def _time_metric_writer(logdir): return summary.SummaryWriter(logdir, filename_base="9999999999.time")
def _image_writer(logdir, digest): timestamp = _next_tfevent_timestamp(logdir) return summary.SummaryWriter(logdir, filename_base="%0.10d.image" % timestamp, filename_suffix="." + digest)
from guild import summary with summary.SummaryWriter(".") as writer: writer.add_scalar("x", 1.123, step=1) writer.add_scalar("x", 2.234, step=2) writer.add_scalar("x", float("inf"), step=3) writer.add_scalar("y", float("-inf"), step=1) writer.add_scalar("y", 1, step=2) writer.add_scalar("y", 2, step=3) writer.add_scalar("z", float("nan"), step=1)
def _hparams_writer(logdir): return summary.SummaryWriter(logdir, filename_base="0000000000.hparams")
import numpy as np from guild import summary noise = 0.1 def f(x): return np.sin(5 * x) * (1 - np.tanh(x**2)) + np.random.randn() * noise min_loss = None writer = summary.SummaryWriter(".") for step, x in enumerate(np.arange(-3.0, 3.0, 0.1)): loss = f(x) min_loss = min(loss, min_loss) if min_loss is not None else loss writer.add_scalar("loss", loss, step) writer.close() print("min_loss: %f" % min_loss)