Back ArrowExplore all kits

Loading data using PyTorch DataLoader

share link

by kanika dot icon Updated: Mar 29, 2023

technology logo
technology logo

Solution Kit Solution Kit  

PyTorch DataLoader is a Python module that provides a set of tools for efficiently loading and transforming data for use in a PyTorch neural network. It helps define how data is loaded, transformed, and fed into a model. It also provides an efficient way to batch, shuffle, and iterate over datasets. DataLoader makes it easier to load and process data, allowing developers to focus more on building and training models.



The PyTorch DataLoader class is used to load data from a dataset and feed it to a model for training or evaluation. The DataLoader class provides a convenient way to iterate over a dataset, with the ability to batch, shuffle, and load the data in parallel.


Here is an example of loading data using PyTorch DataLoader.



Fig 1: Preview of the output that you will get.

Code


In this solution, we use the DataLoader of the PyTorch.

How to load a list of numpy arrays to pytorch dataset loader?

PythondotLines of Code : 13dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import torch
import numpy as np
from torch.utils.data import TensorDataset, DataLoader

my_x = [np.array([[1.0,2],[3,4]]),np.array([[5.,6],[7,8]])] # a list of numpy arrays
my_y = [np.array([4.]), np.array([2.])] # another list of numpy arrays (targets)

tensor_x = torch.Tensor(my_x) # transform to torch tensor
tensor_y = torch.Tensor(my_y)

my_dataset = TensorDataset(tensor_x,tensor_y) # create your datset
my_dataloader = DataLoader(my_dataset) # create your dataloader

Instructions

Follow the steps carefully to get the output easily.

  1. Install Jupyter Notebook on your computer.
  2. Open terminal and install the required libraries with following commands.
  3. Install PyTorch - pip install torch.
  4. Copy the code using the "Copy" button above, and paste it into your IDE's Python file.
  5. Add print statements for the variables my_dataset and my_dataloader using the following commands.
  6. print (my_dataset) and print(my_dataloader)
  7. Run the file to load data using PyTorch DataLoader


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for "Loading data using PyTorch DataLoader" in kandi. You can try any such use case!

Dependent Libraries


pytorchby pytorch

Python doticonstar image 67874 doticonVersion:v2.0.1doticon
License: Others (Non-SPDX)

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Support
    Quality
      Security
        License
          Reuse

            pytorchby pytorch

            Python doticon star image 67874 doticonVersion:v2.0.1doticon License: Others (Non-SPDX)

            Tensors and Dynamic neural networks in Python with strong GPU acceleration
            Support
              Quality
                Security
                  License
                    Reuse

                      Environment Tested


                      I tested this solution in the following versions. Be mindful of changes when working with other versions.

                      1. The solution is created in Python 3.9.6
                      2. The solution is tested on PyTorch 2.0.0+cpu version.


                      Using this solution, we are able to perform indexing and slicing of tensor in PyTorch in Python with simple steps. PyTorch is also used in Computer Vision and Generative Adversarial Networks.

                      Support


                      1. For any support on kandi solution kits, please use the chat
                      2. For further learning resources, visit the Open Weaver Community learning page.


                      See similar Kits and Libraries

                      Open Weaver – Develop Applications Faster with Open Source

                      kandi

                      Community and Support

                      Company

                      Follow

                      • © 2023 Open Weaver Inc.