Simple Neural Network with PyTorch in 20 Minutes

If you’re interested in getting started with neural networks and deep learning, PyTorch is a great framework to use. In this tutorial, we’ll walk you through building a simple neural network using PyTorch in just 20 minutes.

Step 1: Installing PyTorch

First, you’ll need to install PyTorch. You can do this by following the instructions on the PyTorch website.

Step 2: Importing PyTorch

Once you have PyTorch installed, you can import it into your Python script as follows:


import torch
import torch.nn as nn
import torch.optim as optim


Step 3: Creating a Simple Neural Network

Next, we’ll create a simple neural network with just one input layer, one hidden layer, and one output layer. Here’s what the code might look like:


class SimpleNN(nn.Module):
def __init__(self):
super(SimpleNN, self).__init__()
self.fc1 = nn.Linear(1, 3) # 1 input feature, 3 hidden units
self.fc2 = nn.Linear(3, 1) # 3 hidden units, 1 output unit

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


Step 4: Training the Neural Network

Now that we have our neural network defined, we can train it on some sample data. Here’s an example of how you might do this:


# Define the model
model = SimpleNN()

# Training loop
for epoch in range(1000):
optimizer.zero_grad()
outputs = model(x_train)
loss = criterion(outputs, y_train)
loss.backward()
optimizer.step()


Step 5: Testing the Neural Network

Finally, you can test your neural network on some test data to see how well it performs. Here’s an example of how you might do this:


x_test = torch.rand((10, 1))
y_test = 3*x_test + 2
predictions = model(x_test)
print(predictions)


And there you have it – a simple neural network implemented using PyTorch in just 20 minutes! Of course, this is just scratching the surface of what PyTorch can do, but hopefully, it gives you a good starting point for exploring more advanced neural network architectures and techniques.