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Adds label_vocab to all canned Classifiers.

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PiperOrigin-RevId: 158804380
This commit is contained in:
Jianwei Xie 2017-06-12 21:03:48 -07:00 committed by TensorFlower Gardener
parent 5e06dbdc00
commit b044a5d5e8
5 changed files with 59 additions and 11 deletions

14
tensorflow/python/estimator/canned/dnn.py
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@ -196,6 +196,7 @@ class DNNClassifier(estimator.Estimator):
model_dir=None,
n_classes=2,
weight_feature_key=None,
label_vocabulary=None,
optimizer='Adagrad',
activation_fn=nn.relu,
dropout=None,
@ -218,6 +219,13 @@ class DNNClassifier(estimator.Estimator):
weight_feature_key: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
label_vocabulary: A list of strings represents possible label values. If
given, labels must be string type and have any value in
`label_vocabulary`. If it is not given, that means labels are
already encoded as integer or float within [0, 1] for `n_classes=2` and
encoded as integer values in {0, 1,..., n_classes-1} for `n_classes`>2 .
Also there will be errors if vocabulary is not provided and labels are
string.
optimizer: An instance of `tf.Optimizer` used to train the model. If
`None`, will use an Adagrad optimizer.
activation_fn: Activation function applied to each layer. If `None`, will
@ -230,10 +238,12 @@ class DNNClassifier(estimator.Estimator):
"""
if n_classes == 2:
head = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( # pylint: disable=protected-access
weight_column=weight_feature_key)
weight_column=weight_feature_key,
label_vocabulary=label_vocabulary)
else:
head = head_lib._multi_class_head_with_softmax_cross_entropy_loss( # pylint: disable=protected-access
n_classes, weight_column=weight_feature_key)
n_classes, weight_column=weight_feature_key,
label_vocabulary=label_vocabulary)
def _model_fn(features, labels, mode, config):
return _dnn_model_fn(
features=features,

14
tensorflow/python/estimator/canned/dnn_linear_combined.py
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@ -308,6 +308,7 @@ class DNNLinearCombinedClassifier(estimator.Estimator):
dnn_dropout=None,
n_classes=2,
weight_feature_key=None,
label_vocabulary=None,
input_layer_partitioner=None,
config=None):
"""Initializes a DNNLinearCombinedClassifier instance.
@ -337,6 +338,13 @@ class DNNLinearCombinedClassifier(estimator.Estimator):
weight_feature_key: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
label_vocabulary: A list of strings represents possible label values. If
given, labels must be string type and have any value in
`label_vocabulary`. If it is not given, that means labels are
already encoded as integer or float within [0, 1] for `n_classes=2` and
encoded as integer values in {0, 1,..., n_classes-1} for `n_classes`>2 .
Also there will be errors if vocabulary is not provided and labels are
string.
input_layer_partitioner: Partitioner for input layer. Defaults to
`min_max_variable_partitioner` with `min_slice_size` 64 << 20.
config: RunConfig object to configure the runtime settings.
@ -354,11 +362,13 @@ class DNNLinearCombinedClassifier(estimator.Estimator):
'must be defined.')
if n_classes == 2:
head = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( # pylint: disable=protected-access
weight_column=weight_feature_key)
weight_column=weight_feature_key,
label_vocabulary=label_vocabulary)
else:
head = head_lib._multi_class_head_with_softmax_cross_entropy_loss( # pylint: disable=protected-access
n_classes,
weight_column=weight_feature_key)
weight_column=weight_feature_key,
label_vocabulary=label_vocabulary)
def _model_fn(features, labels, mode, config):
return _dnn_linear_combined_model_fn(
features=features,

2
tensorflow/python/estimator/canned/dnn_linear_combined_test.py
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@ -325,6 +325,7 @@ def _dnn_classifier_fn(
model_dir=None,
n_classes=2,
weight_feature_key=None,
label_vocabulary=None,
optimizer='Adagrad',
config=None,
input_layer_partitioner=None):
@ -335,6 +336,7 @@ def _dnn_classifier_fn(
dnn_optimizer=optimizer,
n_classes=n_classes,
weight_feature_key=weight_feature_key,
label_vocabulary=label_vocabulary,
input_layer_partitioner=input_layer_partitioner,
config=config)

32
tensorflow/python/estimator/canned/dnn_testing_utils.py
View File

@ -624,7 +624,7 @@ class BaseDNNClassifierPredictTest(object):
writer_cache.FileWriterCache.clear()
shutil.rmtree(self._model_dir)
def test_one_dim(self):
def _test_one_dim(self, label_vocabulary, label_output_fn):
"""Asserts predictions for one-dimensional input and logits."""
create_checkpoint(
(([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]),
@ -634,6 +634,7 @@ class BaseDNNClassifierPredictTest(object):
dnn_classifier = self._dnn_classifier_fn(
hidden_units=(2, 2),
label_vocabulary=label_vocabulary,
feature_columns=(feature_column.numeric_column('x'),),
model_dir=self._model_dir)
input_fn = numpy_io.numpy_input_fn(
@ -654,10 +655,20 @@ class BaseDNNClassifierPredictTest(object):
0.11105597], predictions[prediction_keys.PredictionKeys.PROBABILITIES])
self.assertAllClose([0],
predictions[prediction_keys.PredictionKeys.CLASS_IDS])
self.assertAllEqual([b'0'],
self.assertAllEqual([label_output_fn(0)],
predictions[prediction_keys.PredictionKeys.CLASSES])
def test_multi_dim(self):
def test_one_dim_without_label_vocabulary(self):
self._test_one_dim(label_vocabulary=None,
label_output_fn=lambda x: ('%s' % x).encode())
def test_one_dim_with_label_vocabulary(self):
n_classes = 2
self._test_one_dim(
label_vocabulary=['class_vocab_{}'.format(i) for i in range(n_classes)],
label_output_fn=lambda x: ('class_vocab_%s' % x).encode())
def _test_multi_dim_with_3_classes(self, label_vocabulary, label_output_fn):
"""Asserts predictions for multi-dimensional input and logits."""
create_checkpoint(
(([[.6, .5], [-.6, -.5]], [.1, -.1]),
@ -669,6 +680,7 @@ class BaseDNNClassifierPredictTest(object):
dnn_classifier = self._dnn_classifier_fn(
hidden_units=(2, 2),
feature_columns=(feature_column.numeric_column('x', shape=(2,)),),
label_vocabulary=label_vocabulary,
n_classes=3,
model_dir=self._model_dir)
input_fn = numpy_io.numpy_input_fn(
@ -698,7 +710,19 @@ class BaseDNNClassifierPredictTest(object):
self.assertAllEqual(
[1], predictions[prediction_keys.PredictionKeys.CLASS_IDS])
self.assertAllEqual(
[b'1'], predictions[prediction_keys.PredictionKeys.CLASSES])
[label_output_fn(1)],
predictions[prediction_keys.PredictionKeys.CLASSES])
def test_multi_dim_with_3_classes_but_no_label_vocab(self):
self._test_multi_dim_with_3_classes(
label_vocabulary=None,
label_output_fn=lambda x: ('%s' % x).encode())
def test_multi_dim_with_3_classes_and_label_vocab(self):
n_classes = 3
self._test_multi_dim_with_3_classes(
label_vocabulary=['class_vocab_{}'.format(i) for i in range(n_classes)],
label_output_fn=lambda x: ('class_vocab_%s' % x).encode())
class BaseDNNRegressorPredictTest(object):

8
tensorflow/python/estimator/canned/linear.py
View File

@ -179,10 +179,12 @@ class LinearClassifier(estimator.Estimator):
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
label_vocabulary: A list of strings represents possible label values. If
it is not given, that means labels are already encoded within [0, 1]. If
given, labels must be string type and have any value in
`label_vocabulary`. Also there will be errors if vocabulary is not
provided and labels are string.
`label_vocabulary`. If it is not given, that means labels are
already encoded as integer or float within [0, 1] for `n_classes=2` and
encoded as integer values in {0, 1,..., n_classes-1} for `n_classes`>2 .
Also there will be errors if vocabulary is not provided and labels are
string.
optimizer: The optimizer used to train the model. If specified, it should
be either an instance of `tf.Optimizer` or the SDCAOptimizer. If `None`,
the Ftrl optimizer will be used.