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Neuronetwork Class

Neuronetwork Class is a powerful tool for training, testing, deploying and inferencing deep learning models. It provides a high-level API to build and train various types of neural networks, including feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). With its flexible architecture and extensive capabilities, Neuronetwork Class enables developers to efficiently experiment with different models, optimize hyperparameters, and iterate on their deep learning pipelines.

Usage

1. Import the necessary modules:

   import torch
   import torchvision
   import torchvision.transforms as transforms

2. Define the hyperparameters and constants:

   INPUT_SIZE = 784
   HIDDEN_SIZE = 100
   NUM_EPOCHS = 5
   BATCH_SIZE = 100
   LEARNING_RATE = 0.001

3. Load and preprocess the dataset:

   train_dataset = torchvision.datasets.MNIST(root='./data',
                                           train=True,
                                           transform=transforms.ToTensor(),
                                           download=True)
   train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=BATCH_SIZE,
                                           shuffle=True)
   train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=BATCH_SIZE,
                                           shuffle=True)
   test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=BATCH_SIZE,
                                          shuffle=False)

4. Define the neural network architecture:

   class NeuralNetwork(torch.nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(NeuralNetwork, self).__init__()
        self.fc1 = torch.nn.Linear(input_size, hidden_size)
        self.relu = torch.nn.ReLU()
        self.fc2 = torch.nn.Linear(hidden_size, num_classes)
   
    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

5. Instantiate the model and move it to the available device:

   model = NeuralNetwork(INPUT_SIZE, HIDDEN_SIZE, num_classes=torch.max(train_dataset.targets).item() + 1)
   device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
   model.to(device)

6. Set up the loss function and optimizer:

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

7. Train the model:

   for epoch in range(NUM_EPOCHS):
    for i, (images, labels) in enumerate(train_loader):
        images = images.reshape(-1, INPUT_SIZE).to(device)
        labels = labels.to(device)
   
        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)
   
        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
   
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, NUM_EPOCHS, i+1, len(train_loader), loss.item()))

8. Evaluate the model:

   
   correct = 0
   total = 0

with torch.no_grad():
for images, labels in test_loader:
images = images.reshape(-1, INPUTSIZE).to(device)
labels = labels.to(device)
outputs = model(images)
predicted = torch.max(outputs, dim=1)
total += labels.size(0)
correct += (predicted == labels).sum()

print(‘Test Accuracy of the model on the 10000 test images: {} %’.format(100 * correct / total))

9. Save the trained model:
```python
torch.save(model.state_dict(), "./model/Mnist.pkl")

Contributions

If you find this code helpful or have any suggestions for improvements, please feel free to create an issue or submit a pull request on the GitHub repository.

License

This project is licensed under the My License.

Acknowledgements

The Neuronetwork Class is inspired by the need for a simple and versatile deep learning framework. It leverages the power of PyTorch to provide a high-level API for building and training neural networks. The code presented here is based on the official PyTorch documentation and tutorials. Special thanks to the PyTorch community for their amazing work!

Summary

The Neuronetwork Class is a versatile deep learning tool that provides a high-level API for training, testing, and deploying neural networks. With its flexible architecture and extensive capabilities, it simplifies the development process and enables efficient experimentation with different models. By leveraging the power of PyTorch, Neuronetwork Class empowers developers to build cutting-edge deep learning solutions.

# Source From GitHub/BoraJun/funcls
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