def test_build_raw_supervised_input_receiver_fn_raw_tensors(self):
features = {
"feature_1": constant_op.constant(["hello"]),
"feature_2": constant_op.constant([42])
}
labels = {
"foo": constant_op.constant([5]),
"bar": constant_op.constant([6])
}
input_receiver_fn1 = export.build_raw_supervised_input_receiver_fn(
features["feature_1"], labels)
input_receiver_fn2 = export.build_raw_supervised_input_receiver_fn(
features["feature_1"], labels["foo"])
with ops.Graph().as_default():
input_receiver = input_receiver_fn1()
self.assertIsInstance(input_receiver.features, ops.Tensor)
self.assertEqual(set(["foo", "bar"]),
set(input_receiver.labels.keys()))
self.assertEqual(set(["input", "foo", "bar"]),
set(input_receiver.receiver_tensors.keys()))
input_receiver = input_receiver_fn2()
self.assertIsInstance(input_receiver.features, ops.Tensor)
self.assertIsInstance(input_receiver.labels, ops.Tensor)
self.assertEqual(set(["input", "label"]),
set(input_receiver.receiver_tensors.keys()))
def test_build_raw_supervised_input_receiver_fn_overlapping_keys(self):
features = {"feature_1": constant_op.constant(["hello"]),
"feature_2": constant_op.constant([42])}
labels = {"feature_1": constant_op.constant([5]),
"bar": constant_op.constant([6])}
with self.assertRaises(ValueError):
export.build_raw_supervised_input_receiver_fn(features, labels)
def test_build_raw_supervised_input_receiver_fn(self):
features = {
"feature_1": constant_op.constant(["hello"]),
"feature_2": constant_op.constant([42])
}
labels = {
"foo": constant_op.constant([5]),
"bar": constant_op.constant([6])
}
input_receiver_fn = export.build_raw_supervised_input_receiver_fn(
features, labels)
with ops.Graph().as_default():
input_receiver = input_receiver_fn()
self.assertEqual(set(["feature_1", "feature_2"]),
set(input_receiver.features.keys()))
self.assertEqual(set(["foo", "bar"]),
set(input_receiver.labels.keys()))
self.assertEqual(set(["feature_1", "feature_2", "foo", "bar"]),
set(input_receiver.receiver_tensors.keys()))
self.assertEqual(
dtypes.string,
input_receiver.receiver_tensors["feature_1"].dtype)
self.assertEqual(
dtypes.int32,
input_receiver.receiver_tensors["feature_2"].dtype)
def test_build_raw_supervised_input_receiver_fn_batch_size(self):
features = {"feature_1": constant_op.constant(["hello"]),
"feature_2": constant_op.constant([42])}
labels = {"foo": constant_op.constant([5]),
"bar": constant_op.constant([6])}
input_receiver_fn = export.build_raw_supervised_input_receiver_fn(
features, labels, default_batch_size=10)
with ops.Graph().as_default():
input_receiver = input_receiver_fn()
self.assertEqual([10], input_receiver.receiver_tensors["feature_1"].shape)
self.assertEqual([10], input_receiver.features["feature_1"].shape)
def dummy_supervised_receiver_fn():
feature_spec = {
'x':
array_ops.placeholder(dtype=dtypes.int64,
shape=(2, 1),
name='feature_x'),
}
label_spec = array_ops.placeholder(dtype=dtypes.float32,
shape=[2, 1],
name='truth')
return export.build_raw_supervised_input_receiver_fn(
feature_spec, label_spec)
