pytorch | 易学教程

Why is the Pytorch Dropout layer affecting all values, not only the ones set to zero?

阅读更多关于 Why is the Pytorch Dropout layer affecting all values, not only the ones set to zero?

问题 The dropout layer from Pytorch changes the values that are not set to zero. Using Pytorch's documentation example: (source): import torch import torch.nn as nn m = nn.Dropout(p=0.5) input = torch.ones(5, 5) print(input) tensor([[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.]]) Then I pass it through a dropout layer: output = m(input) print(output) tensor([[0., 0., 2., 2., 0.], [2., 0., 2., 0., 0.], [0., 0., 0., 0., 2.], [2., 2., 2.,

pytorch android部署 demo 用自己训练的自定义模型踩坑记录

阅读更多关于 pytorch android部署 demo 用自己训练的自定义模型踩坑记录

记录一个用自己定义的模型（一个稍微改了分类数目的vgg网络，分40类）加到 github项目里面时遇到的小坑： 2021 - 01 - 26 19:02:42 . 191 19212 - 19370 / org . pytorch . demo E / AndroidRuntime: FATAL EXCEPTION: ModuleActivity Process : org . pytorch . demo , PID: 19212 com . facebook . jni . CppException: aten::_convolution ( Tensor input , Tensor weight , Tensor? bias , int [ ] stride , int [ ] padding , int [ ] dilation , bool transposed , int [ ] output_padding , int groups , bool benchmark , bool deterministic , bool cudnn_enabled ) - > ( Tensor ) : Expected at most 12 arguments but found 13 positional arguments . : / home / xutengfei / .

斯坦福教授| 什么是博士论文？

阅读更多关于斯坦福教授| 什么是博士论文？

本文中的闪图复制自北岭加州州立大学（California State University, Northridge）网页 https://www. csun.edu/~vcpsy00h/crea tivity/define.htm 注：文末附交流群，最近赶ACL，比较忙，很多同学加了没有回过期了，可以重新加一下，备注好的一定会回复，敬请谅解。这是我在1993年写给一名学生的信，内容涉及他的论文初稿。2003年，我修改了一下这封信，删除了与该学生相关的具体内容，并将修改后的这封信作为对所有研究生的额外知识要求。 I wrote this in 1993 as a letter to a student concerning a draft of his dissertation. in 2003 I edited it to remove some specific references to the student and present it as a small increment to the information available to my grad students. --spaf 先让我以一些看似显而易见的事情开始。Let me start by reviewing some things that may seem obvious: 第一

Pytorch Linear Layer now automatically reshape the input?

阅读更多关于 Pytorch Linear Layer now automatically reshape the input?

问题 I remember in the past, nn.Linear only accepts 2D tensors. But today, I discover that nn.Linear now accepts 3D, or even tensors with arbitrary dimensions. X = torch.randn((20,20,20,20,10)) linear_layer = nn.Linear(10,5) output = linear_layer(X) print(output.shape) >>> torch.Size([20, 20, 20, 20, 5]) When I check the documentation for Pytorch, it does say that it now takes Input: :math: (N, *, H_{in}) where :math: * means any number of additional dimensions and :math: H_{in} = \text{in\

Understanding cdist() function

阅读更多关于 Understanding cdist() function

问题 What does this new_cdist() function actually do? More specifically: Why is there a sqrt() operation when the AdderNet paper does not use it in its backward propagation equation? How is needs_input_grad[] used? def new_cdist(p, eta): class cdist(torch.autograd.Function): @staticmethod def forward(ctx, W, X): ctx.save_for_backward(W, X) out = -torch.cdist(W, X, p) return out @staticmethod def backward(ctx, grad_output): W, X = ctx.saved_tensors grad_W = grad_X = None if ctx.needs_input_grad[0]:

PyTorch transfer learning with pre-trained ImageNet model

阅读更多关于 PyTorch transfer learning with pre-trained ImageNet model

问题 I want to create an image classifier using transfer learning on a model already trained on ImageNet. How do I replace the final layer of a torchvision.models ImageNet classifier with my own custom classifier? 回答1: Get a pre-trained ImageNet model ( resnet152 has the best accuracy): from torchvision import models # https://pytorch.org/docs/stable/torchvision/models.html model = models.resnet152(pretrained=True) Print out its structure so we can compare to the final state: print(model) Remove

PyTorch transfer learning with pre-trained ImageNet model

阅读更多关于 PyTorch transfer learning with pre-trained ImageNet model

Mini batches with DataLoader and a 3D input. (Pytorch)

阅读更多关于 Mini batches with DataLoader and a 3D input. (Pytorch)

问题 I have been struggling to manage and create batches for a 3D tensor. I have used it before as a way to create batches for 1D tensor. However, in my current research, I need to create batches out of a tensor with shape (1024,1024,2). I created custom data to use as my input for the DataLoader method in pytorch. I created the following for the 1D array: class CustomDataset(Dataset): def __init__(self, x_tensor, y_tensor): self.xdomain = x_tensor self.ydomain = y_tensor def __getitem__(self,

Loading a huge dataset batch-wise to train pytorch

阅读更多关于 Loading a huge dataset batch-wise to train pytorch

问题 I am training a LSTM in-order to classify the time-series data into 2 classes(0 and 1).I have huge data-set on the drive where where the 0-class and the 1-class data are located in different folders.I am trying to train the LSTM batch-wise using by creating a Dataset class and wrapping the DataLoader around it. I have to do pre-processing such as reshaping.Here's my code which does that ` class LoadingDataset(Dataset): def __init__(self,data_root1,data_root2,file_name): self.data_root1=data

determinant of a complex matrix in PyTorch

阅读更多关于 determinant of a complex matrix in PyTorch

问题 Is there a way to calculate the determinant of a complex matrix in PyTroch? torch.det is not implemented for 'ComplexFloat' 回答1: Unfortunately it's not implemented currently. One way would be to implement your own version or simply use np.linalg.det . Here is a short function which computes the determinant of a complex matrix that I wrote using LU-decomposition: def complex_det(A): def complex_diag(A): return torch.view_as_complex(torch.stack((A.real.diag(), A.imag.diag()),dim=1)) #Perform LU