def train_left_fit_by_generator(self):
self.trn_gen = generator.DataGenerator(self.train_x, self.train_y,
**self.params)
model = self.model
accept_cur = False
cur_epoch = self.decide_epoch_curround()
if self.fullvalid_stage:
m_history = model.fit_generator(
self.trn_gen,
steps_per_epoch=int(
len(self.train_x) // self.params["batch_size"]),
validation_data=self.g_valid_gen,
epochs=cur_epoch,
max_queue_size=10,
callbacks=self.callbacks,
use_multiprocessing=False,
workers=1,
verbose=ThinRes34Config.VERBOSE,
)
cur_valid_loss = round(m_history.history.get("val_loss")[-1], 6)
cur_valid_acc = round(m_history.history.get("val_acc")[-1], 6)
self.g_val_loss_list.append(cur_valid_loss)
self.g_val_acc_list.append(cur_valid_acc)
else:
self.trn_gen = generator.DataGenerator(self.train_x, self.train_y,
**self.params)
m_history = model.fit_generator(
self.trn_gen,
steps_per_epoch=int(
len(self.train_x) // self.params["batch_size"]),
epochs=cur_epoch,
max_queue_size=10,
callbacks=self.callbacks,
use_multiprocessing=False,
workers=1,
verbose=ThinRes34Config.VERBOSE,
)
cur_valid_loss = 100
cur_valid_acc = -1
cur_train_loss = round(m_history.history.get("loss")[-1], 6)
cur_train_acc = round(m_history.history.get("acc")[-1], 6)
cur_lr = m_history.history.get("lr")[-1]
self.g_train_loss_list.append(cur_train_loss)
self.g_train_acc_list.append(cur_train_acc)
self.g_his_eval_dict[self.round_idx] = {
"t_loss": cur_train_loss,
"t_acc": cur_train_acc,
"v_loss": cur_valid_loss,
"v_acc": cur_valid_acc
}
accept_cur = self.train_bestmodel_decision()
if self.fullvalid_stage:
self.g_accept_cur_list.append(accept_cur)
return model, accept_cur
def decide_if_full_valid(self):
if self.round_idx == self.tr34_mconfig.FULL_VAL_R_START:
self.accpet_loss_list = [100]
self.imp_feat_args["mode"] = "train"
self.g_valid_x_features = ut.pre_trans_wav_update(self.g_valid_x, self.imp_feat_args)
self.g_valid_gen = generator.DataGenerator(self.g_valid_x_features, self.g_valid_y, **self.params)
if self.round_idx > self.tr34_mconfig.FULL_VAL_R_START:
return True
else:
return False
def train_fit_first_by_generator(self):
self.trn_gen = generator.DataGenerator(self.train_x, self.train_y, **self.params)
self.first_r_train_x = self.train_x
self.first_r_data_generator = self.trn_gen
cur_epoch = self.decide_epoch_curround()
early_stopping = TerminateOnBaseline(monitor="acc", baseline=0.999)
self.model.fit_generator(
self.first_r_data_generator,
steps_per_epoch=int(len(self.first_r_train_x) // self.params["batch_size"] // 2),
epochs=cur_epoch,
max_queue_size=10,
callbacks=self.callbacks + [early_stopping],
use_multiprocessing=False,
workers=1,
verbose=ThinRes34Config.VERBOSE,
)
return