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 # 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)
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)