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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:

In [1]:
# Uncomment and run the commands below if imports fail
# !conda install numpy pandas pytorch torchvision cpuonly -c pytorch -y
# !pip install matplotlib --upgrade --quiet
In [2]:
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
In [3]:
# Project name used for jovian.commit
project_name = '03-cifar10-feedforward'

Exploring the CIFAR10 dataset

In [4]:
dataset = CIFAR10(root='data/', download=True, transform=ToTensor())
test_dataset = CIFAR10(root='data/', train=False, transform=ToTensor())
Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/cifar-10-python.tar.gz
HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))
Extracting data/cifar-10-python.tar.gz to data/

Q: How many images does the training dataset contain?

In [ ]:
dataset_size = ???
dataset_size

Q: How many images does the training dataset contain?

In [ ]:
test_dataset_size = ???
test_dataset_size

Q: How many output classes does the dataset contain? Can you list them?

Hint: Use dataset.classes

In [ ]:
classes = ???
classes
In [ ]:
num_classes = ???
num_classes

Q: What is the shape of an image tensor from the dataset?

In [ ]:
img, label = dataset[0]
img_shape = ???
img_shape

Note that this dataset consists of 3-channel color images (RGB). Let us look at a sample image from the dataset. matplotlib expects channels to be the last dimension of the image tensors (whereas in PyTorch they are the first dimension), so we'll the .permute tensor method to shift channels to the last dimension. Let's also print the label for the image.

In [ ]:
img, label = dataset[0]
plt.imshow(img.permute((1, 2, 0)))
print('Label (numeric):', label)
print('Label (textual):', classes[label])

(Optional) Q: Can you determine the number of images belonging to each class?

Hint: Loop through the dataset.

In [ ]:
 

Let's save our work to Jovian, before continuing.

In [20]:
!pip install jovian --upgrade --quiet
WARNING: You are using pip version 20.1; however, version 20.1.1 is available. You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.
In [21]:
import jovian
In [ ]:
jovian.commit(project=project_name, environment=None)
[jovian] Attempting to save notebook..

Preparing the data for training

We'll use a validation set with 5000 images (10% of the dataset). To ensure we get the same validation set each time, we'll set PyTorch's random number generator to a seed value of 43.

In [5]:
torch.manual_seed(43)
val_size = 5000
train_size = len(dataset) - val_size

Let's use the random_split method to create the training & validation sets

In [6]:
train_ds, val_ds = random_split(dataset, [train_size, val_size])
len(train_ds), len(val_ds)
Out[6]:
(45000, 5000)

We can now create data loaders to load the data in batches.

In [7]:
batch_size=128
In [8]:
train_loader = DataLoader(train_ds, batch_size, shuffle=True, num_workers=4, pin_memory=True)
val_loader = DataLoader(val_ds, batch_size*2, num_workers=4, pin_memory=True)
test_loader = DataLoader(test_dataset, batch_size*2, num_workers=4, pin_memory=True)

Let's visualize a batch of data using the make_grid helper function from Torchvision.

In [9]:
for images, _ in train_loader:
    print('images.shape:', images.shape)
    plt.figure(figsize=(16,8))
    plt.axis('off')
    plt.imshow(make_grid(images, nrow=16).permute((1, 2, 0)))
    break
images.shape: torch.Size([128, 3, 32, 32])

Can you label all the images by looking at them? Trying to label a random sample of the data manually is a good way to estimate the difficulty of the problem, and identify errors in labeling, if any.

Base Model class & Training on GPU

Let's create a base model class, which contains everything except the model architecture i.e. it wil not contain the __init__ and __forward__ methods. We will later extend this class to try out different architectures. In fact, you can extend this model to solve any image classification problem.

In [10]:
def accuracy(outputs, labels):
    _, preds = torch.max(outputs, dim=1)
    return torch.tensor(torch.sum(preds == labels).item() / len(preds))
In [11]:
class ImageClassificationBase(nn.Module):
    def training_step(self, batch):
        images, labels = batch 
        out = self(images)                  # Generate predictions
        loss = F.cross_entropy(out, labels) # Calculate loss
        return loss
    
    def validation_step(self, batch):
        images, labels = batch 
        out = self(images)                    # Generate predictions
        loss = F.cross_entropy(out, labels)   # Calculate loss
        acc = accuracy(out, labels)           # Calculate accuracy
        return {'val_loss': loss.detach(), 'val_acc': acc}
        
    def validation_epoch_end(self, outputs):
        batch_losses = [x['val_loss'] for x in outputs]
        epoch_loss = torch.stack(batch_losses).mean()   # Combine losses
        batch_accs = [x['val_acc'] for x in outputs]
        epoch_acc = torch.stack(batch_accs).mean()      # Combine accuracies
        return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}
    
    def epoch_end(self, epoch, result):
        print("Epoch [{}], val_loss: {:.4f}, val_acc: {:.4f}".format(epoch, result['val_loss'], result['val_acc']))

We can also use the exact same training loop as before. I hope you're starting to see the benefits of refactoring our code into reusable functions.

In [12]:
def evaluate(model, val_loader):
    outputs = [model.validation_step(batch) for batch in val_loader]
    return model.validation_epoch_end(outputs)

def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):
    history = []
    optimizer = opt_func(model.parameters(), lr)
    for epoch in range(epochs):
        # Training Phase 
        for batch in train_loader:
            loss = model.training_step(batch)
            loss.backward()
            optimizer.step()
            optimizer.zero_grad()
        # Validation phase
        result = evaluate(model, val_loader)
        model.epoch_end(epoch, result)
        history.append(result)
    return history

Finally, let's also define some utilities for moving out data & labels to the GPU, if one is available.

In [13]:
torch.cuda.is_available()
Out[13]:
False
In [14]:
def get_default_device():
    """Pick GPU if available, else CPU"""
    if torch.cuda.is_available():
        return torch.device('cuda')
    else:
        return torch.device('cpu')
In [15]:
device = get_default_device()
device
Out[15]:
device(type='cpu')
In [16]:
def to_device(data, device):
    """Move tensor(s) to chosen device"""
    if isinstance(data, (list,tuple)):
        return [to_device(x, device) for x in data]
    return data.to(device, non_blocking=True)

class DeviceDataLoader():
    """Wrap a dataloader to move data to a device"""
    def __init__(self, dl, device):
        self.dl = dl
        self.device = device
        
    def __iter__(self):
        """Yield a batch of data after moving it to device"""
        for b in self.dl: 
            yield to_device(b, self.device)

    def __len__(self):
        """Number of batches"""
        return len(self.dl)

Let us also define a couple of helper functions for plotting the losses & accuracies.

In [17]:
def plot_losses(history):
    losses = [x['val_loss'] for x in history]
    plt.plot(losses, '-x')
    plt.xlabel('epoch')
    plt.ylabel('loss')
    plt.title('Loss vs. No. of epochs');
In [18]:
def plot_accuracies(history):
    accuracies = [x['val_acc'] for x in history]
    plt.plot(accuracies, '-x')
    plt.xlabel('epoch')
    plt.ylabel('accuracy')
    plt.title('Accuracy vs. No. of epochs');

Let's move our data loaders to the appropriate device.

In [19]:
train_loader = DeviceDataLoader(train_loader, device)
val_loader = DeviceDataLoader(val_loader, device)
test_loader = DeviceDataLoader(test_loader, device)

Training the model

We will make several attempts at training the model. Each time, try a different architecture and a different set of learning rates. Here are some ideas to try:

  • Increase or decrease the number of hidden layers
  • Increase of decrease the size of each hidden layer
  • Try different activation functions
  • Try training for different number of epochs
  • Try different learning rates in every epoch

What's the highest validation accuracy you can get to? Can you get to 50% accuracy? What about 60%?

In [ ]:
input_size = 3*32*32
output_size = 10

Q: Extend the ImageClassificationBase class to complete the model definition.

Hint: Define the __init__ and forward methods.

In [ ]:
class CIFAR10Model(ImageClassificationBase):
    def __init__(self):
        super().__init__()
        ???
        
    def forward(self, xb):
        # Flatten images into vectors
        out = xb.view(xb.size(0), -1)
        # Apply layers & activation functions
        ???
        return out

You can now instantiate the model, and move it the appropriate device.

In [ ]:
model = to_device(CIFAR10Model(), device)

Before you train the model, it's a good idea to check the validation loss & accuracy with the initial set of weights.

In [ ]:
history = [evaluate(model, val_loader)]
history

Q: Train the model using the fit function to reduce the validation loss & improve accuracy.

Leverage the interactive nature of Jupyter to train the model in multiple phases, adjusting the no. of epochs & learning rate each time based on the result of the previous training phase.

In [ ]:
history += fit(???, ???, model, train_loader, val_loader)
In [ ]: