def parser(record):
"""Dataset parser function.
Args:
record: A single serialized Example proto tensor.
Returns:
A tuple of:
* A dict of features ('patch' and 'weight')
* A label tensor (int64 scalar).
"""
feature_types = {
'patch':
tf.FixedLenFeature((patch_height, patch_width), tf.float32),
'label':
tf.FixedLenFeature((), tf.int64),
}
if FLAGS.use_included_label_weight:
feature_types['label_weight'] = tf.FixedLenFeature((), tf.float32)
features = tf.parse_single_example(record, feature_types)
label = features['label']
weight = label_weights.weights_from_labels(label)
if FLAGS.use_included_label_weight:
# Both operands must be the same type (float32).
weight = tf.to_float(weight) * tf.to_float(features['label_weight'])
patch = _augment(features['patch'])
return {'patch': patch, WEIGHT_COLUMN_NAME: weight}, label
def testWeightsFromLabels(self):
g = musicscore_pb2.Glyph
labels = tf.constant(
[g.NONE, g.NONE, g.NOTEHEAD_FILLED, g.SHARP, g.FLAT, g.NATURAL])
weights = 'NONE=0.1,NATURAL=2.0,SHARP=0.5,NOTEHEAD_FILLED=0.8'
weights_tensor = label_weights.weights_from_labels(labels, weights)
with self.test_session():
self.assertAllEqual([0.1, 0.1, 0.8, 0.5, 1.0, 2.0], weights_tensor.eval())