def make_example_dict(example_protos, example_weights):
def parse_examples(example_protos):
features = {
'target': tf.FixedLenFeature(shape=[1],
dtype=tf.float32,
default_value=0),
'age_indices': tf.VarLenFeature(dtype=tf.int64),
'age_values': tf.VarLenFeature(dtype=tf.float32),
'gender_indices': tf.VarLenFeature(dtype=tf.int64),
'gender_values': tf.VarLenFeature(dtype=tf.float32)
}
return tf.parse_example(
[e.SerializeToString() for e in example_protos], features)
parsed = parse_examples(example_protos)
sparse_features = [
SparseFeatureColumn(
tf.reshape(
tf.split(1, 2, parsed['age_indices'].indices)[0], [-1]),
tf.reshape(parsed['age_indices'].values, [-1]),
tf.reshape(parsed['age_values'].values, [-1])), SparseFeatureColumn(
tf.reshape(
tf.split(1, 2, parsed['gender_indices'].indices)[0], [-1]),
tf.reshape(parsed['gender_indices'].values, [-1]),
tf.reshape(parsed['gender_values'].values, [-1]))
]
return dict(sparse_features=sparse_features,
dense_features=[],
example_weights=example_weights,
example_labels=tf.reshape(parsed['target'], [-1]),
example_ids=['%d' % i for i in range(0, len(example_protos))])
def testBasic(self):
expected_example_indices = [1, 1, 1, 2]
expected_feature_indices = [0, 1, 2, 0]
sfc = SparseFeatureColumn(expected_example_indices,
expected_feature_indices, None)
self.assertTrue(isinstance(sfc.example_indices, tf.Tensor))
self.assertTrue(isinstance(sfc.feature_indices, tf.Tensor))
self.assertEqual(sfc.feature_values, None)
with self._single_threaded_test_session():
self.assertAllEqual(expected_example_indices, sfc.example_indices.eval())
self.assertAllEqual(expected_feature_indices, sfc.feature_indices.eval())
expected_feature_values = [1.0, 2.0, 3.0, 4.0]
sfc = SparseFeatureColumn([1, 1, 1, 2], [0, 1, 2, 0],
expected_feature_values)
with self._single_threaded_test_session():
self.assertAllEqual(expected_feature_values, sfc.feature_values.eval())