Pytorch combine two parameters. model = models.

Pytorch combine two parameters. This guide will explore the various methods and best practices for using multiple dataloaders in PyTorch Lightning, covering everything from basic setup to advanced configurations. I’d like to make a combined model that than take in an instance of each of the types of data, runs them through each of the models that was pre-trained individually, and then has a few feed-forward layers at the top that process the combined result of the two individual models. combinations(input: Tensor, r: int = 2, with_replacement: bool = False) → seq # Compute combinations of length r r of the given tensor. In particular, after a certain number of epochs, I want to combine all but the last 4 layers of the models and average them (In this case all layers start without the name “model. Jun 25, 2022 · 0 A nn. But I am not sure how to get embeddings from two layers and concatenate them in a fast way. The two questions that I end up having are: Can I add parameters to a parameter group in an optimizer? Can I merge two parameter groups that use the same learning rate? Do we suffer (a lot) in performance if our model has one parameter group per parameter? This questions come from the Dec 12, 2017 · If there are more than two optimizers, we will have many opt. I’m not sure if the method I used to combine layers is correct. It’s a bit more efficient, skips quite some computation. 2 Likes hughperkins (Hugh Perkins) July 14, 2017, 8:50am 2 Sep 29, 2024 · I’m trying to share a single parameter between two modules used in a nn. Combine two model on pytorch? Oct 15, 2021 · I have two trained neural networks (NNs) that I want to combine to create a new neural network (with the same structure) but whose weights are a combination of the previous two neural networks’ weights. py, that can be used to merge two PyTorch model . Module inside the parent or This tutorial uses a simple example to demonstrate how you can combine DistributedDataParallel (DDP) with the Distributed RPC framework to combine distributed data parallelism with distributed model parallelism to train a simple model. cat will concatenate only along single axis. BCELoss and the other for the nn. r (int Dec 8, 2022 · Hi, I am trying to create a combined optimizer to train multiple neural networks simultaneously. To achieve this, I split the output of the EfficientNet, which has 1280 classes, into two dense layers with 320 labels each. The goal is to merge models this way: m = alpha * n + (1 - alpha) * o where m n and o are instances of the same class but trained differently. parameters(), lr=0. parameters())” to optimize a model, but how can I optimize multi model in one optimizer? May 9, 2021 · Hence my question is how can I combine two or more lightning modules in a single module and save its hyperparameters? Or is there any alternative way to do so? Thanks in advance! EDIT: Code updated to show both modelA and modelB are pretrained. Fusing Convolution and Batch Norm using Custom Function # Created On: Jul 22, 2021 | Last Updated: Apr 18, 2023 | Last Verified: Nov 05, 2024 Fusing adjacent convolution and batch norm layers together is typically an inference-time optimization to improve run-time. Parameters input (Tensor) – 1D vector. parameters() + model2. MrPositron (Nauryzbay K) January 11, 2019, 7:50am 7 [SOLVED] Dec 9, 2020 · I've two networks, which I need to concatenate for my full model. Nov 13, 2018 · ptrblck November 13, 2018, 1:31pm 2 I assume you have two different outputs in your model, i. t Nov 24, 2018 · oasjd7 (oasjd7) November 24, 2018, 1:21pm 1 I don’ know how to append model. It doesn’t give me any error, but doesn’t do any training either. But, this combined optimizer is updating the weights of networks that have not been used in computing a given loss, which I think is not supposed to happen. The script averages the parameter values of the models for keys that exist in both models. Dec 12, 2017 · If there are more than two optimizers, we will have many opt. Oct 5, 2018 · Hello, I have a dataset composed of labels,features,adjacency matrices, laplacian graphs in numpy format. Feb 12, 2021 · I have tried using pre_model. Conv2d (in_channels=1, out Mar 7, 2023 · MikeTensor March 7, 2023, 6:18pm 1 the setup1377×597 175 KB Source | Paper ModelA: nn. utils. 001, momentum=0. You can have a single comprehension instead of creating multiple levels. optim. Adam(list(net. This can be useful when you need to combine the weights of two models that have the same architecture and are compatible. The model one is a trained NN which I have already saved as a . How can make it clear? Sep 20, 2021 · I’m trying to generate one model’s parameters (ActualModel) with another model (ParameterModel), but running into problems with autograd when I backpropagate multiple times. cat() function in PyTorch concatenates two or more tensors along a specified dimension. Thanks How to merge two torch. step() However, the performance of the model is not good. combinations when with_replacement is set to False, and itertools. Then I combining those two models and train them together. To do that, I plan to use a standard CNN model, take one of its last FC layers, concatenate it with the additional input data and add FC layers processing both inputs. Mar 4, 2017 · Probably you set a bracket to the wrong place. The two questions that I end up having are: Can I add parameters to a parameter group in an optimizer? Can I merge two parameter groups that use the same learning rate? Do we suffer (a lot) in performance if our model has one parameter group per parameter? This questions come from the torch. I’m working on autoencoder and I want to : -calculate the loss from the output and the input -calculate another loss ( KL Divergence) from one of my hidden layer to a arbitrary parameter . Then combine these features (for example, concatenate these features) and to pass it through an MLP to predict the target variables. Is there a way to keep all those parameters and everything around, and export this one even larger combined ONNX model? Jul 8, 2023 · I am trying to estimate two parameters, such as the length and angle of an object, from a given image using an EfficientNet. I’ve implemented the following snippet: import torch list = nn. item() Jun 13, 2022 · Merge datasets together: optionally, PyTorch also allows you to merge multiple datasets together. ParameterList is supposed to hold a single list of nn. weight of each of the 10 models to produce a big weight of shape [10, 784, 128]. Mar 5, 2022 · I'm currently working on two models that use different types of data but are connected. ParameterList can be used like a regular Python list, but Tensors that are Parameter are properly registered, and will be visible by all Module methods. So far, I know Jun 1, 2017 · I know we can use “optimizer = optim. How can I connect two models? I mean simply Aug 25, 2017 · How does one combine network parameters from two different networks? Suppose I have two (could be more but let’s do two) distinct networks model1 and model2. How should this problem be solved in PyTorch? Jul 13, 2023 · However, when I look at the state_dict of the combined model, I think it contains all the parameters and I don’t need to save each models state dict separately. ModelB: nn. Linear(10,10) params = nn. Feb 28, 2023 · The issue doesn't seem to originate from the nn. randn((len(self. The problem is not that one (combining non-linear things not always works), but instead that when Feb 10, 2020 · Hi! I am trying to merge two pretrained ResNet models. data. . Oct 12, 2019 · To illustrate the equivalence, this example combines two kernels with 900 and 5000 parameters respectively into an equivalent kernel of 28 parameters: # Create 2 conv. This repository contains a script, py_merge. Then I proceed and p Dec 24, 2020 · I want to concatenate two layers of convolution class Net (nn. Jun 29, 2018 · I want to build a CNN model that takes additional input data besides the image at a certain layer. Module successfully: self. The two NNs have an accuracy of ~97%, but when I combine them I obtain a value of around 47%. pth file. Feb 18, 2023 · I’m training two UNet MONAI models with the same architecture while combining their parameters while training. CrossEntropyLoss? Now one part of your model learn quite good, while the other gets stuck? A weighting of these losses might be a good idea. steps Maybe it’s good to code some wrapper for optimizers, which will update different model parameters with different optimizers, as we do it in case with different learning rates and etc for different model parameters using one optimizer. I then try to ensemble the two models as shown in the diagram below: The feature map after concatenation and 1x1conv layer fully has the same dimensions as the original input to the final convolutional layer Apr 7, 2021 · I want to add two separate layers on the top of one layer (or a pre-trained model) Is that possible for me to do using Pytorch? Sep 13, 2019 · What is the correct way of sharing weights between two layers (modules) in Pytorch? Based on my findings in the Pytorch discussion forum, there are several ways for doing this. Jul 20, 2025 · Understanding how to add trainable parameters to PyTorch models is essential for building custom neural network architectures, implementing novel algorithms, and fine - tuning existing models. backward() What if I want to learn the weight1 and weight2 during the training process? Should they be declared parameters of the two models? Or of a third one? Multi-Input Deep Neural Networks with PyTorch-Lightning - Combine Image and Tabular Data One of the most significant advantages of artificial deep neural networks has always been that they can pretty much take any kind of data as input and can approximate a non-linear function to predict on that data. I am having two questions: 1) How to combine the two models?2) How should the distance_regressor look like together with the loss_dist_fn Oct 12, 2019 · To illustrate the equivalence, this example combines two kernels with 900 and 5000 parameters respectively into an equivalent kernel of 28 parameters: # Create 2 conv. bias_para = nn. The problem is not that one (combining non-linear things not always works), but instead that when Apr 7, 2021 · I want to add two separate layers on the top of one layer (or a pre-trained model) Is that possible for me to do using Pytorch? Sep 13, 2019 · What is the correct way of sharing weights between two layers (modules) in Pytorch? Based on my findings in the Pytorch discussion forum, there are several ways for doing this. What is Tensor Concatenation? Concatenation refers to joining two or more tensors (multidimensional arrays) together. I am facing the following problem and I want to solve it using the best possible option in pytorch. Source code of the example can be found here. It is usually achieved by eliminating the batch norm layer entirely and updating the weight and bias of the preceding convolution Sep 30, 2021 · I created two separate models, an encoder and a decoder. func. SmoothL1Loss (reduction='mean') → Is used for regression. Any suggestion to merge and copy conv layer parameters to a separate network. That parameter will get Oct 25, 2018 · mse_loss = nn. zero_grad() loss. This process is crucial for numerous applications such as merging outputs from different neural network layers or creating complex inputs for stacking processed data. MultiheadAttention in PyTorch, exploring its parameters, usage, and practical examples. parameters to optimizer when some condition is ok. Mar 13, 2019 · I am reproducing the paper " Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics". SGD(model1. Module will contain all registered parameters and buffer from all registered submodules. Module): def __init__ (self): super (Net,self). __init__() self. model = models. DataLoader() that can take labels,features,adjacency matrices, laplacian graphs. This method accepts the sequence of tensors and dimension (along that the concatenation is to be done) as input parameters. fc1. A solution is to convert the generator to list: model = nn. Load data directly on CUDA tensors: because PyTorch can run on the GPU, you can load the data directly onto the CUDA before they’re returned. These models only share the same input and output only. Jan 31, 2018 · Hi, I have 2 trained models, which has different number of channels in inputs and I would like to merge these network parameters. CrossEntropyLoss () → Is used for Classification. ParameterList() for i in sub_list_1: list. Is there a way to keep all those parameters and everything around, and export this one even larger combined ONNX model? Aug 17, 2018 · how a combined loss function like the following can be implemented? Loss = loss1 * exp(-w1) + w1 + loss2 * exp(-w2) + w2 where w1 and w2 are also trainable parameters. data_utils. We are the weights of the network while σ are used to calculate the weights of each task loss and also to regularize this task loss wight. Jul 25, 2025 · This blog post will delve into the fundamental concepts, usage methods, common practices, and best practices for optimizing parameters of multiple models in PyTorch. I am a new-bee to PyTorch. This is how I define a simple network with just one weight and one bias (one linear layer). Is anything wrong with this model definition, how to debug this? N Feb 5, 2017 · Does torch. 模型集成 # 创建于: 2023 年 3 月 15 日 | 最后更新: 2025 年 10 月 2 日 | 最后验证: 2024 年 11 月 5 日 本教程演示了如何使用 torch. I share a simple reproducible example below. Two different loss functions If you have two different loss functions, finish the forwards for both of them separately, and then finally you can do (loss1 + loss2). cat(). concatenate(tensors, axis=0, out=None) → Tensor # Alias of torch. optim as optim optimizer = optim. 0 I want to use Pytorch for its flexibility and it’s proximity to python. In given network instead of convnet I’ve used pretrained VGG16 model. You have to convert the parameters to a list separately and add the lists afterwards. In this blog, we’ll explore how to apply weight averaging using PyTorch, validate its effectiveness, and demonstrate its relevance in solving a real-world constraint: This repository contains a script, py_merge. listener = listener self. speller = speller Jun 13, 2019 · How would you like to combine the parameters of the two models? Could you give an example of the use case you are thinking about? First, let’s combine the states of the model together by stacking each parameter. weight has shape [784, 128]; we are going to stack the . append(i) for i in sub_list_2: list. e. back ( [l1,l2]) mean this two loss backward for separate node? such as softmaxloss updates fc2 and layers before fc2, custom_loss updates fc1 and layers before fc1? Mar 15, 2021 · Hello, all. Dec 26, 2021 · As the two source layers are Embedding layers, I do not see as optimal that they would share the same dimension. Model1 =>Conv layer parameters [N1,C1,H,W] Model2 =>Conv layer parameters [N2,C2,H,W] Final Model => [N1+N2,C1+C2,H,W] As torch. combinations # torch. 9 will be used for all parameters. data as data_utils # get the numpy data Jul 23, 2025 · When working with complex machine learning models in PyTorch, especially those involving multi-task learning or models with multiple objectives, it is often necessary to handle multiple loss functions. I found 2 different posts (Merging two models & Combining Trained Models in PyTorch - #2 by ptrblck) and noticed that they are different. cat () method. cnn1 = nn. Extra tip: Sum the loss In your code you want to do: loss_sum += loss. I was wondering how to define such groups that have parameters() attribute. Oct 30, 2018 · Hi all, I’m currently working on two models that train on separate (but related) types of data. Hello community , coming from TF 2. I don’t know what’s wrong, please help me, thank you. Conv2d (in_channels=1, out ParameterList # class torch. The tensors must have the same shape in all dimensions except for the dimension along which they are concatenated. In this example, using an embedding dimension of 5 for a vocabulary of 50 items, and an embedding dimension of size 20 for a vocabulary of 200 items. I want to do something like this: import torch. Adam(model1. TensorDataset() and torch. combinations_with_replacement when with_replacement is set to True. Each of them will define a separate parameter group, and should contain a params key, containing a list of parameters belonging to it. node as nodes in a new graph, but the parameters exported from pytorch are lost this way. Note that the constructor, assigning an element of the list, the append() method and the extend() method will convert any Tensor into Parameter Aug 30, 2021 · Some people suggested using two separate embedding layers: one for trainable embeddings and another for the freezing embedding. autograd. paramters (). By the end of this guide, you‘ll have a deep understanding of tensor concatenation and be able to use cat() like a pro. PyTorch offers the torch. data dataloaders with a single operation Asked 4 years, 9 months ago Modified 6 months ago Viewed 17k times Leveraging trained parameters, even if only a few are usable, will help to warmstart the training process and hopefully help your model converge much faster than training from scratch. ParameterList(values=None) [source] # Holds parameters in a list. Previous tutorials, Getting Started With Distributed Data Parallel and Getting Started with Distributed RPC Jul 20, 2022 · optimizer. Could you compare the ranges of both losses and try to rescale them to a similar range? Jul 23, 2025 · This technique, known as multi-head attention, is a cornerstone of transformer models and has been widely adopted in various natural language processing (NLP) and computer vision tasks. graph. So for each parameter in these models, I want to assign initial values to m based on n and o as described in the equation and then continue the training procedure with m only. rand(10)) optimizer = torch. Each module uses the parameter differently. backward(). Sequential. The code I need would be something like: additional_data_dim = 100 output_classes = 2 model = models. Dec 7, 2022 · How to combine two pytorch neworks into one - specifically just adding a sequential end to the end of a conv network? Slowat_Kela (Slowat Kela) December 7, 2022, 11:53am 1 Jul 20, 2022 · optimizer. Nov 15, 2024 · The . ParameterList s. I'd want to create a combination model that takes in one instance of each of the data types, runs them through each of the pre-trained models independently, and then processes the combined output of the two distinct models through a few feed-forward layers Dec 28, 2022 · I also tried to use one optimizer with some optimization parameters, and changing the learning rates of the parameters that I want to remain static at certain times to 0. Nov 24, 2018 · oasjd7 (oasjd7) November 24, 2018, 1:21pm 1 I don’ know how to append model. Model ensembling combines the predictions from multiple models together. The reason why I want to keep them separate is because I want to attach different decoders in my experiment later. In Keras I couldn’t do that , even with a custom function In resume ,I want something like Jul 20, 2025 · Understanding how to add trainable parameters to PyTorch models is essential for building custom neural network architectures, implementing novel algorithms, and fine - tuning existing models. kernels May 3, 2018 · I have two nets and I combine their parameters in some fancy way using only pytorch operations. concat(tensors, dim=0, *, out=None) → Tensor # Alias of torch. one using the nn. May 2, 2018 · Say I have two nets and I combine their parameters in some fancy way using only pytorch operations. I store the result in a third net which has its parameters set to non-trainable. There is no different in using a custom nn. I am trying to combine these models to predict the same output “y”. Jun 18, 2019 · Hello, I am new in Pytorch and this question makes me waste a couple of days. In this blog post, we will explore the fundamental concepts, usage methods, common practices, and best practices for adding trainable parameters to PyTorch. For example, model[i]. What came to my mind was something in the form of Apr 16, 2020 · I have two models trained exactly the same way just with a different learning rate now I would like to average each weight of every single layer in the model an create a new one with the weight averages. Parameter and cannot contain other nn. node +Identity connector+ main_model. vgg16 (pre… Dec 14, 2024 · One of the essential operations in PyTorch is concatenation, allowing developers to join multiple tensors into a single one. Apr 11, 2019 · Just to add an answer to the title of your question: "How does one dynamically add new parameters to optimizers in Pytorch?" You can append params at any time to the optimizer: Jan 4, 2019 · I’m trying to implement the following network in pytorch. Here I have a simple idea but I do not know how to implement it with PyTorch. To do so, l have tried the following import numpy as np import torch. bin files into a single model file. from Oct 24, 2022 · In PyTorch, to concatenate tensors along a given dimension, we use torch. 9) An Oct 29, 2021 · This means that model. Jun 23, 2018 · Let's call the function I'm looking for "magic_combine", which can combine the continuous dimensions of tensor I give to it. Then I want to put another NN with a totally different architecture after it. I just want to add model2. append(i) Is there any functions that takes care of this without a need to loop over each list? Jan 1, 2019 · 7 Third attempt is the best. classifier ’s parameters will use a learning rate of 1e-3, and a momentum of 0. e. The loss function is defined as This means that W and σ are the learned parameters of the network. g. The question is about how to mix model parameters on the fly, to do something like weighted network… Jul 14, 2017 · To what extent is having the embedding parameters passed to the optimizer multiple times ok/ not ok? Any better way to handle this, in idiomatic pytorch? I want to avoid allocating the embedding multiple times, then deallocating the ones we dont need, ideally. Can someone confirm my understanding That’s correct and the parent nn. The behavior is similar to python’s itertools. For more specific, I want it to do the following thing: a = torch. One dense layer is for the angle and the other for the length. __dict__['inception_v3'] del Jul 23, 2025 · PyTorch Lightning provides a streamlined interface for managing multiple dataloaders, which is essential for handling complex datasets and training scenarios. Traditionally this is done by running each model on some inputs separately and then combining the predictions. I checked the parameters of the model before and after training, and found that after training, the model parameters did not change, and grad_value is None. Parameter(torch. MSELoss(size_average=True) a = weight1 * mse_loss(inp, target1) b = weight2 * mse_loss(inp, target2) loss = a + b loss. ResNet1 is trained on one set of images of input size 160x160 and ResNet2 is trained on another set of images of size (1280,1280). Other keys should match the keyword arguments accepted by the optimizers, and will be Mar 20, 2022 · Greetings, I have 2 different models - A (GNN) and B (LSTM). I wonder which one would everyone recommend to follow. base ’s parameters will use the default learning rate of 1e-2, model. parameters()) + [params], lr=1) Jul 14, 2019 · I am trying to concatenate embedding layer with other features. nn. so now I have wrote like this but not fancy. Module): def __init__(self, listener, speller): super(LAS, self). 2” ). May 26, 2020 · Pytorch combine two models into a single class and access their parameters from the parent class jiwidi (Jaime Ferrando Huertas) May 26, 2020, 7:47pm 1 Hi! So I have a model in pytorch that looks like this: class LAS(nn. This […] Jun 13, 2025 · Per-parameter options # Optimizer s also support specifying per-parameter options. The following code will register all those parameters on your nn. Oct 29, 2024 · Utilize PyTorch JIT for Speed: PyTorch JIT compilation can fuse multiple concatenation operations with other layers in your model, providing substantial performance gains. ParameterList([nn. __init__ () self. concat # torch. stack_module_state convenience function to do this. torch. Parameter but from the fact that you are passing a list containing a parameter generator (in 1st position). In this article, we'll delve into the details of how to use nn. It is easy to implement the L1 and L2 Jul 3, 2020 · When we pass a list of parameters or parameter groups to an optimizer, and one parameter appears multiple times we get different behaviours, and it is not clear whether this is intended that way: If the parameter appears twice within one parameter group, everything works. While this may not be a common task, having it available to you is an a great feature. Jan 20, 2022 · I am trying to combine two ParameterLists in Pytorch. I am trying to connect two different neural networks together. For example, imagine a function, parametrized by w: F (x) = x+w Let’s say I’m trying to fin… Oct 30, 2023 · Welcome! As a PyTorch expert, I‘m excited to provide you with this comprehensive guide to torch. I would like to build a torch. About defining Aug 17, 2018 · how a combined loss function like the following can be implemented? Loss = loss1 * exp(-w1) + w1 + loss2 * exp(-w2) + w2 where w1 and w2 are also trainable parameters. backward() optimizer. concatenate # torch. zer May 16, 2022 · One of the ways to “combine” is to perform ensembling of these models as explained below: Have a two-branch architecture with these 2 resnet18 models and get features from each branch (any layer of choice). To do this, instead of passing an iterable of Variable s, pass in an iterable of dict s. I would like the optimizer to be aware of and optimize both models simultaneously. vmap 实现模型集成的向量化。 什么是模型集成？ # 模型集成是将多个模型的预测结果组合在一起。传统上，这是通过分别对每个模型运行一些输入，然后组合预测结果来实现的 Fusing Convolution and Batch Norm using Custom Function # Created On: Jul 22, 2021 | Last Updated: Apr 18, 2023 | Last Verified: Nov 05, 2024 Fusing adjacent convolution and batch norm layers together is typically an inference-time optimization to improve run-time. This article will guide you through the process of managing and combining multiple loss functions in PyTorch, providing insights into best practices and implementation strategies. May 4, 2020 · Hello. However my first model is pre-trained and I need to make it non-trainable when training the full model. tzf hej kmnjzhx ivpv b23f lvptqwu vugcw hh ncaef k8hc