def eval_batch(self, session, mtest, batch, batch_size, get_probs=False):
batch_size_original = len(batch)
ids = tf_utils.make_array(
[item['ids'] for item in batch] + \
[batch[0]['ids'] for _ in xrange(batch_size - len(batch))],
length=self.memory_size)
ids_lengths = np.array(
[len(item['ids']) for item in batch] + \
[len(batch[0]['ids']) for _ in xrange(batch_size - len(batch))])
labels = tf_utils.make_array(
[item['labels'] for item in batch] + \
[batch[0]['labels'] for _ in xrange(batch_size - len(batch))])
labels = labels[:, list(self.label_types)]
if get_probs:
all_probs = session.run(
mtest.all_probs,
tf_utils.filter_none_keys({
mtest.ids: ids,
mtest.ids_lengths: ids_lengths,
mtest.labels: labels,
}))
return all_probs[:batch_size_original]
(preds, ) = session.run([mtest.preds],
tf_utils.filter_none_keys({
mtest.ids: ids,
mtest.ids_lengths: ids_lengths,
mtest.labels: labels,
}))
preds = preds[:batch_size_original]
labels = labels[:batch_size_original]
correct = (preds == labels)
incorrects = []
for i, e in enumerate(correct):
if (False in e):
incorrects.append({
'ids': ids[i],
'preds': preds[i],
'labels': labels[i]
})
if self.label_types == [0, 1, 2, 3]:
correct = np.concatenate(
(correct, np.all(correct[:, [0, 2]], axis=1)[:, np.newaxis],
np.all(correct[:, [1, 3]], axis=1)[:, np.newaxis]),
axis=1)
return correct, len(preds), preds, incorrects
def train_feed_dict(self, m, data):
ids, ids_lengths, labels, expanded_labels = data
feed_dict = tf_utils.filter_none_keys({
m.ids:
ids,
m.ids_lengths:
ids_lengths,
m.labels:
labels,
m.expanded_labels:
expanded_labels
})
return feed_dict
def pred_one(self, session, mtest, batch, batch_size, get_probs=False):
batch_size_original = len(batch)
ids = tf_utils.make_array(
[item['ids'] for item in batch] + \
[batch[0]['ids'] for _ in xrange(batch_size - len(batch))],
length=self.memory_size)
ids_lengths = np.array(
[len(item['ids']) for item in batch] + \
[len(batch[0]['ids']) for _ in xrange(batch_size - len(batch))])
labels = tf_utils.make_array(
[item['labels'] for item in batch] + \
[batch[0]['labels'] for _ in xrange(batch_size - len(batch))])
labels = labels[:, list(self.label_types)]
(preds, ) = session.run([mtest.preds],
tf_utils.filter_none_keys({
mtest.ids: ids,
mtest.ids_lengths: ids_lengths,
mtest.labels: labels,
}))
preds = preds[:batch_size_original]
return preds