def loop(model: Layer, images: List[Tensor], labels: List[Tensor], loss: Loss, optimizer: Optimizer = None) -> None: correct = 0 # Track number of correct predictions. total_loss = 0.0 # Track total loss. with tqdm.trange(len(images)) as t: for i in t: predicted = model.forward(images[i]) # Predict. if argmax(predicted) == argmax(labels[i]): # Check for correct += 1 # correctness. total_loss += loss.loss(predicted, labels[i]) # Compute loss. # If we're training, backpropagate gradient and update weights. if optimizer is not None: gradient = loss.gradient(predicted, labels[i]) model.backward(gradient) optimizer.step(model) # And update our metrics in the progress bar. avg_loss = total_loss / (i + 1) acc = correct / (i + 1) t.set_description(f"mnist loss: {avg_loss:.3f} acc: {acc:.3f}")
def loop(model: Layer, images: List[Tensor], labels: List[Tensor], loss: Loss, optimizer: Optimizer = None) -> None: correct = 0 # Przechowuje liczbę poprawnych przewidywań. total_loss = 0.0 # Przechowuje całkowitą stratę. with tqdm.trange(len(images)) as t: for i in t: predicted = model.forward( images[i]) # Określ wartości przewidywane. if argmax(predicted) == argmax( labels[i]): # Sprawdź poprawność. correct += 1 total_loss += loss.loss(predicted, labels[i]) # Oblicz stratę. # Podczas treningu propaguj wstecznie gradient i zaktualizuj wagi. if optimizer is not None: gradient = loss.gradient(predicted, labels[i]) model.backward(gradient) optimizer.step(model) # Zaktualizuj metryki na pasku postępu. avg_loss = total_loss / (i + 1) acc = correct / (i + 1) t.set_description(f"mnist loss: {avg_loss:.3f} acc: {acc:.3f}")
def fizzbuzz_accuracy(low: int, hi: int, net: Layer) -> float: num_correct = 0 for n in range(low, hi): x = binary_encode(n) predicted = argmax(net.forward(x)) actual = argmax(fizz_buzz_encode(n)) if predicted == actual: num_correct += 1 return num_correct / (hi - low)