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Training Deep Neural Networks on a GPU with PyTorch

Part 4 of "PyTorch: Zero to GANs"

This post is the fourth in a series of tutorials on building deep learning models with PyTorch, an open source neural networks library. Check out the full series:

  1. PyTorch Basics: Tensors & Gradients
  2. Linear Regression & Gradient Descent
  3. Image Classfication using Logistic Regression
  4. Training Deep Neural Networks on a GPU
  5. Image Classification using Convolutional Neural Networks
  6. Data Augmentation, Regularization and ResNets
  7. Generating Images using Generative Adverserial Networks

In the previous tutorial, we trained a logistic regression model to identify handwritten digits from the MNIST dataset with an accuracy of around 86%.

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However, we also noticed that it's quite difficult to improve the accuracy beyond 87%, due to the limited power of the model. In this post, we'll try to improve upon it using a feedforward neural network.

Preparing the Data

The data preparation is identical to the previous tutorial. We begin by importing the required modules & classes.

import torch
import numpy as np
import torchvision
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.data.dataloader import DataLoader
dataset = MNIST(root='data/', 
                download=True, 
                transform=ToTensor())
def split_indices(n, val_pct):
    # Determine size of validation set
    n_val = int(val_pct*n)
    # Create random permutation of 0 to n-1
    idxs = np.random.permutation(n)
    # Pick first n_val indices for validation set
    return idxs[n_val:], idxs[:n_val]