def testDecodeExampleWithSparseTensorToDense(self):
np_indices = np.array([1, 2, 5])
np_values = np.array([0.1, 0.2, 0.6]).astype('f')
np_shape = np.array([6])
np_dense = np.array([0.0, 0.1, 0.2, 0.0, 0.0, 0.6]).astype('f')
example = example_pb2.Example(features=feature_pb2.Features(
feature={
'indices': self._EncodedInt64Feature(np_indices),
'values': self._EncodedFloatFeature(np_values),
}))
serialized_example = example.SerializeToString()
with self.test_session():
serialized_example = array_ops.reshape(serialized_example,
shape=[])
keys_to_features = {
'indices': parsing_ops.VarLenFeature(dtype=dtypes.int64),
'values': parsing_ops.VarLenFeature(dtype=dtypes.float32),
}
items_to_handlers = {
'labels':
tfexample_decoder.SparseTensor(shape=np_shape, densify=True),
}
decoder = tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handlers)
[tf_labels] = decoder.decode(serialized_example, ['labels'])
labels = tf_labels.eval()
self.assertAllClose(labels, np_dense)
def test_decode_example_with_sparse_tensor_with_given_shape(self):
np_indices = np.array([[1], [2], [5]])
np_values = np.array([0.1, 0.2, 0.6]).astype('f')
np_shape = np.array([6])
example = example_pb2.Example(features=feature_pb2.Features(
feature={
'indices': self._encode_int64_feature(np_indices),
'values': self._encode_float_feature(np_values),
}))
serialized_example = example.SerializeToString()
with self.test_session():
serialized_example = array_ops.reshape(serialized_example,
shape=[])
keys_to_features = {
'indices': parsing_ops.VarLenFeature(dtype=dtypes.int64),
'values': parsing_ops.VarLenFeature(dtype=dtypes.float32),
}
items_to_handlers = {
'labels': tfexample_decoder.SparseTensor(shape=np_shape),
}
decoder = TFExampleDecoder(keys_to_features, items_to_handlers)
[tf_labels] = decoder.decode(serialized_example, ['labels'])
labels = tf_labels.eval()
self.assertAllEqual(labels.indices, np_indices)
self.assertAllEqual(labels.values, np_values)
self.assertAllEqual(labels.dense_shape, np_shape)
