Advanced Usage

This guide dives deeper into Brain4J, focusing on how to work efficiently with datasets, advanced training techniques, and utilizing GPU acceleration.

🤖 Using SmartTrainer

SmartTrainer automates training by handling batch updates, stopping conditions, and evaluation.

🔹 Basic Usage

// Learning rate decay 0.95, evaluate every 5 epochs
SmartTrainer trainer = new SmartTrainer(0.95, 5);

// Train until loss < 0.01
trainer.start(model, dataSource, 0.01);

🔹 Training for a Fixed Number of Epochs

trainer.startFor(model, dataSource, 1000); // Train for 1000 epochs

🔹 Monitoring Training Progress

You can add listeners to track the training process in real time.

private static class ExampleListener extends TrainListener {
    @Override
    public void onEvaluated(ListDataSource dataSource, EvaluationResult evaluation, int epoch, long took) {
         System.out.print("\rEpoch " + epoch + " loss: " + evaluation.loss() + " took " + (took / 1e6) + " ms");
    }
}
trainer.addListener(new ExampleListener());

🧠 Using Tensors and GPU Acceleration

Brain4J now supports hardware-accelerated neural network operations through its tensor system. Tensors replace traditional matrices and vectors, providing multidimensional data structures optimized for neural network operations.

🔹 Tensor Structure

Tensors are N-dimensional arrays that can represent scalars (0D), vectors (1D), matrices (2D), and higher-dimensional data. They form the foundation of modern neural networks.

// Create a 2D tensor (matrix)
Tensor matrix = Tensors.matrix(3, 4); // 3x4 matrix

// Create a 3D tensor
Tensor tensor3D = Tensors.create(2, 3, 4); // shape [2,3,4]

// Create tensors with initial values
Tensor ones = Tensors.ones(2, 2); // 2x2 matrix filled with 1.0
Tensor zeros = Tensors.zeros(3, 3); // 3x3 matrix filled with 0.0
Tensor random = Tensors.random(2, 3); // 2x3 matrix with random values

// Create from existing data
float[] data = {1.0f, 2.0f, 3.0f, 4.0f};
Tensor fromData = Tensors.of(new int[]{2, 2}, data); // Creates a 2x2 tensor

🔹 GPU Acceleration

Brain4J can automatically use GPU acceleration for tensor operations when available, providing significant speedups for large models.

// Check if GPU is available
boolean gpuAvailable = TensorGPU.isGpuAvailable();

// Check if GPU is currently being used
boolean usingGPU = Tensors.isUsingGPU();

// Enable GPU if available
Brain4J.useGPUIfAvailable();

// Force CPU usage (even if GPU is available)
Brain4J.forceCPU();

// Remember to release GPU resources when done
TensorGPU.releaseGPUResources();

🔹 Tensor Operations

The tensor system provides optimized operations for neural networks:

// Matrix multiplication
Tensor result = tensorA.matmul(tensorB);

// Element-wise operations
Tensor sum = tensorA.add(tensorB);
Tensor difference = tensorA.sub(tensorB);
Tensor product = tensorA.mul(tensorB);
Tensor quotient = tensorA.div(tensorB);

// Apply function to all elements
Tensor activated = tensor.map(x -> Math.max(0, x)); // ReLU activation

// Reshape tensor
Tensor reshaped = tensor.reshape(3, 4);

// Transpose dimensions
Tensor transposed = matrix.transpose();

📚 Next Steps

• Use cases: Check out Examples & Use Cases