def test_parse_input_fn_result_invalid(self):
def _input_fn():
features = np.expand_dims(np.arange(100), 0)
labels = np.expand_dims(np.arange(100, 200), 0)
return dataset_ops.Dataset.from_tensor_slices((features, labels, labels))
with self.assertRaisesRegexp(ValueError, 'input_fn should return'):
util.parse_input_fn_result(_input_fn())
def test_parse_input_fn_result_invalid(self):
def _input_fn():
features = np.expand_dims(np.arange(100), 0)
labels = np.expand_dims(np.arange(100, 200), 0)
return dataset_ops.Dataset.from_tensor_slices(
(features, labels, labels))
with self.assertRaisesRegexp(ValueError, 'input_fn should return'):
util.parse_input_fn_result(_input_fn())
def build_supervised_input_receiver_fn_from_input_fn(input_fn, **input_fn_args):
"""Get a function that returns a SupervisedInputReceiver matching an input_fn.
Note that this function calls the input_fn in a local graph in order to
extract features and labels. Placeholders are then created from those
features and labels in the default graph.
Args:
input_fn: An Estimator input_fn, which is a function that returns one of:
* A 'tf.data.Dataset' object: Outputs of `Dataset` object must be a
tuple (features, labels) with same constraints as below.
* A tuple (features, labels): Where `features` is a `Tensor` or a
dictionary of string feature name to `Tensor` and `labels` is a
`Tensor` or a dictionary of string label name to `Tensor`. Both
`features` and `labels` are consumed by `model_fn`. They should
satisfy the expectation of `model_fn` from inputs.
**input_fn_args: set of kwargs to be passed to the input_fn. Note that
these will not be checked or validated here, and any errors raised by
the input_fn will be thrown to the top.
Returns:
A function taking no arguments that, when called, returns a
SupervisedInputReceiver. This function can be passed in as part of the
input_receiver_map when exporting SavedModels from Estimator with multiple
modes.
"""
# Wrap the input_fn call in a graph to prevent sullying the default namespace
with ops.Graph().as_default():
result = input_fn(**input_fn_args)
features, labels, _ = util.parse_input_fn_result(result)
# Placeholders are created back in the default graph.
return build_raw_supervised_input_receiver_fn(features, labels)
def build_supervised_input_receiver_fn_from_input_fn(input_fn,
**input_fn_args):
"""Get a function that returns a SupervisedInputReceiver matching an input_fn.
Note that this function calls the input_fn in a local graph in order to
extract features and labels. Placeholders are then created from those
features and labels in the default graph.
Args:
input_fn: An Estimator input_fn, which is a function that returns one of:
* A 'tf.data.Dataset' object: Outputs of `Dataset` object must be a
tuple (features, labels) with same constraints as below.
* A tuple (features, labels): Where `features` is a `Tensor` or a
dictionary of string feature name to `Tensor` and `labels` is a
`Tensor` or a dictionary of string label name to `Tensor`. Both
`features` and `labels` are consumed by `model_fn`. They should
satisfy the expectation of `model_fn` from inputs.
**input_fn_args: set of kwargs to be passed to the input_fn. Note that
these will not be checked or validated here, and any errors raised by
the input_fn will be thrown to the top.
Returns:
A function taking no arguments that, when called, returns a
SupervisedInputReceiver. This function can be passed in as part of the
input_receiver_map when exporting SavedModels from Estimator with multiple
modes.
"""
# Wrap the input_fn call in a graph to prevent sullying the default namespace
with ops.Graph().as_default():
result = input_fn(**input_fn_args)
features, labels, _ = util.parse_input_fn_result(result)
# Placeholders are created back in the default graph.
return build_raw_supervised_input_receiver_fn(features, labels)
def test_parse_input_fn_result_features_only(self):
def _input_fn():
return constant_op.constant(np.arange(100))
features, labels, hooks = util.parse_input_fn_result(_input_fn())
with self.cached_session() as sess:
vals = sess.run([features])
self.assertAllEqual(vals[0], np.arange(100))
self.assertEqual(labels, None)
self.assertEqual(hooks, [])
def test_parse_input_fn_result_features_only(self):
def _input_fn():
return constant_op.constant(np.arange(100))
features, labels, hooks = util.parse_input_fn_result(_input_fn())
with self.test_session() as sess:
vals = sess.run([features])
self.assertAllEqual(vals[0], np.arange(100))
self.assertEqual(labels, None)
self.assertEqual(hooks, [])
def test_parse_input_fn_result_features_only_dataset(self):
def _input_fn():
features = np.expand_dims(np.arange(100), 0)
return dataset_ops.Dataset.from_tensor_slices(features)
features, labels, hooks = util.parse_input_fn_result(_input_fn())
with training.MonitoredSession(hooks=hooks) as sess:
vals = sess.run([features])
self.assertAllEqual(vals[0], np.arange(100))
self.assertEqual(labels, None)
self.assertIsInstance(hooks[0], util._DatasetInitializerHook)
def test_parse_input_fn_result_features_only_dataset(self):
def _input_fn():
features = np.expand_dims(np.arange(100), 0)
return dataset_ops.Dataset.from_tensor_slices(features)
features, labels, hooks = util.parse_input_fn_result(_input_fn())
with training.MonitoredSession(hooks=hooks) as sess:
vals = sess.run([features])
self.assertAllEqual(vals[0], np.arange(100))
self.assertEqual(labels, None)
self.assertIsInstance(hooks[0], util._DatasetInitializerHook)
def test_parse_input_fn_result_tuple(self):
def _input_fn():
features = constant_op.constant(np.arange(100))
labels = constant_op.constant(np.arange(100, 200))
return features, labels
features, labels, hooks = util.parse_input_fn_result(_input_fn())
with self.cached_session() as sess:
vals = sess.run([features, labels])
self.assertAllEqual(vals[0], np.arange(100))
self.assertAllEqual(vals[1], np.arange(100, 200))
self.assertEqual(hooks, [])
def test_parse_input_fn_result_tuple(self):
def _input_fn():
features = constant_op.constant(np.arange(100))
labels = constant_op.constant(np.arange(100, 200))
return features, labels
features, labels, hooks = util.parse_input_fn_result(_input_fn())
with self.test_session() as sess:
vals = sess.run([features, labels])
self.assertAllEqual(vals[0], np.arange(100))
self.assertAllEqual(vals[1], np.arange(100, 200))
self.assertEqual(hooks, [])
