def fit(self):
printer = Printer()
while self.epoch < self.epochs:
self.new_epoch() # increments self.epoch
self.logger.set_mode("train")
stats = self.train_epoch(self.epoch)
self.logger.log(stats, self.epoch)
printer.print(stats, self.epoch, prefix="\ntrain: ")
if self.epoch % self.test_every_n_epochs == 0 or self.phase1_will_end(
) or self.phase2_will_end():
self.logger.set_mode("test")
stats = self.test_epoch(self.validdataloader)
self.logger.log(stats, self.epoch)
printer.print(stats, self.epoch, prefix="\nvalid: ")
if hasattr(self, "visdom"): self.visdom_log_test_run(stats)
if hasattr(self, "visdom"):
self.visdom.plot_epochs(self.logger.get_data())
self.check_events()
# stores all stored values in the rootpath of the logger
#if self.save_logger:
self.logger.save()
return self.logger.data
def fit(self):
printer = Printer()
while self.epoch < self.epochs:
self.new_epoch() # increments self.epoch
self.logger.set_mode("train")
stats = self.train_epoch(self.epoch)
self.logger.log(stats, self.epoch)
printer.print(stats,
self.epoch,
prefix="\n" +
self.traindataloader.dataset.partition + ": ")
if self.epoch % self.test_every_n_epochs == 0 or self.epoch == 1:
self.logger.set_mode("test")
stats = self.test_epoch(self.validdataloader)
self.logger.log(stats, self.epoch)
printer.print(stats,
self.epoch,
prefix="\n" +
self.validdataloader.dataset.partition + ": ")
if self.visdom is not None:
self.visdom_log_test_run(stats)
if self.visdom is not None:
self.visdom.plot_epochs(self.logger.get_data())
if self.checkpoint_every_n_epochs % self.epoch == 0:
print("Saving model to {}".format(self.get_model_name()))
self.snapshot(self.get_model_name())
print("Saving log to {}".format(self.get_log_name()))
self.logger.get_data().to_csv(self.get_log_name())
if self.epoch > self.early_stopping_smooth_period and self.check_for_early_stopping(
smooth_period=self.early_stopping_smooth_period):
print()
print(
f"Model did not improve in the last {self.early_stopping_smooth_period} epochs. stopping training..."
)
print("Saving model to {}".format(self.get_model_name()))
self.snapshot(self.get_model_name())
print("Saving log to {}".format(self.get_log_name()))
self.logger.get_data().to_csv(self.get_log_name())
return self.logger
return self.logger
def fit(self):
printer = Printer()
while self.epoch < self.epochs:
self.new_epoch() # increments self.epoch
self.logger.set_mode("train")
stats = self.train_epoch(self.epoch)
self.logger.log(stats, self.epoch)
printer.print(stats, self.epoch, prefix="\ntrain: ")
if self.epoch % self.test_every_n_epochs == 0 or self.phase1_will_end(
) or self.phase2_will_end():
self.logger.set_mode("test")
stats = self.test_epoch(self.epoch)
self.logger.log(stats, self.epoch)
printer.print(stats, self.epoch, prefix="\nvalid: ")
self.visdom_log_test_run(stats)
self.visdom.plot_epochs(self.logger.get_data())
self.check_events()
return self.logger.data
def fit(self, epochs):
printer = Printer()
while self.epoch < epochs:
self.new_epoch() # increments self.epoch
self.logger.set_mode("train")
stats = self.train_epoch(self.epoch)
self.logger.log(stats, self.epoch)
printer.print(stats, self.epoch, prefix="\ntrain: ")
if self.epoch % self.test_every_n_epochs == 0:
self.logger.set_mode("test")
stats = self.test_epoch(self.testdataloader)
self.logger.log(stats, self.epoch)
printer.print(stats, self.epoch, prefix="\ntest: ")
if hasattr(self, "visdom"): self.visdom_log_test_run(stats)
if hasattr(self, "visdom"):
self.visdom.plot_epochs(self.logger.get_data())
self.logger.save()
return self.logger.data