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2 hours ago
2
// Neural network training with automatic differentiation
let X = tensor([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]], [4, 2])
let Y = tensor([0.0, 1.0, 1.0, 0.0], [4, 1])
// Initialize parameters with gradient tracking
let W1 = tensor_with_grad([0.5, -0.3, 0.2, 0.4], [2, 2])
let b1 = tensor_with_grad([0.1, -0.1], [2])
// Create optimizer
let optimizer = adam_create(0.01)
let epoch = 0
while epoch < 100 {
// Forward pass
let h1 = nn_linear(X, W1, b1)
let pred = nn_sigmoid(h1)
// Compute loss
let loss = nn_mse_loss(pred, Y)
// Backward pass - automatic differentiation
tensor_backward(loss)
// Update parameters
let params = [W1, b1]
let updated = adam_step(optimizer, params)
W1 = updated[0]
b1 = updated[1]
epoch = epoch + 1
}
Automatic gradient computation and optimization
