When preparing the data for a PyTorch Model, I often use the squeeze and unsqueeze functions like so: inps = torch.FloatTensor(data[0]) tgts = torch.FloatTensor(data[1]) tgts = torch.unsqueeze(tgts, -1) tgts = torch.unsqueeze(tgts, -1) tgts ...
While attempting to calculate the loss of the policy target network in Deep Deterministic Policy Gradient Algorithms using PyTorch 1.5, an error is encountered as shown below. File "F:agentsddpg.py", line 128, in train_model policy_loss.bac ...
As an illustration, consider that 'labels' represent ground truth data. The size of labels is [4,224,224], where 4 refers to the batch size and 224 represents height and width. The dtype of labels is torch.int64. In my training code, the pixels in the la ...
I need to find an alternative to the torch.norm function in Pytorch. I was successful in replacing torch.norm when dealing with a single tensor, as shown below: import torch x = torch.randn(9) out1 = torch.norm(x) out2 = sum(abs(x)**2)**(1./2) out1 == out ...
I am currently immersed in the creation of a seminar paper focused on natural language processing (NLP) and the summarization of source code function documentation. To achieve this, I have meticulously curated my own dataset consisting of approximately 640 ...
A few months ago, I developed a code to train an NER model which was functioning perfectly. However, when I tried running the same code recently, I encountered this error: ImportError: Using the `Trainer` with `PyTorch` requires `accelerate`: Run `pip inst ...
Currently, I am attempting to provide PyTorch with input for creating a simple Neural Network. The challenge lies in the following issue: I have all the necessary data stored in a CSV file, which I am reading using Panda. Below is the code snippet used: d ...
I have a unique PyTorch model that I loaded and I am curious about determining its input shape. Is there a method like this? model.input_shape Can anyone provide assistance in obtaining this information? Updated: The methods print() and summary() do not ...
Currently, I am struggling to grasp a specific section of the code within the ResNet architecture. You can find the complete code on this link: https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/02-intermediate/deep_residual_network/main-gpu. ...
After successfully installing Pytorch through Anaconda, I encountered an issue where PyCharm was unable to find the module. ModuleNotFoundError: No module named 'torch' In addition, I have CUDA installed, but when attempting to add the package with PyC ...
Currently, I am delving into the realm of tensor storage by exploring a blog written in my mother tongue, Vietnamese. While tinkering with various examples, I stumbled upon a concept that proved to be quite perplexing. Consider three tensors x, zzz, and x_ ...
I have a set of images where each pixel value should fall within the range of [0, 1]. While running a deep learning model on these images, I am looking for a way to normalize each image in such a manner that they always stay within the confines of [0, 1] p ...
Recently started with PyTorch. I have built a custom model inspired by a research paper, but encountered an error during training. element 0 of tensors does not require grad and does not have a grad_fn Below is the model implementation: class Classifica ...
Does anyone know how to convert the given lr_lambda def into Lambda format? from torch.optim.lr_scheduler import LambdaLR def cosine_scheduler(optimizer, training_steps, warmup_steps): def lr_lambda(current_step): if current_step < warmup_s ...
Looking to create 100 unique python scripts containing the MyData class from MyData_1 all the way up to MyData_100. import torch import numpy as np from torch_geometric.data import InMemoryDataset, Data from torch_geometric.utils import to_undirected clas ...
After extensive testing, I discovered that my code functions properly when the weights are initialized with 0. However, when I try to initialize them based on a specific seed, they fail to converge as expected. This should not be the case since the loss fu ...
Attempting to install PyTorch using pip3 install torch --no-cache-dir resulted in the following error after a few seconds: Collecting torch Downloading https://files.pythonhosted.org/packages/24/19/4804aea17cd136f1705a5e98a00618cb8f6ccc375ad8bfa4374 ...
My MacBook Pro does not have GPU support, so I uploaded a directory of codes to Google Colab to utilize Cuda support. However, I am facing an issue where the code is unable to access other files in folders within the current directory. Any advice on how to ...
Having trouble installing pytorch in conda within a Docker environment, resulting in an UnsatisfiableError. The error message does not reveal any clear conflicts or I might be misinterpreting it. The Docker image being used is nvidia/cuda:10.1-cudnn7-devel ...
I am currently delving into the realm of machine learning and working on constructing an LSTM neural network. My model takes in 7 features as input and aims to predict 2 labels. However, I encountered an error when passing all 7 inputs into the LSTM laye ...
I am attempting to execute a Pytorch LSTM network in the browser, but I am encountering the following error: graph.ts:313 Uncaught (in promise) Error: unrecognized input '' for node: LSTM_4 at t.buildGraph (graph.ts:313) at new t (graph.ts:139) ...
Can the torch.nn.Embedding function process a one-hot vector ([batch_size, seq_len, vocab_size]) to produce embeddings equivalent to those generated from an input of integer tokens [batch_size, seq_len]? And if so, would this process be differentiable? ...
I am currently developing a model that utilizes MLP for both feature extraction and dimension reduction. This model has the ability to condense data from 204 dimensions down to just 80 dimensions through the following process: A dense layer with 512 dimen ...
I'm in the process of constructing a Convolutional Neural Network (CNN) inspired by the research paper located at: (). For my project, I am utilizing images obtained from the LAB-color space. I have developed a data loader to handle the "l and a, b" value ...
After struggling to get answers from similar threads, I find myself in need of guidance. My challenge is to simultaneously train two models within the same loop, with model updates involving a unique computation that incorporates combined loss values from ...
What is the correct way to use a PyTorch tensor or an OpenCV image as input for OpenAI CLIP? I have attempted the following approach, but it has not been successful thus far: device = "cuda" if torch.cuda.is_available() else "cpu" clip_model, clip_preproc ...
Looking for a way to save the weights of a PyTorch model into a .txt or .json file? One method is to write it to a .txt file using the following code: #import torch model = torch.load("model_path") string = str(model) with open('some_file.txt', ' ...