def input_fn(labeled: DataSet, unlabeled: DataSet, labeled_size,
unlabeled_size):
input_dict = {}
# labeled data
labeled = labeled.get_batch(labeled_size)
input_dict['labeled_inputs'] = tf.constant(np.array(labeled.inputs()))
input_dict['labeled_sequence_length'] = tf.constant(labeled.lengths())
input_dict['labeled_mask'] = tf.constant(labeled.masks())
labels = tf.constant(labeled.labels())
# unlabeled data
unlabeled = unlabeled.get_batch(unlabeled_size)
input_dict['unlabeled_inputs'] = tf.constant(
np.array(unlabeled.inputs()))
input_dict['unlabeled_sequence_length'] = tf.constant(
unlabeled.lengths())
input_dict['unlabeled_mask'] = tf.constant(unlabeled.masks())
return input_dict, labels
def input_fn(data_set: DataSet, size):
input_dict = {}
# labeled data
data_set = data_set.get_batch(size)
input_dict['inputs'] = tf.constant(np.array(data_set.inputs()))
input_dict['sequence_length'] = tf.constant(data_set.lengths())
input_dict['mask'] = tf.constant(data_set.masks())
labels = tf.constant(data_set.labels())
return input_dict, labels
def input_fn(labeled: DataSet, unlabeled: DataSet = None, size: int = BATCH_SIZE):
input_dict = {
}
if unlabeled is not None and unlabeled.size() == 0:
unlabeled = None
# labeled data
labeled = labeled.get_batch(size)
input_dict['labeled_inputs'] = tf.constant(np.array(labeled.inputs()))
input_dict['labeled_sequence_length'] = tf.constant(labeled.lengths())
input_dict['labeled_mask'] = tf.constant(labeled.masks())
labels = tf.constant(labeled.labels())
# unlabeled data
unlabeled = unlabeled is None and labeled or unlabeled.get_batch(labeled.size())
input_dict['unlabeled_inputs'] = tf.constant(np.array(unlabeled.inputs()))
input_dict['unlabeled_sequence_length'] = tf.constant(unlabeled.lengths())
input_dict['unlabeled_mask'] = tf.constant(unlabeled.masks())
input_dict['unlabeled_size'] = tf.constant(unlabeled.size())
input_dict['unlabeled_target'] = tf.constant(unlabeled.labels())
return input_dict, labels
def run(cls, dev, test, labeled_slot, labeled_train, unlabeled_slot,
unlabeled_train, steps, gpu_memory):
training_set = DataSet(labeled_slot, labeled_train)
validation_set = DataSet(labeled_slot, dev)
test_set = DataSet(labeled_slot, test)
unlabeled_set = DataSet(unlabeled_slot, unlabeled_train)
print('# training_set (%d)' % training_set.size())
print('# validation_set (%d)' % validation_set.size())
print('# test_set (%d)' % test_set.size())
print('# unlabeled_set (%d)' % unlabeled_set.size())
classifier = tf.contrib.learn.Estimator(
model_fn=SlotFilling.rnn_model_fn,
params={
'num_slot': training_set.num_classes(),
'num_pos': unlabeled_set.num_classes(),
'drop_out': DROP_OUT,
'embedding_dimension': EMBEDDING_DIMENSION,
'vocab_size': DataSet.vocab_size(),
'unlabeled': unlabeled_set.size() > 0
},
config=tf.contrib.learn.RunConfig(
gpu_memory_fraction=gpu_memory,
save_checkpoints_secs=30,
),
model_dir='./model')
validation_metrics = {
"accuracy":
tf.contrib.learn.MetricSpec(
metric_fn=tf.contrib.metrics.streaming_accuracy,
prediction_key='predictions',
weight_key='labeled_mask')
}
monitor = tf.contrib.learn.monitors.ValidationMonitor(
input_fn=lambda: SlotFilling.input_fn(
validation_set, unlabeled_set, validation_set.size(), 1),
eval_steps=1,
every_n_steps=50,
metrics=validation_metrics,
early_stopping_metric="loss",
early_stopping_metric_minimize=True,
early_stopping_rounds=300)
classifier.fit(input_fn=lambda: SlotFilling.input_fn(
training_set, unlabeled_set, training_set.size(), 500),
monitors=[monitor],
steps=steps)
predictions = classifier.predict(input_fn=lambda: SlotFilling.input_fn(
test_set, unlabeled_set, test_set.size(), 1))
slot_correct = 0
slot_no_match = 0
slot_mismatch = 0
slot_over_match = 0
for i, p in enumerate(predictions):
target = test_set.labels()[i][:test_set.lengths()[i]]
prediction = list(p['predictions'])[:test_set.lengths()[i]]
for expected, actual in zip(target, prediction):
actual = int(actual)
if expected is actual:
slot_correct += 1
elif test_set.get_slot(actual) is 'o':
slot_no_match += 1
elif test_set.get_slot(expected) is 'o':
slot_over_match += 1
else:
slot_mismatch += 1
return {
'accuracy': slot_correct / sum(test_set.lengths()),
'correct': slot_correct,
'no_match': slot_no_match,
'mismatch': slot_mismatch,
'over_match': slot_over_match,
}
