pruning

问题 My Prune code is shown below, after running this, I will get a file named 'pruned_model.pth'. import torch from torch import nn import torch.nn.utils.prune as prune import torch.nn.functional as F from cnn import net ori_model = '/content/drive/My Drive/ECG_weight_prune/checkpoint_dir/model.pth' save_path = '/content/drive/My Drive/ECG_weight_prune/checkpoint_dir/pruned_model.pth' device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = net().to(device) model.load_state

问题 The following question is not a duplicate of How to apply layer-wise learning rate in Pytorch? because this question aims at freezing a subset of a tensor from training rather than the entire layer. I am trying out a PyTorch implementation of Lottery Ticket Hypothesis. For that, I want to freeze the weights in a model that are zero. Is the following a correct way to implement it? for name, p in model.named_parameters(): if 'weight' in name: tensor = p.data.cpu().numpy() grad_tensor = p.grad

问题 The following question is not a duplicate of How to apply layer-wise learning rate in Pytorch? because this question aims at freezing a subset of a tensor from training rather than the entire layer. I am trying out a PyTorch implementation of Lottery Ticket Hypothesis. For that, I want to freeze the weights in a model that are zero. Is the following a correct way to implement it? for name, p in model.named_parameters(): if 'weight' in name: tensor = p.data.cpu().numpy() grad_tensor = p.grad

问题 I have a requirement where I need to group my categorical variables (having more than 5 category values) into 5 groups based on their association with my continuous variable. To achieve this I am using rpart with " annova " method. So for example my categorical variable is type having codes 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 so I want to have 5 groups of this variable. After running the tree inorder to have only 5 groups I need to prune the tree. One way I tried is to use the nsplit from

问题 In alpha, beta pruning algorithm, I have a class in which a function def getAction(self,gamestate) id defined. I made 2 more function in def getAction Like: class BAC: def you(self,gamestate): def me(gamestate,depth,alpha, beta): ------ return v def both(gamestate,depth,alpha, beta): ------------------- return v return me(gamestate,0,alpha, beta)- I need to put alpha, beta in functions me and both. But where do I define alpha and beta values. If I define alpha and beta in def me and def both

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 years ago . Is there a free tool capable of pruning unused code from a CLI assembly? I know there are obfuscators that are capable of performing this optimization, but these all cost money. Is there a free (or even open source) tool that removes the unused code in an already compiled assembly? 回答1: There is. It's called the

问题 I use sklearn.tree.DecisionTreeClassifier to build a decision tree. With the optimal parameter settings, I get a tree that has unnecessary leaves (see example picture below - I do not need probabilities, so the leaf nodes marked with red are a unnecessary split) Is there any third-party library for pruning these unnecessary nodes? Or a code snippet? I could write one, but I can't really imagine that I am the first person with this problem... Code to replicate: from sklearn.tree import

问题 I'm trying to design a neural network using Keras with priority on prediction performance, and I cannot get sufficiently high accuracy by further reducing the number of layers and nodes per layer. I have noticed that very large portion of my weights are effectively zero (>95%). Is there a way to prune dense layers in hope of reducing prediction time? 回答1: Not a dedicated way :( There's currently no easy (dedicated) way of doing this with Keras. A discussion is ongoing at https://groups.google

问题 Is rpart automatic pruning? The decision tree produced by rpart is much more levels than that produced by Oracle Data Mining which has the automatic pruning. 回答1: No, but the defaults for the fitting function may stop splitting "early" (for some definition of "early"). See ?rpart.control for the parameters you can tweak. In particular, see the argument minsplit and minbucket in that help file. These are stopping rules that will prevent any node being split if those conditions are not met. You

问题 As you see in images below, in some of my segmentation result(segmentation done by watershed transformation method), there are some leftovers left. I want to somehow crop the images so that only the rectangles remain. This operation is only based on rectangle shape and it doesn't relate to intensity level. 回答1: Solution explanation I suggest the following approach: generate an initial guess for the 4 corners of the shape according to geometric properties (see code below for further details).