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Classifying images of everyday objects using a neural network

The ability to try many different neural network architectures to address a problem is what makes deep learning really powerful, especially compared to shallow learning techniques like linear regression, logistic regression etc.

In this assignment, you will:

  1. Explore the CIFAR10 dataset: https://www.cs.toronto.edu/~kriz/cifar.html
  2. Set up a training pipeline to train a neural network on a GPU
  3. Experiment with different network architectures & hyperparameters

As you go through this notebook, you will find a ??? in certain places. Your job is to replace the ??? with appropriate code or values, to ensure that the notebook runs properly end-to-end. Try to experiment with different network structures and hypeparameters to get the lowest loss.

You might find these notebooks useful for reference, as you work through this notebook:

# Uncomment and run the commands below if imports fail
# !conda install numpy pandas pytorch torchvision cpuonly -c pytorch -y
# !pip install matplotlib --upgrade --quiet
!ls -al
total 16 drwxr-xr-x 1 root root 4096 Jun 10 16:28 . drwxr-xr-x 1 root root 4096 Jun 12 16:43 .. drwxr-xr-x 1 root root 4096 Jun 10 16:28 .config drwxr-xr-x 1 root root 4096 Jun 10 16:28 sample_data
!cd sample_data
!rm sample_data/mnist_train_small.csv
!ls sample_data -al
total 20 drwxr-xr-x 1 root root 4096 Jun 12 16:51 . drwxr-xr-x 1 root root 4096 Jun 10 16:28 .. -rwxr-xr-x 1 root root 1697 Jan 1 2000 anscombe.json -rwxr-xr-x 1 root root 930 Jan 1 2000 README.md
import torch
import torchvision
import numpy as np
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
from torchvision.datasets import CIFAR10
from torchvision.transforms import ToTensor
from torchvision.utils import make_grid
from torch.utils.data.dataloader import DataLoader
from torch.utils.data import random_split
%matplotlib inline