Coding Llama 2 from scratch in PyTorch – Part 3

Welcome to Part 3 of our Coding Llama 2 tutorial series on how to build a PyTorch model from scratch. In this part, we will dive deeper into the code and explore more advanced concepts.

Setting Up the Model

Let’s start by setting up our PyTorch model. We will define the structure of the neural network architecture, including the number of layers, activation functions, and loss function.

	import torch
	import torch.nn as nn

	class NeuralNetwork(nn.Module):
    	def __init__(self):
	        super(NeuralNetwork, self).__init__()
	        self.fc1 = nn.Linear(784, 256)
	        self.fc2 = nn.Linear(256, 128)
	        self.fc3 = nn.Linear(128, 10)

    	def forward(self, x):
        	x = torch.relu(self.fc1(x))
        	x = torch.relu(self.fc2(x))
        	x = self.fc3(x)
        	return x

	model = NeuralNetwork()

Training the Model

Next, we will train our model using a training loop. We will define the optimizer and learning rate, as well as the number of epochs and batch size.

	import torch.optim as optim

	optimizer = optim.Adam(model.parameters(), lr=0.001)
	criterion = nn.CrossEntropyLoss()

	for epoch in range(num_epochs):
    	for i, data in enumerate(train_loader, 0):
        	inputs, labels = data
        	optimizer.zero_grad()
        	outputs = model(inputs)
        	loss = criterion(outputs, labels)
        	loss.backward()
        	optimizer.step()

Evaluating the Model

Finally, we will evaluate our model on the test dataset to see how well it performs.

	correct = 0
	total = 0

	with torch.no_grad():
    	for data in test_loader:
        	inputs, labels = data
        	outputs = model(inputs)
        	_, predicted = torch.max(outputs.data, 1)
        	total += labels.size(0)
        	correct += (predicted == labels).sum().item()

	accuracy = correct / total
	print(f'Accuracy: {accuracy}')

Congratulations! You have successfully built and trained a PyTorch model from scratch. Stay tuned for more advanced topics in our Coding Llama 2 series.