Hello all,
I am building a basic deep learning model using PyTorch for image classification-
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
# Define a simple feedforward neural network
class NeuralNetwork(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetwork, self).__init()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, num_classes)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
# Hyperparameters
input_size = 784 # Example: 28x28 images
hidden_size = 128
num_classes = 10
learning_rate = 0.001
num_epochs = 5
# Load and preprocess data (MNIST dataset)
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transform, download=True)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=100, shuffle=True)
# Initialize the model
model = NeuralNetwork(input_size, hidden_size, num_classes)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Training loop
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.view(-1, 28 * 28)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backpropagation and optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(train_loader)}], Loss: {loss.item():.4f}')
# Test the model on test data (not shown here)
# Save the model
torch.save(model.state_dict(), 'model.ckpt')
When I run the code, it’s showing the below error-
Output:
Traceback (most recent call last):
File “main.py”, line 1, in
import torch
ModuleNotFoundError: No module named ‘torch’
Any solution will highly be appreciated.
Thanks