def show(self):
self._update_phrases_counts()
# print progress-bar off known_words
progressBar(self._counters[Level.HIGH], self.all_count, magenta, 80)
# count high
View.print_title('Seen: ' + str(self.seen) + ' High: ' +
str(self._counters[Level.HIGH]) + ' Mid: ' +
str(self._counters[Level.MID]) + ' Low: ' +
str(self._counters[Level.LOW]) + ' Unknown: ' +
str(self._counters[Level.UNKNOWN]))
batch_elmo_inp,
targets=batch_labels)
model.train()
batch_labels = batch_labels.cpu().detach().numpy()
batch_lengths = batch_lengths.cpu().detach().numpy()
ncorr, ntotal = batch_accuracy_func(batch_predictions,
batch_labels,
batch_lengths)
batch_acc = ncorr / ntotal
train_acc += batch_acc
# update progress
progressBar(
batch_id + 1,
int(np.ceil(len(train_data) / TRAIN_BATCH_SIZE)), [
"batch_time", "batch_loss", "avg_batch_loss",
"batch_acc", "avg_batch_acc"
], [
time.time() - st_time, batch_loss, train_loss /
(batch_id + 1), batch_acc, train_acc / (batch_id + 1)
])
print(f"\nEpoch {epoch_id} train_loss: {train_loss/(batch_id+1)}")
# valid loss
valid_loss = 0.
valid_acc = 0.
print("valid_data size: {}".format(len(valid_data)))
valid_data_iter = batch_iter(valid_data,
batch_size=VALID_BATCH_SIZE,
shuffle=False)
for batch_id, (batch_labels,
batch_sentences) in enumerate(valid_data_iter):