Pytorch Deep Learning with Google Colaboratory
PyTorch is an open-source deep learning library developed by Facebook’s AI research lab, and it is widely used for building and training neural networks. Google Colaboratory, or Colab for short, is a free cloud-based platform that supports Python programming and enables easy access to GPUs for deep learning tasks.
Combining PyTorch and Google Colab provides a powerful environment for deep learning projects without the need for expensive hardware. In this article, we will explore how to use PyTorch in Google Colab for training neural networks.
Setting up Google Colab for PyTorch
To get started, you first need to create a new Python 3 notebook in Google Colab. Next, you will need to install PyTorch in the notebook. Luckily, PyTorch provides pre-built packages for easy installation with just a few lines of code:
!pip install torch torchvision
Training a Neural Network in PyTorch
With PyTorch installed, you can start building and training neural networks in Google Colab. For example, you can create a simple fully-connected neural network for classifying images using the MNIST dataset:
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
# Define the neural network architecture
class SimpleNN(nn.Module):
def __init__(self):
super(SimpleNN, self).__init__()
self.fc1 = nn.Linear(784, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = torch.flatten(x, 1)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# Load the MNIST dataset
train_loader = torch.utils.data.DataLoader(datasets.MNIST('data', train=True, download=True,
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])),
batch_size=64, shuffle=True)
# Initialize the neural network and optimizer
model = SimpleNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Train the neural network
for epoch in range(5):
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
Conclusion
Using PyTorch in Google Colab provides a convenient and cost-effective way to build and train neural networks. With access to GPUs in Colab, you can accelerate the training process and tackle more complex deep learning tasks. Experiment with different neural network architectures, datasets, and hyperparameters to develop and enhance your deep learning skills.
Great stuff