pytorch

Lack of Sparse Solution with L1 Regularization in Pytorch

由 落花浮王杯 提交于 2021-01-27 12:52:48
问题 I am trying to implement L1 regularization onto the first layer of a simple neural network (1 hidden layer). I looked into some other posts on StackOverflow that apply l1 regularization using Pytorch to figure out how it should be done (references: Adding L1/L2 regularization in PyTorch?, In Pytorch, how to add L1 regularizer to activations?). No matter how high I increase lambda (the l1 regularization strength parameter) I do not get true zeros in the first weight matrix. Why would this be?

How to print the “actual” learning rate in Adadelta in pytorch

由 試著忘記壹切 提交于 2021-01-27 12:41:08
问题 In short : I can't draw lr/epoch curve when using adadelta optimizer in pytorch because optimizer.param_groups[0]['lr'] always return the same value. In detail : Adadelta can dynamically adapts over time using only first order information and has minimal computational overhead beyond vanilla stochastic gradient descent [1]. In pytorch, the source code of Adadelta is here https://pytorch.org/docs/stable/_modules/torch/optim/adadelta.html#Adadelta Since it requires no manual tuning of learning

PyTorch how to compute second order jacobian?

由 本小妞迷上赌 提交于 2021-01-27 11:50:45
问题 I have a neural network that's computing a vector quantity u . I'd like to compute first and second-order jacobian with respect to the input x , a single element. Would anybody know how to do that in PyTorch? Below, the code snippet from my project. import torch import torch.nn as nn class PINN(torch.nn.Module): def __init__(self, layers:list): super(PINN, self).__init__() self.linears = nn.ModuleList([]) for i, dim in enumerate(layers[:-2]): self.linears.append(nn.Linear(dim, layers[i+1]))

PyTorch how to compute second order jacobian?

由 懵懂的女人 提交于 2021-01-27 11:50:16
问题 I have a neural network that's computing a vector quantity u . I'd like to compute first and second-order jacobian with respect to the input x , a single element. Would anybody know how to do that in PyTorch? Below, the code snippet from my project. import torch import torch.nn as nn class PINN(torch.nn.Module): def __init__(self, layers:list): super(PINN, self).__init__() self.linears = nn.ModuleList([]) for i, dim in enumerate(layers[:-2]): self.linears.append(nn.Linear(dim, layers[i+1]))

是时候学习机器学习系统设计了！斯坦福CS 329S开课，课件、笔记同步更新

由 。_饼干妹妹 提交于 2021-01-27 09:53:22
这是一门新的课程——在学习了算法、框架等内容后，是时候深入了解一下「机器学习系统设计」了！ 机器之心报道，作者：蛋酱。 近日，斯坦福大学宣布开设一门全新课程：CS 329S《机器学习系统设计》。 课程主页： https:// stanford-cs329s.github.io / 这门课程的主讲人、计算机科学家 Chip Huyen 也在推特上认真宣传了一波（很多人应该都读过她的博客文章，因为这位小姐姐确实很有名气）。 机器学习系统设计的概念是指，为了满足特定要求，针对机器学习系统对软件体系架构、基础架构、算法和数据进行定义的过程。虽然现有的系统也可以满足大部分模型搭建的需求，但我们必须承认：首先，工具空间是不断革新的；其次，业务需求是不断变化的；最后，数据分布也是持续更替的。因此，「系统」是很容易过时的。如果不能及时更新，那么出错、崩溃都是可以预料的。这也是本门课程开设的初衷。 本门课程旨在为现实中的机器学习系统提供一个迭代框架，该框架的目标是构建一个可部署、可信赖、可扩展的系统。首先要考虑的是每个 ML 项目的利益相关者及目标，不同的目标则需要不同的设计选择，且要考虑如何权衡。 课程涵盖了 从项目界定、数据管理、模型开发、部署、基础架构、团队架构到业务分析的所有步骤 ，在每个步骤中，都会探讨不同解决方案的动机、挑战和局限性。在课程的最后一部分，将会探讨机器学习生产生态系统的未来

How to extract feature vector from single image in Pytorch?

由 谁说胖子不能爱 提交于 2021-01-27 07:02:57
问题 I am attempting to understand more about computer vision models, and I'm trying to do some exploring of how they work. In an attempt to understand how to interpret feature vectors more I'm trying to use Pytorch to extract a feature vector. Below is my code that I've pieced together from various places. import torch import torch.nn as nn import torchvision.models as models import torchvision.transforms as transforms from torch.autograd import Variable from PIL import Image img=Image.open(

How to extract feature vector from single image in Pytorch?

由 假装没事ソ 提交于 2021-01-27 07:01:53
问题 I am attempting to understand more about computer vision models, and I'm trying to do some exploring of how they work. In an attempt to understand how to interpret feature vectors more I'm trying to use Pytorch to extract a feature vector. Below is my code that I've pieced together from various places. import torch import torch.nn as nn import torchvision.models as models import torchvision.transforms as transforms from torch.autograd import Variable from PIL import Image img=Image.open(

what is uninitialized data in pytorch.empty function

由 笑着哭i 提交于 2021-01-27 05:33:13
问题 i was going through pytorch tutorial and came across pytorch.empty function. it was mentioned that empty can be used for uninitialized data . But, when i printed it, i got a value. what is the difference between this and pytorch.rand which also generates data(i know that rand generates between 0 and 1). Below is the code i tried a = torch.empty(3,4) print(a) Output: tensor([[ 8.4135e-38, 0.0000e+00, 6.2579e-41, 5.4592e-39], [-5.6345e-08, 2.5353e+30, 5.0447e-44, 1.7020e-41], [ 1.4000e-38, 5

what is uninitialized data in pytorch.empty function

由 倾然丶 夕夏残阳落幕 提交于 2021-01-27 05:32:23
问题 i was going through pytorch tutorial and came across pytorch.empty function. it was mentioned that empty can be used for uninitialized data . But, when i printed it, i got a value. what is the difference between this and pytorch.rand which also generates data(i know that rand generates between 0 and 1). Below is the code i tried a = torch.empty(3,4) print(a) Output: tensor([[ 8.4135e-38, 0.0000e+00, 6.2579e-41, 5.4592e-39], [-5.6345e-08, 2.5353e+30, 5.0447e-44, 1.7020e-41], [ 1.4000e-38, 5

PoseWarping: How to vectorize this for loop (z-buffer)

由 落花浮王杯 提交于 2021-01-27 05:31:58
问题 I'm trying to warp a frame from view1 to view2 using ground truth depth map, pose information, and camera matrix. I've been able to remove most of the for-loops and vectorize it, except one for-loop. When warping, multiple pixels in view1 may get mapped to a single location in view2, due to occlusions. In this case, I need to pick the pixel with the lowest depth value (foreground object). I'm not able to vectorize this part of the code. Any help to vectorize this for loop is appreciated.
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