3.,4.]],[[5.,6.],[7.,8.]]]) >>> tensor([[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]]]) t.view([-1,2]) >>> tensor([[1., 2.], [3., 4.], [5., 6.], [7., 8.]]) a=torch.randn(1,2,3,4) a >>> tensor([[[[ 0.3861, -0...

Error $ pip install pytorch ... Building wheels for collected packages: pytorch Building wheel for pytorch (setup.py) ... error error: subprocess-exited-with-error ... Solution Must find the command here : https://pytorch.org/get-started/locally/ $ conda install pytorch -c pytorch Collecting package metadata (current_repodata.json): done Solving environment: done ... pip-22.3.1 | 2.8 MB | ######..

contiguous Returns a contiguous in memory tensor containing the same data as self tensor. If self tensor is already in the specified memory format, this function returns the self tensor. Let's just define two tensors that shape are same. a = torch.randn(3, 4) a.transpose_(0, 1) b = torch.randn(4, 3) # both tensor shape is (4, 3) print(a) >>> tensor([[-0.7290, 0.7509, 1.1666], [-0.9321, -0.4360, ..

permute Returns a view of the original tensor input with its dimensions permuted. It can exchange all dimentions. torch.randn(2, 3, 5) --> permute(2,0,1) --> 5, 2, 3 x = torch.randn(2, 3, 5) >>> tensor([[[-1.7663, 1.2394, -0.4237, -2.3218, 0.1908], [ 1.6466, 0.8089, -2.2276, -0.5936, 2.8272], [ 0.8317, 0.9335, -1.3993, -0.3371, 1.3174]], [[ 0.5403, -0.4540, -0.5958, 0.4685, 0.5951], [ 0.9191, 0...