python-pytorchHow do I use the Python PyTorch function?

The Python PyTorch function is a library of functions used to create deep learning models with the PyTorch library. To use the PyTorch function, you will need to first install the PyTorch library.

Once the library is installed, you can import the PyTorch function into your Python script. For example, the following code will import the PyTorch function into your script:

import torch

You can then use the PyTorch function to create a deep learning model. For example, the following code will create a simple neural network:

import torch.nn as nn

model = nn.Sequential(
    nn.Linear(input_size, hidden_size),
    nn.ReLU(),
    nn.Linear(hidden_size, output_size),
    nn.Softmax(dim=1)
)

Once the model is created, you can then train the model using the PyTorch function. For example, the following code will train the model using the PyTorch function:

# Train the model
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

for epoch in range(num_epochs):
    # Forward pass
    outputs = model(inputs)
    loss = criterion(outputs, labels)

    # Backward and optimize
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

Finally, you can use the PyTorch function to evaluate the model. For example, the following code will evaluate the model using the PyTorch function:

# Test the model
correct = 0
total = 0

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

print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

The output of the code will be the accuracy of the model on the test set.

Code parts explained

1. import torch: imports the PyTorch function into your Python script.
2. nn.Sequential(): creates a simple neural network.
3. nn.CrossEntropyLoss(): defines the loss function used for training the model.
4. torch.optim.Adam(): defines the optimizer used for training the model.
5. model(inputs): forward pass of the model.
6. loss.backward(): backward pass of the model.
7. torch.max(outputs.data, 1): predicts the labels of the test set.

Relevant links

1. PyTorch Documentation
2. PyTorch Tutorials
3. PyTorch Examples

Edit this code on GitHub