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Delete deprecated TensorFlowDNN(Regressor|Classifier). Also add tf_random_seed to fix flakiness for the affected tests.

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Change: 132347779
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A. Unique TensorFlower 2016-09-06 11:10:35 -08:00 committed by TensorFlower Gardener
parent 7946b8481f
commit 7bad05cb75
10 changed files with 69 additions and 80 deletions
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2
tensorflow/contrib/learn/__init__.py
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@ -25,8 +25,6 @@ Train and evaluate TensorFlow models.
@@ModeKeys
@@DNNClassifier
@@DNNRegressor
@@TensorFlowDNNClassifier
@@TensorFlowDNNRegressor
@@TensorFlowEstimator
@@LinearClassifier
@@LinearRegressor

2
tensorflow/contrib/learn/python/learn/estimators/__init__.py
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@ -26,8 +26,6 @@ from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowEsti
from tensorflow.contrib.learn.python.learn.estimators.classifier import Classifier
from tensorflow.contrib.learn.python.learn.estimators.dnn import DNNClassifier
from tensorflow.contrib.learn.python.learn.estimators.dnn import DNNRegressor
from tensorflow.contrib.learn.python.learn.estimators.dnn import TensorFlowDNNClassifier
from tensorflow.contrib.learn.python.learn.estimators.dnn import TensorFlowDNNRegressor
from tensorflow.contrib.learn.python.learn.estimators.dnn_linear_combined import DNNLinearCombinedClassifier
from tensorflow.contrib.learn.python.learn.estimators.dnn_linear_combined import DNNLinearCombinedRegressor
from tensorflow.contrib.learn.python.learn.estimators.estimator import BaseEstimator

13
tensorflow/contrib/learn/python/learn/estimators/dnn.py
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@ -19,9 +19,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.learn.python.learn.estimators import _sklearn
from tensorflow.contrib.learn.python.learn.estimators import dnn_linear_combined
from tensorflow.contrib.learn.python.learn.estimators.base import DeprecatedMixin
from tensorflow.python.ops import nn
@ -279,14 +277,3 @@ class DNNRegressor(dnn_linear_combined.DNNLinearCombinedRegressor):
@property
def bias_(self):
return self.dnn_bias_
# TensorFlowDNNClassifier and TensorFlowDNNRegressor are deprecated.
class TensorFlowDNNClassifier(DeprecatedMixin, DNNClassifier,
_sklearn.ClassifierMixin):
pass
class TensorFlowDNNRegressor(DeprecatedMixin, DNNRegressor,
_sklearn.RegressorMixin):
pass

23
tensorflow/contrib/learn/python/learn/tests/early_stopping_test.py
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@ -47,26 +47,29 @@ class EarlyStoppingTest(tf.test.TestCase):
x_train, y_train, test_size=0.2, random_state=42)
val_monitor = learn.monitors.ValidationMonitor(
x_val, y_val, every_n_steps=50, early_stopping_rounds=100,
early_stopping_metric='accuracy', early_stopping_metric_minimize=False)
early_stopping_metric='loss', early_stopping_metric_minimize=False)
feature_columns = learn.infer_real_valued_columns_from_input(iris.data)
# classifier without early stopping - overfitting
classifier1 = learn.TensorFlowDNNClassifier(
hidden_units=[10, 20, 10], n_classes=3, steps=1000)
classifier1.fit(x_train, y_train)
classifier1 = learn.DNNClassifier(
feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
classifier1.fit(x_train, y_train, steps=1000)
_ = accuracy_score(y_test, classifier1.predict(x_test))
# Full 1000 steps, 12 summaries and no evaluation summary.
# 12 summaries = global_step + first + every 100 out of 1000 steps.
self.assertEqual(12, len(_get_summary_events(classifier1.model_dir)))
# Full 1000 steps, 19 summaries and no evaluation summary:
# 1 summary of net at step 1
# 9 x (1 summary of net and 1 summary of global step) for steps 101, 201,...
self.assertEqual(19, len(_get_summary_events(classifier1.model_dir)))
with self.assertRaises(ValueError):
_get_summary_events(classifier1.model_dir + '/eval')
# classifier with early stopping - improved accuracy on testing set
classifier2 = learn.TensorFlowDNNClassifier(
hidden_units=[10, 20, 10], n_classes=3, steps=2000,
classifier2 = learn.DNNClassifier(
hidden_units=[10, 20, 10], feature_columns=feature_columns, n_classes=3,
config=tf.contrib.learn.RunConfig(save_checkpoints_secs=1))
classifier2.fit(x_train, y_train, monitors=[val_monitor])
classifier2.fit(x_train, y_train, monitors=[val_monitor], steps=2000)
_ = accuracy_score(y_val, classifier2.predict(x_val))
_ = accuracy_score(y_test, classifier2.predict(x_test))

26
tensorflow/contrib/learn/python/learn/tests/estimators_test.py
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@ -41,19 +41,18 @@ class InferredfeatureColumnTest(tf.test.TestCase):
test_size=0.2,
random_state=42)
def custom_optimizer(learning_rate):
return tf.train.MomentumOptimizer(learning_rate, 0.9)
def custom_optimizer():
return tf.train.MomentumOptimizer(learning_rate=0.01, momentum=0.9)
cont_features = [
tf.contrib.layers.real_valued_column("", dimension=4)]
classifier = learn.TensorFlowDNNClassifier(
classifier = learn.DNNClassifier(
feature_columns=cont_features,
hidden_units=[10, 20, 10],
n_classes=3,
steps=400,
learning_rate=0.01,
optimizer=custom_optimizer)
classifier.fit(x_train, y_train)
optimizer=custom_optimizer,
config=learn.RunConfig(tf_random_seed=1))
classifier.fit(x_train, y_train, steps=400)
score = accuracy_score(y_test, classifier.predict(x_test))
self.assertGreater(score, 0.65, "Failed with score = {0}".format(score))
@ -71,17 +70,16 @@ class CustomOptimizer(tf.test.TestCase):
test_size=0.2,
random_state=42)
def custom_optimizer(learning_rate):
return tf.train.MomentumOptimizer(learning_rate, 0.9)
def custom_optimizer():
return tf.train.MomentumOptimizer(learning_rate=0.01, momentum=0.9)
classifier = learn.TensorFlowDNNClassifier(
classifier = learn.DNNClassifier(
hidden_units=[10, 20, 10],
feature_columns=learn.infer_real_valued_columns_from_input(x_train),
n_classes=3,
steps=400,
learning_rate=0.01,
optimizer=custom_optimizer)
classifier.fit(x_train, y_train)
optimizer=custom_optimizer,
config=learn.RunConfig(tf_random_seed=1))
classifier.fit(x_train, y_train, steps=400)
score = accuracy_score(y_test, classifier.predict(x_test))
self.assertGreater(score, 0.65, "Failed with score = {0}".format(score))

12
tensorflow/contrib/learn/python/learn/tests/grid_search_test.py
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@ -43,12 +43,14 @@ class GridSearchTest(tf.test.TestCase):
if HAS_SKLEARN:
random.seed(42)
iris = datasets.load_iris()
classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
n_classes=3,
steps=50)
feature_columns = learn.infer_real_valued_columns_from_input(iris.data)
classifier = learn.DNNClassifier(
feature_columns=feature_columns, hidden_units=[10, 20, 10],
n_classes=3)
grid_search = GridSearchCV(classifier,
{'hidden_units': [[5, 5], [10, 10]],
'learning_rate': [0.1, 0.01]})
{'hidden_units': [[5, 5], [10, 10]]},
scoring='accuracy',
fit_params={'steps': [50]})
grid_search.fit(iris.data, iris.target)
score = accuracy_score(iris.target, grid_search.predict(iris.data))
self.assertGreater(score, 0.5, 'Failed with score = {0}'.format(score))

34
tensorflow/contrib/learn/python/learn/tests/nonlinear_test.py
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@ -37,9 +37,10 @@ class NonLinearTest(tf.test.TestCase):
def testIrisDNN(self):
iris = tf.contrib.learn.datasets.load_iris()
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
classifier = tf.contrib.learn.TensorFlowDNNClassifier(
feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(iris.data, iris.target)
classifier = tf.contrib.learn.DNNClassifier(
feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3,
config=tf.contrib.learn.RunConfig(tf_random_seed=1))
classifier.fit(iris.data, iris.target, max_steps=200)
score = accuracy_score(iris.target, classifier.predict(iris.data))
self.assertGreater(score, 0.9, "Failed with score = {0}".format(score))
weights = classifier.weights_
@ -53,10 +54,11 @@ class NonLinearTest(tf.test.TestCase):
def testBostonDNN(self):
boston = tf.contrib.learn.datasets.load_boston()
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=13)]
regressor = tf.contrib.learn.TensorFlowDNNRegressor(
feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=0,
batch_size=boston.data.shape[0], steps=300, learning_rate=0.01)
regressor.fit(boston.data, boston.target)
regressor = tf.contrib.learn.DNNRegressor(
feature_columns=feature_columns, hidden_units=[10, 20, 10],
config=tf.contrib.learn.RunConfig(tf_random_seed=1))
regressor.fit(
boston.data, boston.target, steps=300, batch_size=boston.data.shape[0])
score = mean_squared_error(boston.target, regressor.predict(boston.data))
self.assertLess(score, 110, "Failed with score = {0}".format(score))
weights = regressor.weights_
@ -71,10 +73,10 @@ class NonLinearTest(tf.test.TestCase):
# Dropout prob == 0.
iris = tf.contrib.learn.datasets.load_iris()
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
classifier = tf.contrib.learn.TensorFlowDNNClassifier(
classifier = tf.contrib.learn.DNNClassifier(
feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3,
dropout=0.0)
classifier.fit(iris.data, iris.target)
dropout=0.0, config=tf.contrib.learn.RunConfig(tf_random_seed=1))
classifier.fit(iris.data, iris.target, max_steps=200)
score = accuracy_score(iris.target, classifier.predict(iris.data))
self.assertGreater(score, 0.9, "Failed with score = {0}".format(score))
@ -82,10 +84,10 @@ class NonLinearTest(tf.test.TestCase):
# Dropping only a little.
iris = tf.contrib.learn.datasets.load_iris()
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
classifier = tf.contrib.learn.TensorFlowDNNClassifier(
classifier = tf.contrib.learn.DNNClassifier(
feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3,
dropout=0.1)
classifier.fit(iris.data, iris.target)
dropout=0.1, config=tf.contrib.learn.RunConfig(tf_random_seed=1))
classifier.fit(iris.data, iris.target, max_steps=200)
score = accuracy_score(iris.target, classifier.predict(iris.data))
# If the quality is lower - dropout is not working.
self.assertGreater(score, 0.9, "Failed with score = {0}".format(score))
@ -94,10 +96,10 @@ class NonLinearTest(tf.test.TestCase):
# Dropping out most of it.
iris = tf.contrib.learn.datasets.load_iris()
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
classifier = tf.contrib.learn.TensorFlowDNNClassifier(
classifier = tf.contrib.learn.DNNClassifier(
feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3,
dropout=0.9)
classifier.fit(iris.data, iris.target)
dropout=0.9, config=tf.contrib.learn.RunConfig(tf_random_seed=1))
classifier.fit(iris.data, iris.target, max_steps=200)
score = accuracy_score(iris.target, classifier.predict(iris.data))
self.assertGreater(score, 0.3, "Failed with score = {0}".format(score))
# If the quality is higher - dropout is not working.

24
tensorflow/contrib/learn/python/learn/tests/saver_test.py
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@ -62,17 +62,17 @@ class SaverTest(tf.test.TestCase):
# self.assertGreater(score, 0.5, 'Failed with score = {0}'.format(score))
def testDNN(self):
path = tf.test.get_temp_dir() + '/tmp_saver3'
random.seed(42)
iris = datasets.load_iris()
cont_features = [
tf.contrib.layers.real_valued_column('', dimension=4)]
classifier = learn.TensorFlowDNNClassifier(feature_columns=cont_features,
hidden_units=[10, 20, 10],
n_classes=3)
classifier.fit(iris.data, iris.target)
classifier.save(path)
classifier = learn.DNNClassifier(feature_columns=cont_features,
hidden_units=[10, 20, 10],
n_classes=3)
classifier.fit(iris.data, iris.target, max_steps=100)
# TODO(ipolosukhin): Remove or restore.
# path = tf.test.get_temp_dir() + '/tmp_saver3'
# classifier.save(path)
# new_classifier = learn.TensorFlowEstimator.restore(path)
# self.assertEqual(type(new_classifier), type(classifier))
# score = accuracy_score(iris.target, new_classifier.predict(iris.data))
@ -83,17 +83,17 @@ class SaverTest(tf.test.TestCase):
learn.TensorFlowEstimator.restore('no_model_path')
def testNoCheckpoints(self):
path = tf.test.get_temp_dir() + '/tmp/tmp.saver4'
random.seed(42)
iris = datasets.load_iris()
cont_features = [
tf.contrib.layers.real_valued_column('', dimension=4)]
classifier = learn.TensorFlowDNNClassifier(feature_columns=cont_features,
hidden_units=[10, 20, 10],
n_classes=3)
classifier.fit(iris.data, iris.target)
classifier.save(path)
classifier = learn.DNNClassifier(feature_columns=cont_features,
hidden_units=[10, 20, 10],
n_classes=3)
classifier.fit(iris.data, iris.target, max_steps=100)
# TODO(ipolosukhin): Remove or restore.
# path = tf.test.get_temp_dir() + '/tmp/tmp.saver4'
# classifier.save(path)
# os.remove(os.path.join(path, 'checkpoint'))
# with self.assertRaises(NotImplementedError):
# learn.TensorFlowEstimator.restore(path)

7
tensorflow/examples/skflow/hdf5_classification.py
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@ -45,12 +45,11 @@ y_test = h5f['y_test']
# Build 3 layer DNN with 10, 20, 10 units respectively.
feature_columns = learn.infer_real_valued_columns_from_input(x_train)
classifier = learn.TensorFlowDNNClassifier(
feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3,
steps=200)
classifier = learn.DNNClassifier(
feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
# Fit and predict.
classifier.fit(x_train, y_train)
classifier.fit(x_train, y_train, steps=200)
score = metrics.accuracy_score(y_test, classifier.predict(x_test))
print('Accuracy: {0:f}'.format(score))

6
tensorflow/g3doc/contrib/learn/get_started/index.md
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@ -44,8 +44,10 @@ Example of 3 layer network with 10, 20 and 10 hidden units respectively:
from sklearn import datasets, metrics
iris = datasets.load_iris()
classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(iris.data, iris.target)
feature_columns = learn.infer_real_valued_columns_from_input(iris.data)
classifier = learn.DNNClassifier(
feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(iris.data, iris.target, steps=100)
score = metrics.accuracy_score(iris.target, classifier.predict(iris.data))
print("Accuracy: %f" % score)