for i in range(epochs):
for j in range(nbatch):
start = j * batch_size
end = start + batch_size
inp = train_data[start:end]
tgt = train_targets[start:end]
res = my_step(inp, tgt)
running_losses.append(res.value)
running_hits.append(int(sum(res.hits)) / batch_size)
loss = sum(running_losses) / len(running_hits)
accuracy = sum(running_hits) / len(running_hits)
stats = [
f"E: {i + 1}/{epochs}",
f"B: {j + 1}/{nbatch}",
f"L: {loss:2.5f}",
f"A: {accuracy:.0%}",
]
if weight_stats:
data = tuple(zip(*res.wstat))
mx = max(data[0])
avg = sum(data[1]) / len(data[1])
mn = min(data[2])
stats.append(f"W: {mx:.4f} > {avg:.4f} > {mn:.4f}")
print(" -- ".join(stats))
if __name__ == "__main__":
auto_cli(train, category=cat.CliArgument, eval_env=globals())
def test_conflict():
with pytest.raises(ConflictError):
auto_cli(stout, (3, ),
category=cat.Argument,
argv="--z=3 --q=10".split())
def test_cli():
assert auto_cli(stout, (3, ), category=cat.Argument,
argv="--z=3".split()) == (7, 8)
assert auto_cli(stout, (3, ),
category=cat.Argument,
argv="--z=3 --w=10".split()) == (16, 8)
def test_unknown_argument():
with pytest.raises(SystemExit):
auto_cli(stout, (3, ), category=cat.Argument, argv="--x=4".split())
target: cat.CliArgument = default(random.randint(minimum, maximum))
assert minimum <= target <= maximum
print(f"> Please guess a number between {minimum} and {maximum}")
for i in range(maxtries):
guess = float(input())
if guess == target:
print("Yes! :D")
return True
elif i == maxtries - 1:
print("You failed :(")
return False
elif guess < target:
print("> Too low. Guess again.")
elif guess > target:
print("> Too high. Guess again.")
@ptera
def main():
# Number of rounds of guessing
rounds: cat.CliArgument = default(1)
for i in range(rounds):
guess()
if __name__ == "__main__":
auto_cli(main, description="Guessing game", category=cat.CliArgument)