def test_input_receiver_raw_values(self):
"""Tests that no errors are raised when input is expected."""
features = {
"feature0":
tf.constant([0]),
u"feature1":
tf.constant([1]),
"feature2":
tf.sparse.SparseTensor(
indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
42: # ints are allowed in the `features` dict
tf.constant([3]),
}
labels = {
"classes": tf.constant([0] * 100),
43: # ints are allowed in the `labels` dict
tf.constant([3]),
}
receiver_tensors = {
"example0": tf.constant(["test0"], name="example0"),
u"example1": tf.constant(["test1"], name="example1"),
}
rec = export.SupervisedInputReceiver(features["feature2"], labels,
receiver_tensors)
self.assertIsInstance(rec.features, tf.sparse.SparseTensor)
rec = export.SupervisedInputReceiver(features, labels["classes"],
receiver_tensors)
self.assertIsInstance(rec.labels, tf.Tensor)
def test_input_receiver_receiver_tensors_invalid(self):
features = {
"feature0":
tf.constant([0]),
u"feature1":
tf.constant([1]),
"feature2":
tf.sparse.SparseTensor(
indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
}
labels = tf.constant([0])
with self.assertRaisesRegexp(ValueError,
"receiver_tensors must be defined"):
export.SupervisedInputReceiver(
features=features, labels=labels, receiver_tensors=None)
with self.assertRaisesRegexp(ValueError,
"receiver_tensor keys must be strings"):
export.SupervisedInputReceiver(
features=features,
labels=labels,
receiver_tensors={1: tf.constant(["test"], name="example0")})
with self.assertRaisesRegexp(ValueError,
"receiver_tensor example1 must be a Tensor"):
export.SupervisedInputReceiver(
features=features, labels=labels, receiver_tensors={"example1": [1]})
def test_input_receiver_raw_values(self):
"""Tests that no errors are raised when input is expected."""
features = {
"feature0":
constant_op.constant([0]),
u"feature1":
constant_op.constant([1]),
"feature2":
sparse_tensor.SparseTensor(indices=[[0, 0]],
values=[1],
dense_shape=[1, 1]),
}
labels = {
"classes": constant_op.constant([0] * 100),
}
receiver_tensors = {
"example0": array_ops.placeholder(dtypes.string, name="example0"),
u"example1": array_ops.placeholder(dtypes.string, name="example1"),
}
rec = export.SupervisedInputReceiver(features["feature2"], labels,
receiver_tensors)
self.assertIsInstance(rec.features, sparse_tensor.SparseTensor)
rec = export.SupervisedInputReceiver(features, labels["classes"],
receiver_tensors)
self.assertIsInstance(rec.labels, ops.Tensor)
def test_multi_feature_multi_receiver(self):
features = {"foo": constant_op.constant(5),
"bar": constant_op.constant(6)}
labels = {"value": constant_op.constant(5)}
receiver_tensors = {"baz": constant_op.constant(5),
"qux": constant_op.constant(6)}
_ = export.SupervisedInputReceiver(features, labels, receiver_tensors)
def test_multi_feature_single_receiver(self):
features = {
"foo": constant_op.constant(5),
"bar": constant_op.constant(6)
}
labels = {"value": constant_op.constant(5)}
receiver_tensor = constant_op.constant(["test"])
_ = export.SupervisedInputReceiver(features, labels, receiver_tensor)
def test_multi_feature_single_receiver(self):
features = {
"foo": constant_op.constant(5),
"bar": constant_op.constant(6)
}
labels = {"value": constant_op.constant(5)}
receiver_tensor = array_ops.placeholder(dtypes.string)
_ = export.SupervisedInputReceiver(features, labels, receiver_tensor)
def test_single_feature_single_receiver(self):
feature = tf.constant(5)
label = tf.constant(5)
receiver_tensor = tf.constant(["test"])
input_receiver = export.SupervisedInputReceiver(feature, label,
receiver_tensor)
# single receiver is automatically named
receiver_key, = input_receiver.receiver_tensors.keys()
self.assertEqual("input", receiver_key)
def test_single_feature_single_receiver(self):
feature = constant_op.constant(5)
label = constant_op.constant(5)
receiver_tensor = array_ops.placeholder(dtypes.string)
input_receiver = export.SupervisedInputReceiver(
feature, label, receiver_tensor)
# single receiver is automatically named
receiver_key, = input_receiver.receiver_tensors.keys()
self.assertEqual("input", receiver_key)
def test_multi_feature_multi_receiver(self):
features = {
"foo": constant_op.constant(5),
"bar": constant_op.constant(6)
}
labels = {"value": constant_op.constant(5)}
receiver_tensors = {
"baz": array_ops.placeholder(dtypes.int64),
"qux": array_ops.placeholder(dtypes.float32)
}
_ = export.SupervisedInputReceiver(features, labels, receiver_tensors)
def test_input_receiver_constructor(self):
"""Tests that no errors are raised when input is expected."""
features = {
"feature0": constant_op.constant([0]),
u"feature1": constant_op.constant([1]),
"feature2": sparse_tensor.SparseTensor(
indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
}
labels = {
"classes": constant_op.constant([0] * 100),
}
receiver_tensors = {
"example0": constant_op.constant(["test0"], name="example0"),
u"example1": constant_op.constant(["test1"], name="example1"),
}
export.SupervisedInputReceiver(features, labels, receiver_tensors)
def test_input_receiver_features_invalid(self):
features = constant_op.constant([0] * 100)
labels = constant_op.constant([0])
receiver_tensors = {
"example0": constant_op.constant(["test0"], name="example0"),
u"example1": constant_op.constant(["test1"], name="example1"),
}
with self.assertRaisesRegexp(ValueError, "features must be defined"):
export.SupervisedInputReceiver(
features=None,
labels=labels,
receiver_tensors=receiver_tensors)
with self.assertRaisesRegexp(ValueError, "feature keys must be strings"):
export.SupervisedInputReceiver(
features={1: constant_op.constant([1])},
labels=labels,
receiver_tensors=receiver_tensors)
with self.assertRaisesRegexp(ValueError, "label keys must be strings"):
export.SupervisedInputReceiver(
features=features,
labels={1: constant_op.constant([1])},
receiver_tensors=receiver_tensors)
with self.assertRaisesRegexp(
ValueError, "feature feature1 must be a Tensor or SparseTensor"):
export.SupervisedInputReceiver(
features={"feature1": [1]},
labels=labels,
receiver_tensors=receiver_tensors)
with self.assertRaisesRegexp(
ValueError, "feature must be a Tensor or SparseTensor"):
export.SupervisedInputReceiver(
features=[1],
labels=labels,
receiver_tensors=receiver_tensors)
with self.assertRaisesRegexp(
ValueError, "label must be a Tensor or SparseTensor"):
export.SupervisedInputReceiver(
features=features,
labels=100,
receiver_tensors=receiver_tensors)
def test_feature_labeled_tensor(self): feature = LabeledTensorMock() label = tf.constant(5) receiver_tensor = tf.constant(["test"]) _ = export.SupervisedInputReceiver(feature, label, receiver_tensor)
def test_feature_labeled_tensor(self):
feature = LabeledTensorMock()
label = constant_op.constant(5)
receiver_tensor = array_ops.placeholder(dtypes.string)
_ = export.SupervisedInputReceiver(feature, label, receiver_tensor)