Pytorch concat two model I would like to predict by observation. Using word embeddings, let’s say each token is mapped to 100D embeddings. Code for processing data samples can get messy and hard to maintain; we ideally want our dataset code to be decoupled from our model training code for better readability and modularity. concat(tensors, dim=0, *, out=None) → Tensor # Alias of torch. Jun 24, 2021 · What is the difference between concatenating two image and passing it through resnet network ,while passing both images individually to the network. You can concatenate these there and pass them through the rest of the model. The input data is two types: customer review or agent reply. Tensors are similar to NumPy’s ndarrays, except that tensors can run on GPUs or other hardware accelerators. Is something similar possible by stacking torch’s Sequential models and if so, how? Apr 23, 2019 · The second is to first use fully connected layers to make the two features of the same length, and then concatenate the vectors and make the prediction. cat` is a fundamental and powerful function for concatenating tensors. I understand I can use ConcatDataset to combine datasets first, but this does not work for my use case. In PyTorch, we use tensors to encode the inputs and outputs of a model, as well as the model’s parameters. Traditionally this is done by running each model on some inputs separately and then combining the predictions Jun 18, 2019 · Hello, I am new in Pytorch and this question makes me waste a couple of days. I will then concat the output of the single channel stem at some later point in the network. Nov 14, 2025 · One such important operation is `torch. How can I concat these two models when th Apr 13, 2022 · You can just concatenate the two images on the last axis. One of the common operations when working with tensors (which can be thought of as multi - dimensional matrices in PyTorch) is the concatenation of two matrices. concatenate on them. Then I want to do ensemble learning to combine torch. 1 day ago · Conclusion In this tutorial, we built a two-input PyTorch model that combines CNN and FC layers to process image and tabular data. This blog post will provide a detailed overview of PyTorch's concat bidirectional LSTM, including fundamental concepts, usage methods, common practices, and best practices. The singular first dimension can be added with torch. I am planning to use two separate pre-trained Resnet18’s to for images from each source. Oct 2, 2017 · Hi; I have images taken from two separate cameras (camera1 and camera2). cat () function using different examples. parameters())” to optimize a model, but how can I optimize multi model in one optimizer? Mar 19, 2017 · What is the recommended approach to combine two instances from torch. Jun 19, 2025 · Learn how to effectively use PyTorch's torch. I can’t find what exact term to describe the technique I am trying, but basically I want to try three versions: Version 1: take the customer Feb 24, 2021 · How to efficiently concatenate two tensors during an training phase samsja (Samsja) February 24, 2021, 2:02pm 1 Jan 14, 2019 · Hi, I’m wondering if there is any alternative concatenation method that concatenate two tensor without memory copying? Currently, I use t = torch. add (x, y) and z = torch. Aug 9, 2022 · Hey all, I am working on a Model in which a layer takes two inputs. split() and torch. The combining of at least two informational indexes into a single informational collection through concatenation is known as concatenate dataset collections. The concatenation might be applied e. Now if I also want to use other features, like part-of-speech, do I simply concatenate them and have 101D inputs? Doesn’t this diminish the effect of the POS tags? Also, the embeddings are trainable and can be learned during training Sep 18, 2019 · yes, the problem appears during `load_state_dict (), I have looked at the structure of my model instantiation and the model constructor below from the model class and cannot seem to notice the issue Jul 20, 2022 · 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. Apr 17, 2021 · You should be able to create a pytorch model with each of the huggingface models initialized as layers of the model. chunk(). pt files) with the following commands: model_c = torch… Apr 7, 2023 · Guide to PyTorch concatenate. Jul 30, 2019 · How? Concat weights from two pre-trained models Ensemble two features How to organize the dataset for multi-label, multi-model classification Fusion/ensembel of two MLP models Understanding nn. In given network instead of convnet I’ve used pretrained VGG16 model. What is Torch Concatenate? Torch concatenate is a powerful function in the Torch library that allows you to concatenate tensors along a specified dimension. I want to concatenate the two output of these layers and pass it to the next layer. stack() Nov 15, 2024 · The . Here we discuss Definition, overviews, How to use PyTorch concatenate? examples with code implementation. Then, we converted the two parameter lists into regular Python lists using list () and concatenated them with the + operator. Then combine these features (for example, concatenate these features) and to pass Nov 14, 2025 · In the field of deep learning, efficient tensor manipulation is crucial for building and training models. Tensor 1 has dimensions (15, 200, 2048) and Tensor 2 has dimensions (1, 200, 2048). This approach is similar to b (same problems) - but I want to make sure that it makes sense. cat`, from its basic Jun 13, 2025 · torch. My goal is to crop an image using a binary mask from the segmentation model and then use the cropped image for classification. The model one is a trained NN which I have already saved as a . concatenate ( [x, y]) in keras? Model ensembling # Created On: Mar 15, 2023 | Last Updated: Oct 02, 2025 | Last Verified: Nov 05, 2024 This tutorial illustrates how to vectorize model ensembling using torch. Sequential(stuff) block2 = nn. Thank you. 1 It is possible to create data_loaders seperately and train on them sequentially: f To use the model, we pass it the input data. ) I’ve tried to combine the two using torch. Aug 27, 2018 · I know that PyTorch is a great one but I am not sure it fits with my project or not. I am trying to make an ensemble model composed of two pre-trained models, using torch, in order to classify an image. Dataset? I came up with two ideas: Wrapper-Dataset: class Concat (Dataset): def __init__ (self, datasets): self. CIFAR10 in the snippet below to form I have a model that's quite complicated and I therefore can't just call self. cat (x, y) in pytorch same as z = keras. Sep 13, 2020 · Hi, I’m trying to implement an autoregressive model which is a variant of memory-augmented neural network. Given the customer review is more important and is already exceed 512 limitation, I don’t want to concatenate two different tex input together. data # Created On: Jun 13, 2025 | Last Updated On: Jun 13, 2025 At the heart of PyTorch data loading utility is the torch. Oct 17, 2021 · How I can feed two pytorch models with different data, then concatenate the output of the two models before prediction. Learn how to avoid common errors and optimize performance. This […] Apr 20, 2021 · Hello, I have make two training on image classification. Sequential (block1,block2) ?? <- like this? Assume that block1 can feed directly into block2. Then I want to put another NN with a totally different architecture after it. I tried using concatenate datasets… Dec 14, 2024 · The examples above show how to concatenate two matrices, either by extending the number of rows or the number of columns. Sep 11, 2020 · Let’s say I have two GPUs with 4GB VRAM and I have a 6GB model. Conv2d (in_channels=1, out Mar 20, 2022 · The linked use cases are a bit different as the first one (merging two models) seems to pass features from one model to the other and could train these models in an end2end manner. Feb 17, 2019 · Basically, in other words, I want to concatenate the first 3 dimensions of data with fake to give a 4-dimensional tensor. This flexibility is beneficial when dealing with image data, batch processing, or sequencing models. So I want to train the model in A and B and save the model in checkpoint1 and checkpoint2. I want to create a third model by removing the classification layers from the pre-trained models and freezing all the other layers, then concatenating the features from these two models and finally adding a new classification head [fine-tune this layer only]. I don’t know what’s wrong, please help me, thank you. How can I connect two models? I mean simply Oct 30, 2023 · Welcome! As a PyTorch expert, I‘m excited to provide you with this comprehensive guide to torch. We get the prediction Oct 30, 2018 · Hi all, I’m currently working on two models that train on separate (but related) types of data. PyTorch, one of the most popular deep learning frameworks, provides a `torch. Nov 15, 2019 · I have two image classification models that have multiple images per observation. What is model ensembling? # Model ensembling combines the predictions from multiple models together. Please let me know if there is any possible example or method. Jan 22, 2018 · Afterwards I try to concatenate or stack the vectors a to g back to one array, but it shows me this error: 1 day ago · Conclusion In this tutorial, we built a two-input PyTorch model that combines CNN and FC layers to process image and tabular data. To make it more clear I simplified the case, and presented it on the graph: In Tensorflow I just have three tf. Concatenation is the process of joining multiple tensors along a specified dimension. It represents a Python iterable over a dataset, with support for map-style and iterable-style datasets, customizing data loading order, automatic batching, single- and multi-process data loading, automatic memory pinning. Then in the forward function for the pytorch model, pass the inputs through self. So I’m going to end up with 3 models. It takes 21 values and returns 11. First training on type of glass Second on type of clothes So I have two files: glass. This operation is helpful in combining data with the same number of rows but differing in the number of columns. datasets. Apr 6, 2022 · I'm interested in how I'd go about combining multiple DataLoaders sequentially for training. cat work with backpropagation? Nov 1, 2021 · I am training a GANS on the Cifar-10 dataset in PyTorch (and hence don't need train/val/test splits), and I want to be able to combine the torchvision. Now I want to ensemble this two model. Apr 3, 2023 · Thank you ptrblck April 4, 2023, 12:19am 2 youb: images are fed into VggFace (fine tuned ) model (1); the output shape is (1800,128) audios are fed into a Vggish model (2), the output shape is (900, 128) This sounds wrong as it seems you are increasing the number of samples in the output of VggFace assuming dim0 represents the batch dimension. train_loader = DataLoader(train_dataset, batch_size = 512, drop_last Feb 28, 2022 · In this article, we are going to see how to join two or more tensors in PyTorch. __init__ () self. Modules, such as a GAN, a sequence-to-sequence model, or an ensemble of models, you must save a dictionary of each model’s state_dict When does this apply ? Apr 2, 2019 · Hi All, I have a question on combining model weights accurately. Feb 21, 2017 · Suppose I have two training dataset with different size and I am trying to train it on a network simultaneously, So I can do it? also, I need to keep a track of from which dataset image is coming to find out the loss after each iteration by the equation: Oct 24, 2022 · In that context, PyTorch’s system can be used to model digital neural networks or use predictive logic to determine the location of physical objects. How can I implement this in Pytorch Lightning ? Are there already supports for this ? Here is an example of Two-input model: With the data ready, it's time to build the two-input model architecture! To do so, you will set up a model class with the following methods: Tensors are a specialized data structure that are very similar to arrays and matrices. resnet18(pretrained=True) my Aug 23, 2017 · If I have two nn. I finally got time to keep working again on this and am to the point where I concatenate the parameters of the models in one single list. How can I achieve this in P torch. Sep 24, 2023 · Hello, I am having two different, already trained models for video classification, where one model takes keypoints as input, and another model takes video frames as input. e. We’ll focus on a common issue: combining tensors of different dimensions, specifically the frustrating “RuntimeError: zero-dimensional tensor…” This usually happens when you try to directly concatenate a scalar (a single number) with a vector or higher-dimensional tensor using torch. 3. model_a and self. Sequential(other_stuff) block3 = nn. stack () functions. Most images are in the format of (w, h, channels) when converted to a numpy array so you can just concatenate the two arrays on the channels' axis. May 8, 2024 · In this tutorial we covered the concept of tensor concatenation in PyTorch using torch. cat(tensors, dim=0, *, out=None) → Tensor # Concatenates the given sequence of tensors in tensors in the given dimension. (The batch size of the graph network is not static as the number of nodes Mar 5, 2019 · I’m doing an image processing task and I want to use torch. DataLoader class. Does the above method work properly? Or is there another efficient way? In summary, I want to train one model using multiple data loaders, add up each loss and update it back to one model. PyTorch provides two data primitives: torch. cat() can be best understood via examples. pitch: ensembles of models are super effective - as was recently Tensors are a specialized data structure that are very similar to arrays and matrices. This blog post will guide you through the ins and outs of `torch. Key takeaways: Multi-input models enable learning from diverse data types (images, text, tabular). Apr 25, 2022 · Hello pytorch-community, in pytorch (& yolov5) i was able to train two models (one detects cars, the other detects bikes). May 16, 2022 · I want one model that have parameters/weights from both AFAIK, combining models does not work that way. e. Dec 9, 2020 · I've two networks, which I need to concatenate for my full model. Sequential blocks how can I make a new nn. Then, I would like to Sep 2, 2021 · All possible concatenations of two tensors in PyTorch Asked 4 years, 2 months ago Modified 2 years, 6 months ago Viewed 3k times Apr 7, 2020 · I defining two model which are encoder and classfication respectively. My idea of putting the two things together was because I wanted the LSTM to learn the operation patterns based also on the subj_info because, after the training, my goal would be to use the network to generate new unseen sequences given the subj_info as input. pt") Model1 is pre-trained on heart ultrasound dataset and model2 is pre-trained on abdomen ultrasound dataset. This can be done at different levels, such as concatenating feature maps in convolutional neural networks (CNNs) or concatenating hidden states in recurrent neural networks (RNNs). Concatenation allows us to combine two or more tensors into a single tensor along a specified dimension. However, to compute BCE losses at once, I concat all these 100 outputs as one tensor Jul 23, 2025 · PyTorch Lightning provides a streamlined interface for managing multiple dataloaders, which is essential for handling complex datasets and training scenarios. I’m not completely sure what the Torch7 code does, but I assume it’s repeating the tensor in the spatial dimension, which could be done in PyTorch using: Dec 7, 2022 · You are indeed right. My problem is with the method that I should apply to concatenate the encoders’ outputs. Branch design: Use CNNs for spatial data (images) and FC layers for structured data (tabular). fc. most efficient) to append a scalar value (i. Module): def __init__(self): super Mar 24, 2021 · Each model in the first box with three convolutional layers will look like this as a code, but I'm not quite sure how I can put 20 different input to separate models of same structure to eventually concatenate. Dataset that allow you to use pre-loaded datasets as well as your own data. pt") model2 = torch. Dec 30, 2019 · I have 2 similar dataset A and B . Concatenate(axis=-1, name Sep 13, 2022 · Hi, I have two pre-trained classification models. I am thinking of creating a class that will merge both of them inspired by this: Combining Trained Models in PyTorch - #2 by torch. datasets = … Dec 5, 2020 · Hi. nn Code for processing data samples can get messy and hard to maintain; we ideally want our dataset code to be decoupled from our model training code for better readability and modularity. What are different ways to combine the weights in anyone’s opinion? Thanks in advance for answering this question Jun 18, 2018 · For an LSTM model for sentence classification, I have text sequences for input. I'd like to integrate the two pre-trained models into one and use it for transfer learning. I am trying to load two datasets and use them both for training. Another solution would be converting the two ONNX models to a framework (Tensorflow or PyTorch) using tools like onnx-tensorflow or onnx2pytorch. vmap. However, I got the out-o… Nov 5, 2020 · In keras this would be solved by creating a model out of the (A) layers, creating a second model out of the (B) layers and calling keras. Is it possible to Apr 27, 2021 · Hi, Hope you are fine! So I want to concat the output of two linear layers with dynamic batch size. These Dec 26, 2021 · As the two source layers are Embedding layers, I do not see as optimal that they would share the same dimension. Code 1 for 1st scenario and code 2 for second. However my first model is pre-trained and I need to make it non-trainable when training the full model. safetensors after full finetuned Flux with Ostris AI tool kit, I guess at this point I will have to fusion/merge theses 3 parts for created the whole checkpoint usable for a1111 Forge . The first input is part of the input data, and the second input are embeddings (of other input data. . Below is some code, based on this post. 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). g. One of the major points to keep in mind when using PyTorch’s tensor is that it’s more complex and powerful than a standard Python collection. nn. This process is straightforward on my workstation, but I haven’t found useful resources on how to achieve it. The size of the images in folder 1 is 224 * 224 * 3, and the size of the images in folder 2 is 224 * 224 * 1. 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. Sequential block that is the concatenation of both of them? block1 = nn. From my understanding I can create three lstm networks and then create a class for merging those networks together. Is it possible to combine 2 sides of a frame as input of training data in PyTorch? Dec 25, 2022 · There are many use cases for running two layers or models completely in parallel: bagging small models, mixture of experts, model soups, multi-model (clip like) models, running a variety of kernel sized convolutions in parallel. After training I want to save the two model separately. Linear () function and obtained their parameters, params1 and params2. We can join tensors in PyTorch using torch. I want use encoder’s ouput as the input of the classfication then train the two model. Examples Sep 12, 2023 · For step i, I receive a batch from dataset 0, update my model. This blog post aims to provide a comprehensive Aug 6, 2018 · All pytorch examples I have found are one input go through each layer. Among these, `torch. Adam(model1. safetensors and diffusion_pytorch_model-00003-of-00003. __dict__['inception_v3'] del Jun 24, 2023 · Hi! I’m trying to move my project from Tensorflow to PyTorch and I need your help with one thing. keras. layers. I want to concatenate the I have two dataloaders and I would like to merge them without redefining the datasets, in my case train_dataset and val_dataset. As of now I have written this piece of code for the same 🙂 pretrained_model = models. FloatTensor' as child Jun 10, 2024 · I have two fine-tuned PyTorch models: one for segmentation and one for classification. In the above code, we created two models, model1 and model2, using the nn. Nov 30, 2022 · Hi! I create two encoders models to concatenate the features of two images, and then I’d like to concatenate the encoders’ output to be one input to the decoder module. forward() directly! Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of each output. Now, I would like to extract high-level features from two pre-trained segmentation network U-Net. With a input of sequence length 100, the model basically do the read and write process 100 times and give 100 outputs in order. concatenate # torch. concat(), which returns a tensor. pth I would like now merge this two files on one. The graph network has a Linear layer at the end and so does the CNN. Understanding how to effectively use the concatenation operation on batches is crucial for building efficient and scalable deep learning models. I am trying to connect two different neural networks together. That tensor however cannot be used downstream without modifications; I receive errors like TypeError: cannot assign 'torch. I have a model that process numerical data. import timm import torch from torch. so I want to iterate over the model in some way. The target variable is binary. 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. cat(). PyTorch, one of the most popular deep learning frameworks, provides a wide range of tools for tensor operations. At the moment I have the following Mar 30, 2020 · Does torch. 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. load("UNetmodel1. I tried torch. Or, you can add two input layers on your model and pass them to a Concatenate layer to combine their last axis, works either way. Let: model1 = torch. It takes as input a list of tensors, all of the same shape except for the concatenation axis, and returns a single tensor that is the concatenation of all inputs. I have Graph Convolutional Netowrk that I am combining with a CNN. I made one NN and trained the model separately on two datasets. Each folder has 100 images. Both of them I am using for feature extraction and want to concatenate their outputs at the end and put them into LSTM. cnn1 = nn. So far, I know Jul 8, 2022 · Given two datasets of length 8000 and 1480 and their corresponding train and validation loaders,I would like o create a new dataloader that allows me to iterate through those loaders. pitch: ensembles of models are super effective - as was recently Aug 29, 2024 · Hi there, I got diffusion_pytorch_model-00001-of-00003. stack but this requires the dimen… Oct 11, 2020 · pytorch concat layers with sequential Asked 5 years, 1 month ago Modified 5 years, 1 month ago Viewed 2k times Nov 14, 2025 · In PyTorch, implementing a concatenated BiLSTM is a powerful technique for tasks such as natural language processing (NLP), speech recognition, and time - series analysis. The tensors must have the same shape in all dimensions except for the dimension along which they are concatenated. Nov 14, 2025 · In the world of deep learning and numerical computing, PyTorch has emerged as a powerful and widely - used library. here’… Jul 1, 2023 · Discover the definition, , and use cases of torch concatenate in this beginner’s guide to deep learning. I’ve included the code and ideas below and found that they have similar accuracy. tensor with empty size) to a tensor with multidimensional shape. This executes the model’s forward, along with some background operations. Jun 5, 2020 · Is z = torch. model_b to get logits from both. What might be the best way to do this? Jul 13, 2023 · Thanks @ptrblck ! Is the advice in this tutorial for a different scenario ? Quoting When saving a model comprised of multiple torch. Nov 14, 2025 · Model concatenation in PyTorch refers to the process of combining the outputs of two or more neural network models. Package versions: python 3. Both the function help us to join the tensors but torch. cat` function which is equivalent to a concatenation layer. PyTorch Tensor Concatenation is a fundamental operation in PyTorch, yet it often trips up newcomers. cat and torch. Further I plan to concatenate/ club the penultimate layer features both networks before I perform classification. For step i+2, I get a batch from dataset 2 and update my model. utils. I want them to be concated in the axial direction and I will use the results as input for deep learning. whereas the torch. The reason why I want to keep them separate is because I want to attach different decoders in my experiment later. cat ( [t1, t2], dim=0) in my data pre-processing. May 9, 2021 · Good day, I'm currently working on two models which train on the same data. 7; pytorch 1. add ( [x, y]) and z = keras. I would like to concat a high-level feature from two own pre-trained U-Net models. cat to concat pictures belonging to two different folders. cat`, which allows us to concatenate tensors along a specified dimension. cat() function to concatenate tensors along specified dimensions with practical examples and best practices. I am little bit confused if my concatenation is correct and how should input to the LSTM look like. cat() can be seen as an inverse operation for torch. What is Tensor Concatenation? Concatenation refers to joining two or more tensors (multidimensional arrays) together. The combination is written as Feb 28, 2021 · The method that comes to mind right now is the same as above. All tensors must either have the same shape (except in the concatenating dimension) or be a 1-D empty tensor with size (0,). 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. In simple terms, it combines multiple tensors into a single tensor Mar 24, 2022 · c Create a main model that holds two separate original models (without the linear layers) and pass subgraph a through model_a and subgraph b through model_b. Does anyone know the difference between the two? And if there are any other methods for feature combination Nov 14, 2025 · PyTorch, a popular deep learning framework, provides a convenient way to concatenate batches of tensors. Concatenate the results and pass in linear layers i the main model. cat Aug 21, 2017 · Now, @smth has said before that there are no 0 dimensional Tensors in pytorch (For-loop with a 2D matrix of size 0) but does anyone know of a solution to this problem, where for example the 0 size of the dim is being calculated in a loop? Merging Multiple PyTorch Models Using Weight Averaging — A Practical Solution to Low-Infrastructure Training Introduction When building deep learning models, we often strive for better … Dec 24, 2020 · I want to concatenate two layers of convolution class Net (nn. cat () and torch. I want to concatenate their features and then pass concatenated vector to the LSTM. Module. May 22, 2020 · In order to concatenate them with the two other parts, it needs to have size [1, 768], so that it can be concatenated on the first dimension to create a tensor of size [num_left + 1 + num_right, 768]. If I take two identical models, but give them different (but equivalent) initializations, and train them on the same training data (but probably batched up into different (but equivalent) random batches), there is no reason for “weight-17” in model A to have the same value as “weight-17” in model B. Can I run this model by utilizing the two GPUs? Keras documentation: Concatenate layerConcatenates a list of inputs. This Mar 2, 2019 · I have 8 CNN models model1, model2, model3, model4, model5, model6, model7, model8 each with conv2d, activation, maxpooling, dropout layers. load("UNetmodel2. Aug 27, 2019 · Hi, I need to know what is the best way (i. Dec 8, 2024 · I want to change this so I can first split the in_channels into 3 and 1 so that I can have 2 stems: one stem for the RGB channels, and a second stem for the 4th channel. Jun 29, 2018 · I want to build a CNN model that takes additional input data besides the image at a certain layer. Starting with the simplest form of concatenating vectors, we moved to more complex structures like 2D and 3D tensors. on the activation volumes or the linear activation outputs. The process repeats until all samples are iterated. stack () function allows us to stack the tensors and we can join May 19, 2018 · Is it possible to concatenate two tensors with different dimensions without using for loop. Concatenate several datasets Let's look at how to concatenate the various datasets in PyTorch now. How can I concat these two models when th Nov 30, 2022 · Hi! I create two encoders models to concatenate the features of two images, and then I’d like to concatenate the encoders’ output to be one input to the decoder module. Do not call model. weight etc. Nov 26, 2020 · I am trying to create three separate LSTM networks, and then merge them together into one big model. The “ensemble” approach uses two pretrained models to combine their output features (often from the penultimate layer) to train a new classifier, which could boost the performance compared to each model Apr 26, 2021 · in the weights of a neural-network model. pth file. Module): def __init__ (self): super (Net,self). Jun 13, 2019 · In this case, you could use two submodules (each working on the specific data samples) and concatenate these features later in the model. Jan 4, 2019 · I’m trying to implement the following network in pytorch. I am using PyTorch and came across the functions torch. Any help is really appreciated! below my code # define the NN architecture class ConvDenoiser(nn. unsqueeze. pth and clothes. Training model A takes it down some Aug 13, 2020 · To concatenate two tensors in a specific dimension called dim all other dimensions must have the same shape. Sep 30, 2021 · I created two separate models, an encoder and a decoder. cat() function in PyTorch concatenates two or more tensors along a specified dimension. cat # torch. model = models. May 19, 2019 · I want to implement a model similar to the one described in the picture below taken from machine learning - Merging two different models in Keras - Data Science Stack Exchange I have implementations of ModelA and ModelB that work fine when I train them separately. Nov 2, 2024 · In PyTorch, . How can I write this program? May 23, 2020 · What I do is I forward propagate model A (an autoencoder), and subsequently use the latent hidden layer as input for model B (everything online?). safetensors, diffusion_pytorch_model-00002-of-00003. The code I need would be something like: additional_data_dim = 100 output_classes = 2 model = models. Jul 13, 2023 · Thanks @ptrblck ! Is the advice in this tutorial for a different scenario ? Quoting When saving a model comprised of multiple torch. Modules, such as a GAN, a sequence-to-sequence model, or an ensemble of models, you must save a dictionary of each model’s state_dict When does this apply ? Nov 22, 2020 · I want to write a code in by Pytorch that concatenate two images (32 by 32), in the way the output image becomes (64 by 32), how should I do that? Thank you~ Jun 18, 2018 · For an LSTM model for sentence classification, I have text sequences for input. How can I define forward func to process 2 inputs separately then combine them in a middle layer? Aug 7, 2020 · For dynamic size inputs, one solution would be writing your own code using ONNX API as stated earlier. cat() and torch. DataLoader and torch. Sep 24, 2020 · Hello, I am using PyTorch for a BERT model. In that case, I would have to loop for how many operations I want to generate and infer the network each time Out: This is used for the output of tensor and it is an optional part of this syntax. For step i+1, I receive a batch from dataset 1 and update my model. Concatenating tensors is often required when we want to combine different parts of data, such as feature maps in a neural network or different input sequences. torch. hstack() (short for horizontal stack) is a function used to concatenate two or more tensors along the horizontal axis (axis=1). concatenate(tensors, axis=0, out=None) → Tensor # Alias of torch. My question is how to combine this two model? Sep 30, 2023 · I am having two pretrained models in pytorch which use different type of input data. 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. By the end of this guide, you‘ll have a deep understanding of tensor concatenation and be able to use cat() like a pro. Sequential containers that I merge like this: concat = tf. cat () is basically used to concatenate the given sequence of tensors in the given dimension. Now if I also want to use other features, like part-of-speech, do I simply concatenate them and have 101D inputs? Doesn’t this diminish the effect of the POS tags? Also, the embeddings are trainable and can be learned during training Jul 20, 2022 · 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. I’m not sure if the method I used to combine layers is correct. I am loading two different models (last. concat # torch. vgg16 (pre… 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. Jun 1, 2017 · I know we can use “optimizer = optim. data. Aug 23, 2017 · If I have two nn. (More precisely, I’m implementing a model called DKVMN in this paper). Now I am trying to obtain a single model out these two models by combining the weights. parameters () Concat Features from two pretrained models and add a classification head then do Ensemble Learning Parallel two encoder in Pytorch Jul 12, 2025 · In the realm of deep learning, data manipulation and feature combination play a crucial role in building effective models. Then I combining those two models and train them together. Is that correct? I am kind of new to this. cvx xvwxu dtgvi quisyv ytox ntm qrbcuwq cxyh ymhow dvv nwwjwb rgxa kcn cdmercrs auhgahw