vocab_size=dtgen.tokenizer.vocab_size)
model.compile(learning_rate=0.001)
model.load_checkpoint(target=target_path)
if args.train:
model.summary(output_path, "summary.txt")
callbacks = model.get_callbacks(logdir=output_path,
checkpoint=target_path,
verbose=1)
start_time = time.time()
h = model.fit(x=dtgen.next_train_batch(),
epochs=args.epochs,
steps_per_epoch=dtgen.steps['train'],
validation_data=dtgen.next_valid_batch(),
validation_steps=dtgen.steps['valid'],
callbacks=callbacks,
shuffle=True,
verbose=1)
total_time = time.time() - start_time
loss = h.history['loss']
val_loss = h.history['val_loss']
min_val_loss = min(val_loss)
min_val_loss_i = val_loss.index(min_val_loss)
time_epoch = (total_time / len(loss))
total_item = (dtgen.size['train'] + dtgen.size['valid'])
t_corpus = "\n".join([
model.compile(learning_rate=0.001)
model.load_checkpoint(target=target_path)
if args.train:
model.summary(output_path, "summary.txt")
callbacks = model.get_callbacks(logdir=output_path,
checkpoint=target_path,
verbose=1)
start_time = datetime.datetime.now()
h = model.fit(x=ds.getNext().imgs,
epochs=args.epochs,
steps_per_epoch=ds.train_steps,
validation_data=ds.getNext(),
validation_steps=ds.valid_steps,
callbacks=callbacks,
shuffle=True,
verbose=1)
total_time = datetime.datetime.now() - start_time
loss = h.history['loss']
val_loss = h.history['val_loss']
min_val_loss = min(val_loss)
min_val_loss_i = val_loss.index(min_val_loss)
time_epoch = (total_time / len(loss))
total_item = (len(ds.samples))
