def train(net: NeuralNet, inputs: Tensor, targets: Tensor, num_epochs: int = 5000, iterator: DataIterator = BatchIterator(), loss: Loss = MSE(), optimizer: Optimizer = SGD() ) -> None: for epoch in range(num_epochs): epoch_loss = 0.0 for batch in iterator(inputs, targets): predicted = net.forward(batch.inputs) epoch_loss += loss.loss(predicted, batch.targets) grad = loss.grad(predicted, batch.targets) net.backward(grad) optimizer.step(net) print(epoch, epoch_loss)