def _input_fn():
"""Example-based input function."""
english_label = label_vocabulary[1] if label_vocabulary else 1.0
chinese_label = label_vocabulary[0] if label_vocabulary else 0.0
if n_classes > 2:
# For multi-class labels, English is class 0, Chinese is class 1.
chinese_label = label_vocabulary[1] if label_vocabulary else 1
english_label = label_vocabulary[0] if label_vocabulary else 0
serialized_examples = [
x.SerializeToString() for x in [
util.make_example(
age=1.0, language='english', label=english_label),
util.make_example(
age=2.0, language='english', label=english_label),
util.make_example(
age=3.0, language='chinese', label=chinese_label),
util.make_example(
age=4.0, language='chinese', label=chinese_label)
]
]
features = tf.io.parse_example(serialized=serialized_examples,
features=feature_spec)
labels = features.pop('label')
if n_classes > 2 and not label_vocabulary:
labels = tf.sparse.to_dense(labels, default_value=-1)
return features, labels
def _input_fn():
"""Example-based input function."""
serialized_examples = [
x.SerializeToString() for x in [
util.make_example(age=1.0, language='english', label=1.0),
util.make_example(age=2.0, language='english', label=1.0),
util.make_example(age=3.0, language='chinese', label=0.0),
util.make_example(age=4.0, language='chinese', label=0.0)
]
]
features = tf.parse_example(serialized_examples, feature_spec)
labels = features.pop('label')
return features, labels
def testMakeExample(self):
expected = example_pb2.Example()
expected.features.feature['single_float'].float_list.value[:] = [1.0]
expected.features.feature['single_int'].int64_list.value[:] = [2]
expected.features.feature['single_str'].bytes_list.value[:] = ['apple']
expected.features.feature['multi_float'].float_list.value[:] = [
4.0, 5.0
]
expected.features.feature['multi_int'].int64_list.value[:] = [6, 7]
expected.features.feature['multi_str'].bytes_list.value[:] = [
'orange', 'banana'
]
self.assertEqual(
expected,
util.make_example(single_float=1.0,
single_int=2,
single_str='apple',
multi_float=[4.0, 5.0],
multi_int=[6, 7],
multi_str=['orange', 'banana']))
def make_example_with_label(values_t1=None, values_t2=None, values_t3=None):
"""Make example with label."""
effective_t1 = 0.0
effective_t2 = 0.0
effective_t3 = 0.0
args = {}
if values_t1 is not None:
args['values_t1'] = float(values_t1)
effective_t1 = values_t1
if values_t2 is not None:
args['values_t2'] = float(values_t2)
effective_t2 = values_t2
if values_t3 is not None:
args['values_t3'] = float(values_t3)
effective_t3 = values_t3
label = (3 * effective_t1 + 6 * effective_t2 + 9 * effective_t3 +
4 * (effective_t1 + effective_t1**2 + effective_t1**3) +
5 * (effective_t2 + effective_t2**2 + effective_t2**3) +
6 * (effective_t3 + effective_t3**2 + effective_t3**3))
args['label'] = float(label)
return util.make_example(**args)
def _makeExample(self, **kwargs):
return util.make_example(**kwargs)
def _makeExample(self, **kwargs) -> example_pb2.Example:
return util.make_example(**kwargs)
def _makeExample(self, **kwargs) -> tf.train.Example:
return util.make_example(**kwargs)
def generate_regression_examples(
num_examples) -> Iterator[example_pb2.Example]:
for _ in range(num_examples):
yield util.make_example(label=float(np.random.random()),
prediction=float(np.random.uniform()))
def generate_classification_examples(
num_examples) -> Iterator[example_pb2.Example]:
for _ in range(num_examples):
yield util.make_example(label=float(np.random.choice([0, 1])),
prediction=float(np.random.uniform()))
