Pytorch sample weight. Finally we’ll end with recommendations from the literature for using 2 PyTorch solution Well, actually I have gone through docs and you can simply use pos_weight indeed. CrossEntropyLoss (weights) (my problem is classification) where i weight each class for the update part My own personal theory, for which I have absolutely no evidence, says that if WeightedRandomSampler is likely to give you a batch with duplicate samples from your underrepresented class (the class Knowledge Distillation Tutorial # Created On: Aug 22, 2023 | Last Updated: Jan 24, 2025 | Last Verified: Nov 05, 2024 Author: Alexandros Chariton Knowledge distillation is a technique that enables knowledge transfer from large, computationally expensive models to smaller ones without losing validity. In the following sections, we’ll build a neural network to classify images in the FashionMNIST dataset. Before proceeding further, let’s recap all the classes you’ve seen so far. 9921, 0. Also holds the gradient w. For example, if a dataset contains 100 positive and 300 negative examples of a single class, then pos_weight for the class should be equal to 300/100=3. So I first run as standard PyTorch code and then manually both. Jul 17, 2024 · The nn. Have a look at this post for an example. You can collaborate on training, local and regional events, open-source developer tooling, academic research, and guides to help new users and contributors have a productive experience. Oct 14, 2019 · The weight parameter is usually with respect to labels, not batch samples ! It will apply the weight depending on the ground truth label for the given sample. CrossEntropyLoss (weights) (my problem is classification) where i weight each class for the update part My own personal theory, for which I have absolutely no evidence, says that if WeightedRandomSampler is likely to give you a batch with duplicate samples from your underrepresented class (the class Mar 18, 2018 · I’m doing an image segmentation task here and I’m trying to give weights to gradients in such a way that some resulting pixels on different pictures are going to have a different weight. weight_norm() which uses the modern parametrization API. It influences aspects such as gradients and the output subspace. I believe in case of non-mean reductions the sample loss is just scaled by respective class weight for that sample. In case of image classification where we have one label for each class, we can compute samples weight using sklearn library. This nested structure allows for building and managing complex architectures easily. Tensor with shape (270,) defining weight for each class. By default, the losses are averaged over each loss element in the batch. The length of the weight parameter should be equal to the number of classes. The only solution that I find in pytorch is by using WeightedRandomSamplerwith DataLoader, that is simply a way to take more or less the same number of samples per each class (and Models and pre-trained weights The torchvision. Setting Up the PyTorch Model and Custom Layers Before we explore weight initialization, let’s set up a PyTorch model that will serve as our testbed. Aug 30, 2022 · Now, that we have our class counts, we can calculate the weight for each class by taking the reciprocal of the count. This blog post will delve into the fundamental concepts of PyTorch sample weights, explore their usage methods, common practices, and best practices. CrossEntropyLoss () uses for the class-wise weight. Recap: torch. 5, 0] and the label for a sample is one-hot encoded as [1, 0, 1], then the total weight for that sample would be 1. These losses are multiplied by an individual sample weight lambda. This argument gives weight to positive sample for each class, hence if you have 270 classes you should pass torch. non-weighted data sample ratio 0. Compared to the previous model,its performance isn’t that great,so I was wondering can I use Aug 7, 2019 · Hi, Is there any method that can sample with weights under the distributed case? Thanks. In this post, we will Jul 18, 2024 · Weight initialization in PyTorch is a powerful tool in your deep learning toolkit. We‘ll explore the ins and outs of serializing model architecture and weight parameters, best practices for managing saved model files, and tackle common challenges that arise. For example, if you have 300 positive samples and 200 negative ones, you should set pos_weight to 200 / 300. I was used to Keras’ class_weight, although I am not sure what it really did (I think it was a matter of penalizing more or less certain classes). Jul 23, 2025 · Class imbalance is a common challenge in machine learning, where certain classes are underrepresented compared to others. Good Jan 18, 2018 · 本文介绍了如何在Pytorch中使用样本权重进行深度学习的多分类任务。通过将标签转为one-hot形式，并将每个one-hot标签的1替换为样本权重，可以调整损失函数，公式变为loss = - Q * log (P) * sample_weight。提供了相应的Pytorch示例代码。 Saving and Loading Models # Created On: Aug 29, 2018 | Last Updated: Jun 26, 2025 | Last Verified: Nov 05, 2024 Author: Matthew Inkawhich This document provides solutions to a variety of use cases regarding the saving and loading of PyTorch models. data. 0, generator=None) [source] # Fill the input Tensor with values drawn from the uniform distribution. PyTorch provides numerous strategies for weight initialization, including methods like drawing samples from uniform and normal distributions, as well as sophisticated approaches such as Xavier (Glorot) initialization and . fit(sample_weight=?) tried using torch. nn. It is very similar to Noise Contrastive Estimation (NCE) and Negative Sampling, both of which are popular in natural language processing, where the vocabulary size can be very large. 3:drooling_face: 1 Like ptrblck May 9, 2018, 8:46am 2 You are using it correctly! However, I think there is an explanation missing on how size_average works regarding the weight in the docs. a text data point becomes (sentence, label, weight for sampling) UNLESS some order is implied on the data set before we can use WeightedRandomSampler. No warning will be raised and it is the user’s responsibility to ensure that target contains valid probability distributions. utils. Feb 8, 2022 · Quantization is a cheap and easy way to make your DNN run faster and with lower memory requirements. In this blog post, we’ll lay a (quick) foundation of quantization in deep learning, and then take a look at how each technique looks like in practice. The stronger the weight, the more likely that sample will get sampled. This version is more numerically stable than using a plain Sigmoid followed by a BCELoss as, by combining the operations into one layer, we take advantage of the log-sum-exp trick Every module in PyTorch subclasses the nn. Must be a vector with length equal to the number of classes. In Keras I could get around by using sample_weight with sample_weight Aug 15, 2023 · はじめに pytorchのWeightedRandomSamplerについてまとめてみた。 なお本記事は英文のこちらの記事を参考にまとめているのでご承知おきください。 参考記事にもあるように、WeightedRandomSamplerの公式ドキュメントを見ても実装方法につ Jun 13, 2025 · Each sample obtained from the dataset is processed with the function passed as the collate_fn argument. I was wondering how we can compute the samples weight in this case? May 27, 2021 · I am training a PyTorch model to perform binary classification. Parameters tensor (Tensor) – an n-dimensional torch. Here is marginally modified snippet from documentation: Mar 20, 2021 · I am using Python 3. Sep 29, 2017 · And if that’s the case, you’d have to write code that computes this weight per data point and somehow “attach” that weight to the data point, e. 0, 0. In the above code, we calculate the sample weights by element-wise multiplying the class_weights with the class_labels of each sample, and then aggregating them through a sum operation. Jul 20, 2024 · Master PyTorch model weight management with our in-depth guide. optim. 4611, 0. (To be exact there is 95 times more background Mar 30, 2019 · I need to change the weights at specific layers of ResNet-152 during training. PyTorch offers a few different approaches to quantize your model. General information on pre-trained weights TorchVision offers pre-trained weights for every Jul 10, 2023 · To give more importance to a certain class in the CrossEntropyLoss, we can use the weight parameter in the PyTorch implementation of the loss function. A neural network is a module itself that consists of other modules (layers). Yet, in the case of mean reduction, the loss is first scaled per sample, and then the sum is normalized by the sum of weights within the batch Oct 30, 2018 · To handle unbalanced data, I would like to weight each class according to their data distribution. Sep 19, 2018 · The 1D division (sample)-weight tensor would be also returned from the DataLoader or do you need to calculate and load it from “somewhere else”? As far as I understand the divisions vary based on some criteria of your recording. Dec 28, 2020 · 此步非必须，因为我们给定各个样本不同权重其实就是要使得各个样本的loss有区别的。 最后，其实也可以不使用CrossEntropy，而使用softmax+nnl_loss函数来给各个样本添加权重，这种方式更灵活，也稍微麻烦一些。 参考： Per-class and per-sample weighting Mar 13, 2020 · The weight tensor should contain a weight value for each sample, while yours seem to contain the class weights. On the other hand, replacement=False will still use the sample weights to draw the samples, however already picked May 27, 2025 · PyTorch Implementation Use the weight argument in loss functions like torch. By assigning different weights to individual samples, we can guide the model to pay more attention to important samples and improve its performance. With fixed seed 12345, x should be # tensor([0. The loss would act as if the dataset contains Aug 7, 2018 · I am trying to find a way to deal with imbalanced data in pytorch. Compose([ transforms. If you have some scheme for assigning samples to classes, you could then construct a vector of sample weights (for each batch of samples) that are given by the class weight of each sample’s corresponding class. Pytorch uses weights instead to random sample training examples and they state in the doc that the weights don't have to sum to 1 so that's what I mean that it's not exactly like numpy's random choice. Introduction ¶ The goal of skorch is to make it possible to use PyTorch with sklearn. com Nov 19, 2021 · In this short post, I will walk you through the process of creating a random weighted sampler in PyTorch. Scale(600 Get model weights in PyTorch with just a few lines of code. 9817, 0. What is the correct way of simulating a class Apr 24, 2020 · I was trying to understand how weight is in CrossEntropyLoss works by a practical example. This can lead to biased models that perform poorly on minority classes. init module in PyTorch provides a variety of weight initialization techniques that can be applied to different layers of a neural network. This simple guide will show you how to load, save, and transfer model weights between different models and projects. tensor ( [1, 3]) the same as weight=torch. See full list on towardsdatascience. In my case, I need to weight sample-wise manner. parametrizations. original0 and parametrizations. I think there has been a similar question sometime earlier, but I cannot find it! May 10, 2022 · Similar to this code, but in my case, the previous implementation in the pytorch forum does not work, so I rewrote this code and found that the new codes work well. Ignored when reduce is False. Gradients wrt weights in pytorch are always accumulated (even over the mini-batch). Mar 13, 2020 · Is there a simple way to get this weight value for each sample knowing the ammount of samples of each class? You would need to get the target tensor beforehand to be able to create the weights for each sample. , pos_weight (Tensor, optional ) – a weight of positive examples. However, for segmentation, for each image we have multiple classes. Feb 25, 2021 · Hi, the only way to do some kind ‘sample - weighting’ is by setting reduction='none' (you’ll get the individual loss values) and multiply them with your custom sample - weight what you should therefore provide. Note that for some losses, there are multiple elements per Dec 17, 2017 · what is the interpretation of weight here? weight = 1. If you want gradients wrt each sample, you will have to run each sample individually through the network. This package generally follows the design of the TensorFlow Distributions skorch documentation A scikit-learn compatible neural network library that wraps PyTorch. This makes the model more sensitive to the minority classes, as the misclassification of a minority class sample will result in a larger loss. Learn to save, load, and leverage pre-trained models for efficient deep learning workflows. ) But perhaps this is what you’ve already done. This allows for deployment on less powerful hardware, making evaluation faster and more Dec 27, 2023 · In this comprehensive guide, I will walk through exactly how to save PyTorch deep learning models to disk and reload them for continued training, transfer learning, and inference deployment. The dataloader for this model uses random sampling. edu) 5 days ago · Join PyTorch Foundation As a member of the PyTorch Foundation, you’ll have access to resources that allow you to be stewards of stable, secure, and long-lasting codebases. Is that the same thing as pos_weight=3 Also, is weight normalized? Is weight=torch. When I use the binary_cross_entropy_with_logits function, I found: import torch import torch. When automatic batching is disabled, the default collate_fn simply converts NumPy arrays into PyTorch Tensors, and keeps everything else untouched. Table of Content Understanding Class May 1, 2017 · Hello guys, I would like to implement below loss function which is a weighted mean square loss function: How can I implement such a lost function in pytorch? In another words, Is there any way to use nn. This allows the construction of stochastic computation graphs and stochastic gradient estimators for optimization. In this article, we'll think through the core idea of the Jun 16, 2025 · Master Adam optimizer in PyTorch with practical examples. / class_sample_count shouldn’t the weight be the class frequency ? weight = numDataPoints / class_sample_count Mar 15, 2021 · I’m confused reading the explanation given in the official doc i. the tensor. But the losses are not the same. shirui-japina (Shirui Zhang) October 15, 2019, 1:54am 3 albanD: Jul 23, 2025 · PyTorch has developed a strong and adaptable framework for creating deep neural networks (DNNs) in the field of deep learning. […] Warning This function is deprecated. May 2, 2022 · I am working on a project where I have to calculate the losses for each individual sample t. The docs for BCELoss and CrossEntropyLos Nov 25, 2020 · The WeightedRandomSampler expects weights for each sample and uses it to draw the corresponding sample. Jul 10, 2025 · Sample weights allow us to assign different weights to individual samples during the training process, enabling the model to pay more attention to important samples. flow_from_directory(directory=train_dir, target_size=input_shape, batch_size=batch_size, Jun 13, 2025 · Each sample obtained from the dataset is processed with the function passed as the collate_fn argument. Jul 10, 2025 · PyTorch sample weights provide a powerful mechanism to handle scenarios where not all samples are equally important. init. May 16, 2017 · Hey there, I’m trying to increase the weight of an under sampled class in a binary classification problem. Sequential. If you have only one class, pos_weight should contain only one argument. Migration guide: The magnitude (weight_g) and direction (weight_v) are now expressed as parametrizations. Jun 9, 2019 · I have the distribution of each weight (and bias) of a neural network. ImageFolder(traindir, transforms. Default: True reduce (bool, optional) – Deprecated (see reduction). It is very straightforward in Tensofrflow as the foloowing from sklearn. models subpackage contains definitions of models for addressing different tasks, including: image classification, pixelwise semantic segmentation, object detection, instance segmentation, person keypoint detection, video classification, and optical flow. I would now like to calculate the gradients for each individual loss before multiplying them with the given sample weight. skorch does not re-invent the wheel, instead getting as much out of your way as possible. nn. I also believe this feature should be added for many users. By understanding and applying these techniques, you're setting your models up for faster convergence, better performance, and fewer headaches during training. This is because both the THNN backend and CuDNN dont support individual sample gradients wrt weight. Initialization of weights is critical in deciding how successfully your neural network will learn from input and converge to a suitable answer. uniform_(tensor, a=0. 7 to manually assign and change the weights and biases for a neural network. The expectation of pos_weight is that the model will get higher loss when the positive sample gets the wrong label than the negative sample. Sep 25, 2019 · Hi, There have been previous discussions on weighted BCELoss here but none of them give a clear answer how to actually apply the weight tensor and what will it contain? I’m doing binary segmentation where the output is either foreground or background (1 and 0). Is there an already implemented way of do it? Thanks Code: train_loader = torch. weight_orig stores the unpruned version of the tensor. cs. 1784, 0. Installing PyTorch On your own computer Anaconda/Miniconda: conda install pytorch -c pytorch Others via pip: pip3 install torch On Princeton CS server (ssh cycles. original1 respectively. cross_entropy ()? How to use it correctly? I’m using pytorch 0. When it comes to saving and loading models, there Parameters weight (Tensor, optional) – a manual rescaling weight given to the loss of each batch element. I’m not sure if I’m missing something. py at main · pytorch/examples Dec 12, 2024 · Let’s get into it. 4076 whatever w is. Here is an example how to use it. The bias was not pruned, so it will remain intact. unsqueeze(0) to add a fake batch dimension. Jul 27, 2025 · In PyTorch, we can assign a weight to each class, and the loss for samples belonging to a particular class is multiplied by the corresponding weight. The new weight_norm is compatible with state_dict generated from old weight_norm. PyTorch does not validate whether the values provided in target lie in the range [0,1] or whether the distribution of each data sample sums to 1. t. An alternative is to sample from negative BCEWithLogitsLoss # class torch. Aug 30, 2021 · Hi everyone, I have gotten confused in understanding the “pos_weight” and “weight” parameters in BCEWithLogitsLoss. This package generally follows the design of the TensorFlow Distributions Mar 28, 2017 · there is no way to access the gradients wrt weight for each individual sample. In practice, we can do this directly from the class counts, as demonstrated below: Jul 20, 2019 · You could treat each occurrence of a class as the positive sample and could calculate the pos_weight for each class. If this is Sampled Softmax Loss Sampled Softmax is a drop-in replacement for softmax cross entropy which improves scalability e. My minority class makes up about 10% of the data, so I want to use a weighted loss function. I also have another model,which given an image predicts it’s age weight and body tone. This will ensure that classes with a higher representation will have a smaller weight. torch. Cons Requires careful tuning of the class weights in the loss function. (This is actually the same as what is mentioned in this thread. DataLoader( datasets. princeton. However, I am not sure about the dimensions. It will indeed rescale the gradients and is very useful if your dataset is unbalanced for example. 0832, 0. But my dataset is highly imbalanced and there is way more background than foreground. appending "_orig" to the initial parameter name). Parameter() but seemed useless; expecting some solutions plz. Module - Neural network module. MSELoss to achieve to my mentioned loss function? Nov 21, 2022 · Basically, I have different weight assigned to each example and I am using the weighted MSE loss function. Mar 14, 2022 · Does the weight parameter do the same thing? Say that I set it weight=torch. by iterating the Dataset once. Because, some parts of the images are irrelevant to me and I don’t want to bother the net with learning stuff that isn’t important. I suppose that I should build a new sampler. What is a fast way to generate sample neural networks? To be specific, the distribution is assumed to be Gaussian and the mean and variance are stored in two dictionaries. For one sample loss, calculating the gradients, in my case, results Jan 11, 2020 · Thanks for you answer. Mar 23, 2022 · I have used weighted sample dataloader for performing classification task where the objective of the model is to determine which class does the image belong. if your complete dataset contains 100 samples in total, 90 class0 samples, and 80 class1 samples, your pos_weight could be calculated as negative/positive = [10/90, 20/80]. tensor ( [3, 9]), or are they different in how they affect the magnitude of the loss? ptrblck March 15, 2022, 2:58am 2 Santosh Apr 30, 2020 · Use torch. As an example, I have defined a LeNet-300-100 fully-connected neural network to train on MNIST dataset. Use torch. This can be done offline before training e. Jan 11, 2020 · Thanks for you answer. The fastest way I got so far takes about 1 minute to generate a sample neural Probability distributions - torch. If given, has to be a Tensor of size nbatch. CrossEntropyLoss. U (a, b) \mathcal {U} (a, b) U (a,b). These techniques offer flexibility in initializing the weights based on the specific requirements of each layer. It will virtually “reduce” the set of positive samples from 300 to 200. BCEWithLogitsLoss(weight=None, size_average=None, reduce=None, reduction='mean', pos_weight=None) [source] # This loss combines a Sigmoid layer and the BCELoss in one single class. class_weight import compute_class_weight generator_train = datagenerator_train. But as far as I know, the weight in nn. Feel free to read the whole document, or just skip to the code you need for a desired use case. Aug 19, 2024 · 标题：掌握 PyTorch 的加权随机采样： WeightedRandomSampler 全解析 在 机器学习 领域，数据不平衡是常见问题，特别是在分类任务中。PyTorch提供了一个强大的工具 torch. Aug 16, 2018 · The list with [0, 1, 0] is not a list of classes but just a list of values for a single class. In this article, we will explore various techniques to handle class imbalance in PyTorch, ensuring your models are robust and generalize well across all classes. Currently, I have a list of class labels that are [0, 1, 2, 3, 4, 5, 6, 7, 8 Aug 30, 2022 · Now, we simply need to assign the appropriate weight to each sample based on its class. If the field size_average is set to False, the losses are instead summed for each minibatch. The weight parameter is a 1D Tensor that contains the weight of each class. Tensor - A multi-dimensional array with support for autograd operations like backward(). e. With replacement=True, each sample can be picked in each draw again. Tensor a (float) – the lower bound of the uniform distribution b (float) – the upper bound of the uniform distribution generator (Optional[Generator from pytorch_quantization import tensor_quant # Generate random input. I leave it for any users who want to use or develop this feature further. Is there a way to sample the weight tensor using TensorDataset along with input and output batch samples? Aug 28, 2020 · label for “fish” than is “reptile”? Having said all that, you can write your own MSE loss that takes sample weights. Have a look at this example. This is achieved by providing a wrapper around PyTorch that has an sklearn interface. tensor ( [1, 3]). To start off, lets assume you have a dataset with images grouped in folders based on their class. Aug 24, 2022 · To use weighted random sampler in PyTorch we need to compute samples weight. I have a small ResNet with about 400,000 parameters (weights and biases). Explore parameter tuning, real-world applications, and performance comparison for deep learning models Aug 1, 2021 · When we deal with imbalanced training data (there are more negative samples and less positive samples), usually pos_weight parameter will be used. size_average (bool, optional) – Deprecated (see reduction). 8961442786069652 expected weighted data sample ratio Jun 11, 2019 · torch. Now, we simply need to assign the appropriate weight to each sample based on its class. What is behind the weight parameter for F. Imagine that I have a multi-class, multi-label classification problem; my imbalanced one-hot coded dataset includes 1000 images with 4 labels Mar 20, 2021 · I am using Python 3. BCELoss has a weight attribute, however I don’t quite get it as this weight parameter is a constructor parameter and it is not updated depending on the batch of data being computed, therefore it doesn’t achieve what I need. when there are millions of classes. Jun 2, 2021 · Problem I am training a deep learning model in PyTorch for binary classification, and I have a dataset containing unbalanced class proportions. from torch Sep 3, 2020 · Hi all, from my understanding the weight parameter in CrossEntropyLoss is behaving different for mean reduction and other reductions. As an example, I have defined a LeNet-300-100 fully-connected neural network to trai If you have a single sample, just use input. Mar 10, 2018 · I just wanted to ask how the mechanism of passing the weights to CrossEntropyLoss works. In practice, we can do this directly from the class counts, as demonstrated below: Models and pre-trained weights The torchvision. WeightedRandomSampler，专门用于处理这种情况。本文将详细介绍如何在PyTorch中使用 WeightedRandomSampler 进行加权随机采样，以提高模型对 Pytorch中使用样本权重 (sample_weight)的正确方式，代码先锋网，一个为软件开发程序员提供代码片段和技术文章聚合的网站。 A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc. If you want to support the fit() arguments sample_weight and class_weight, you'd simply do the following: Unpack sample_weight from the data argument May 5, 2017 · Hi all, I’m trying to find a way to make a balanced sampling using ImageFolder and DataLoader with a imbalanced dataset. The number of drawn samples is defined by the num_samples argument. Dec 27, 2023 · In this comprehensive guide, I will walk through exactly how to save PyTorch deep learning models to disk and reload them for continued training, transfer learning, and inference deployment. Module. 8796, 0. If you are familiar with sklearn and Apr 30, 2020 · Use torch. So if class weights are [1. weight. 8 and PyTorch 1. g. I’ve read all the relevant discussions in this regard, to my knowledge; however, still, I’ve not understood them completely. Note that for some losses, there are multiple elements per sample. May 9, 2018 · However, the output of cross entropy loss is always 1. Choosing the proper weight for your model is an important component in designing an efficient DNN. Oct 16, 2023 · So if i want to set the weight of different samples when calculating the loss, which has the lenth of the number of samples, what should i do? ps: just like "sample_weight" in keras. My minority class makes up about 10% of the given Jun 5, 2020 · The weights tensor should contain a weight for each sample, not the class weights. r. distributions # Created On: Oct 19, 2017 | Last Updated On: Jun 13, 2025 The distributions package contains parameterizable probability distributions and sampling functions. Jun 27, 2023 · Supporting sample_weight & class_weight You may have noticed that our first basic example didn't make any mention of sample weighting. WeightedRandomSampler () where i sample with probability (weights) Use torch. - examples/mnist/main. In this article, we'll think through the core idea of the Apr 30, 2021 · Importance of weight initialization in Deep Learning Initializing model weights is important in deep learning. functional Pruning acts by removing weight from the parameters and replacing it with a new parameter called weight_orig (i. I. 0. 0, b=1. qa4 hov xb7yz 4m e06pglf nkh nn14 ecn70a kcmy 6ndo