Move the init file content to API gen build rule.
The final leftover piece is keras.layer.
PiperOrigin-RevId: 276390172
Change-Id: Ie2efc73e9b987df15fb8085f82b8369eca8ce664
diff --git a/tensorflow/python/keras/__init__.py b/tensorflow/python/keras/__init__.py
index 36ff3ca..7df3c2b 100644
--- a/tensorflow/python/keras/__init__.py
+++ b/tensorflow/python/keras/__init__.py
@@ -23,10 +23,7 @@
from tensorflow.python import tf2
-from tensorflow.python.keras import estimator
from tensorflow.python.keras import layers
-from tensorflow.python.keras import premade
-from tensorflow.python.keras import preprocessing
from tensorflow.python.keras.layers import Input
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.models import Sequential
diff --git a/tensorflow/python/keras/api/BUILD b/tensorflow/python/keras/api/BUILD
index ca1b13a..3c9e57b 100644
--- a/tensorflow/python/keras/api/BUILD
+++ b/tensorflow/python/keras/api/BUILD
@@ -37,12 +37,18 @@
"tensorflow.python.keras.datasets.imdb",
"tensorflow.python.keras.datasets.mnist",
"tensorflow.python.keras.datasets.reuters",
+ "tensorflow.python.keras.estimator",
"tensorflow.python.keras.initializers",
"tensorflow.python.keras.losses",
"tensorflow.python.keras.metrics",
"tensorflow.python.keras.models",
"tensorflow.python.keras.ops",
"tensorflow.python.keras.optimizers",
+ "tensorflow.python.keras.premade.linear",
+ "tensorflow.python.keras.premade.wide_deep",
+ "tensorflow.python.keras.preprocessing.image",
+ "tensorflow.python.keras.preprocessing.sequence",
+ "tensorflow.python.keras.preprocessing.text",
"tensorflow.python.keras.regularizers",
"tensorflow.python.keras.saving.model_config",
"tensorflow.python.keras.saving.save",
diff --git a/tensorflow/python/keras/distribute/keras_correctness_test_base.py b/tensorflow/python/keras/distribute/keras_correctness_test_base.py
index 73b899b..e8428c9 100644
--- a/tensorflow/python/keras/distribute/keras_correctness_test_base.py
+++ b/tensorflow/python/keras/distribute/keras_correctness_test_base.py
@@ -32,6 +32,7 @@
from tensorflow.python.eager import test
from tensorflow.python.framework import random_seed
from tensorflow.python.keras.distribute import distributed_training_utils
+from tensorflow.python.keras.preprocessing import sequence
from tensorflow.python.util import nest
_RANDOM_SEED = 1337
@@ -619,7 +620,7 @@
labels.append(label)
features.append(word_ids)
- features = keras.preprocessing.sequence.pad_sequences(
+ features = sequence.pad_sequences(
features, maxlen=max_words)
x_train = np.asarray(features, dtype=np.float32)
y_train = np.asarray(labels, dtype=np.int32).reshape((count, 1))
diff --git a/tensorflow/python/keras/preprocessing/__init__.py b/tensorflow/python/keras/preprocessing/__init__.py
index 0842a84..58b670d 100644
--- a/tensorflow/python/keras/preprocessing/__init__.py
+++ b/tensorflow/python/keras/preprocessing/__init__.py
@@ -29,10 +29,6 @@
keras_preprocessing.set_keras_submodules(backend=backend, utils=utils)
-from tensorflow.python.keras.preprocessing import image
-from tensorflow.python.keras.preprocessing import sequence
-from tensorflow.python.keras.preprocessing import text
-
del absolute_import
del division
del print_function
diff --git a/tensorflow/python/keras/preprocessing/image_test.py b/tensorflow/python/keras/preprocessing/image_test.py
index f7cbb58..1245c1e 100644
--- a/tensorflow/python/keras/preprocessing/image_test.py
+++ b/tensorflow/python/keras/preprocessing/image_test.py
@@ -24,7 +24,7 @@
import numpy as np
-from tensorflow.python import keras
+from tensorflow.python.keras.preprocessing import image as preprocessing_image
from tensorflow.python.platform import test
try:
@@ -41,11 +41,11 @@
bias = np.random.rand(img_w, img_h, 1) * 64
variance = np.random.rand(img_w, img_h, 1) * (255 - 64)
imarray = np.random.rand(img_w, img_h, 3) * variance + bias
- im = keras.preprocessing.image.array_to_img(imarray, scale=False)
+ im = preprocessing_image.array_to_img(imarray, scale=False)
rgb_images.append(im)
imarray = np.random.rand(img_w, img_h, 1) * variance + bias
- im = keras.preprocessing.image.array_to_img(imarray, scale=False)
+ im = preprocessing_image.array_to_img(imarray, scale=False)
gray_images.append(im)
return [rgb_images, gray_images]
@@ -60,10 +60,10 @@
for test_images in _generate_test_images():
img_list = []
for im in test_images:
- img_list.append(keras.preprocessing.image.img_to_array(im)[None, ...])
+ img_list.append(preprocessing_image.img_to_array(im)[None, ...])
images = np.vstack(img_list)
- generator = keras.preprocessing.image.ImageDataGenerator(
+ generator = preprocessing_image.ImageDataGenerator(
featurewise_center=True,
samplewise_center=True,
featurewise_std_normalization=True,
@@ -96,10 +96,10 @@
def test_image_data_generator_with_split_value_error(self):
with self.assertRaises(ValueError):
- keras.preprocessing.image.ImageDataGenerator(validation_split=5)
+ preprocessing_image.ImageDataGenerator(validation_split=5)
def test_image_data_generator_invalid_data(self):
- generator = keras.preprocessing.image.ImageDataGenerator(
+ generator = preprocessing_image.ImageDataGenerator(
featurewise_center=True,
samplewise_center=True,
featurewise_std_normalization=True,
@@ -119,14 +119,14 @@
generator.flow(x)
with self.assertRaises(ValueError):
- generator = keras.preprocessing.image.ImageDataGenerator(
+ generator = preprocessing_image.ImageDataGenerator(
data_format='unknown')
- generator = keras.preprocessing.image.ImageDataGenerator(
+ generator = preprocessing_image.ImageDataGenerator(
zoom_range=(2, 2))
def test_image_data_generator_fit(self):
- generator = keras.preprocessing.image.ImageDataGenerator(
+ generator = preprocessing_image.ImageDataGenerator(
featurewise_center=True,
samplewise_center=True,
featurewise_std_normalization=True,
@@ -139,7 +139,7 @@
# Test RBG
x = np.random.random((32, 10, 10, 3))
generator.fit(x)
- generator = keras.preprocessing.image.ImageDataGenerator(
+ generator = preprocessing_image.ImageDataGenerator(
featurewise_center=True,
samplewise_center=True,
featurewise_std_normalization=True,
@@ -192,14 +192,14 @@
# Test image loading util
fname = os.path.join(temp_dir, filenames[0])
- _ = keras.preprocessing.image.load_img(fname)
- _ = keras.preprocessing.image.load_img(fname, grayscale=True)
- _ = keras.preprocessing.image.load_img(fname, target_size=(10, 10))
- _ = keras.preprocessing.image.load_img(fname, target_size=(10, 10),
- interpolation='bilinear')
+ _ = preprocessing_image.load_img(fname)
+ _ = preprocessing_image.load_img(fname, grayscale=True)
+ _ = preprocessing_image.load_img(fname, target_size=(10, 10))
+ _ = preprocessing_image.load_img(fname, target_size=(10, 10),
+ interpolation='bilinear')
# create iterator
- generator = keras.preprocessing.image.ImageDataGenerator()
+ generator = preprocessing_image.ImageDataGenerator()
dir_iterator = generator.flow_from_directory(temp_dir)
# check number of classes and images
@@ -223,7 +223,7 @@
return np.zeros_like(x)
# Test usage as Sequence
- generator = keras.preprocessing.image.ImageDataGenerator(
+ generator = preprocessing_image.ImageDataGenerator(
preprocessing_function=preprocessing_function)
dir_seq = generator.flow_from_directory(
str(temp_dir),
@@ -276,7 +276,7 @@
count += 1
# create iterator
- generator = keras.preprocessing.image.ImageDataGenerator(
+ generator = preprocessing_image.ImageDataGenerator(
validation_split=validation_split)
with self.assertRaises(ValueError):
@@ -317,32 +317,32 @@
# Test channels_first data format
x = np.random.random((3, height, width))
- img = keras.preprocessing.image.array_to_img(
+ img = preprocessing_image.array_to_img(
x, data_format='channels_first')
self.assertEqual(img.size, (width, height))
- x = keras.preprocessing.image.img_to_array(
+ x = preprocessing_image.img_to_array(
img, data_format='channels_first')
self.assertEqual(x.shape, (3, height, width))
# Test 2D
x = np.random.random((1, height, width))
- img = keras.preprocessing.image.array_to_img(
+ img = preprocessing_image.array_to_img(
x, data_format='channels_first')
self.assertEqual(img.size, (width, height))
- x = keras.preprocessing.image.img_to_array(
+ x = preprocessing_image.img_to_array(
img, data_format='channels_first')
self.assertEqual(x.shape, (1, height, width))
# Test channels_last data format
x = np.random.random((height, width, 3))
- img = keras.preprocessing.image.array_to_img(x, data_format='channels_last')
+ img = preprocessing_image.array_to_img(x, data_format='channels_last')
self.assertEqual(img.size, (width, height))
- x = keras.preprocessing.image.img_to_array(img, data_format='channels_last')
+ x = preprocessing_image.img_to_array(img, data_format='channels_last')
self.assertEqual(x.shape, (height, width, 3))
# Test 2D
x = np.random.random((height, width, 1))
- img = keras.preprocessing.image.array_to_img(x, data_format='channels_last')
+ img = preprocessing_image.array_to_img(x, data_format='channels_last')
self.assertEqual(img.size, (width, height))
- x = keras.preprocessing.image.img_to_array(img, data_format='channels_last')
+ x = preprocessing_image.img_to_array(img, data_format='channels_last')
self.assertEqual(x.shape, (height, width, 1))
def test_batch_standardize(self):
@@ -353,10 +353,10 @@
for test_images in _generate_test_images():
img_list = []
for im in test_images:
- img_list.append(keras.preprocessing.image.img_to_array(im)[None, ...])
+ img_list.append(preprocessing_image.img_to_array(im)[None, ...])
images = np.vstack(img_list)
- generator = keras.preprocessing.image.ImageDataGenerator(
+ generator = preprocessing_image.ImageDataGenerator(
featurewise_center=True,
samplewise_center=True,
featurewise_std_normalization=True,
@@ -382,15 +382,15 @@
def test_img_transforms(self):
x = np.random.random((3, 200, 200))
- _ = keras.preprocessing.image.random_rotation(x, 20)
- _ = keras.preprocessing.image.random_shift(x, 0.2, 0.2)
- _ = keras.preprocessing.image.random_shear(x, 2.)
- _ = keras.preprocessing.image.random_zoom(x, (0.5, 0.5))
- _ = keras.preprocessing.image.apply_channel_shift(x, 2, 2)
- _ = keras.preprocessing.image.apply_affine_transform(x, 2)
+ _ = preprocessing_image.random_rotation(x, 20)
+ _ = preprocessing_image.random_shift(x, 0.2, 0.2)
+ _ = preprocessing_image.random_shear(x, 2.)
+ _ = preprocessing_image.random_zoom(x, (0.5, 0.5))
+ _ = preprocessing_image.apply_channel_shift(x, 2, 2)
+ _ = preprocessing_image.apply_affine_transform(x, 2)
with self.assertRaises(ValueError):
- keras.preprocessing.image.random_zoom(x, (0, 0, 0))
- _ = keras.preprocessing.image.random_channel_shift(x, 2.)
+ preprocessing_image.random_zoom(x, (0, 0, 0))
+ _ = preprocessing_image.random_channel_shift(x, 2.)
if __name__ == '__main__':
diff --git a/tensorflow/python/keras/preprocessing/sequence_test.py b/tensorflow/python/keras/preprocessing/sequence_test.py
index ce26b20..cb75b6e 100644
--- a/tensorflow/python/keras/preprocessing/sequence_test.py
+++ b/tensorflow/python/keras/preprocessing/sequence_test.py
@@ -22,7 +22,7 @@
import numpy as np
-from tensorflow.python import keras
+from tensorflow.python.keras.preprocessing import sequence as preprocessing_sequence
from tensorflow.python.platform import test
@@ -32,65 +32,65 @@
a = [[1], [1, 2], [1, 2, 3]]
# test padding
- b = keras.preprocessing.sequence.pad_sequences(a, maxlen=3, padding='pre')
+ b = preprocessing_sequence.pad_sequences(a, maxlen=3, padding='pre')
self.assertAllClose(b, [[0, 0, 1], [0, 1, 2], [1, 2, 3]])
- b = keras.preprocessing.sequence.pad_sequences(a, maxlen=3, padding='post')
+ b = preprocessing_sequence.pad_sequences(a, maxlen=3, padding='post')
self.assertAllClose(b, [[1, 0, 0], [1, 2, 0], [1, 2, 3]])
# test truncating
- b = keras.preprocessing.sequence.pad_sequences(
+ b = preprocessing_sequence.pad_sequences(
a, maxlen=2, truncating='pre')
self.assertAllClose(b, [[0, 1], [1, 2], [2, 3]])
- b = keras.preprocessing.sequence.pad_sequences(
+ b = preprocessing_sequence.pad_sequences(
a, maxlen=2, truncating='post')
self.assertAllClose(b, [[0, 1], [1, 2], [1, 2]])
# test value
- b = keras.preprocessing.sequence.pad_sequences(a, maxlen=3, value=1)
+ b = preprocessing_sequence.pad_sequences(a, maxlen=3, value=1)
self.assertAllClose(b, [[1, 1, 1], [1, 1, 2], [1, 2, 3]])
def test_pad_sequences_vector(self):
a = [[[1, 1]], [[2, 1], [2, 2]], [[3, 1], [3, 2], [3, 3]]]
# test padding
- b = keras.preprocessing.sequence.pad_sequences(a, maxlen=3, padding='pre')
+ b = preprocessing_sequence.pad_sequences(a, maxlen=3, padding='pre')
self.assertAllClose(b, [[[0, 0], [0, 0], [1, 1]], [[0, 0], [2, 1], [2, 2]],
[[3, 1], [3, 2], [3, 3]]])
- b = keras.preprocessing.sequence.pad_sequences(a, maxlen=3, padding='post')
+ b = preprocessing_sequence.pad_sequences(a, maxlen=3, padding='post')
self.assertAllClose(b, [[[1, 1], [0, 0], [0, 0]], [[2, 1], [2, 2], [0, 0]],
[[3, 1], [3, 2], [3, 3]]])
# test truncating
- b = keras.preprocessing.sequence.pad_sequences(
+ b = preprocessing_sequence.pad_sequences(
a, maxlen=2, truncating='pre')
self.assertAllClose(b, [[[0, 0], [1, 1]], [[2, 1], [2, 2]], [[3, 2], [3,
3]]])
- b = keras.preprocessing.sequence.pad_sequences(
+ b = preprocessing_sequence.pad_sequences(
a, maxlen=2, truncating='post')
self.assertAllClose(b, [[[0, 0], [1, 1]], [[2, 1], [2, 2]], [[3, 1], [3,
2]]])
# test value
- b = keras.preprocessing.sequence.pad_sequences(a, maxlen=3, value=1)
+ b = preprocessing_sequence.pad_sequences(a, maxlen=3, value=1)
self.assertAllClose(b, [[[1, 1], [1, 1], [1, 1]], [[1, 1], [2, 1], [2, 2]],
[[3, 1], [3, 2], [3, 3]]])
def test_make_sampling_table(self):
- a = keras.preprocessing.sequence.make_sampling_table(3)
+ a = preprocessing_sequence.make_sampling_table(3)
self.assertAllClose(
a, np.asarray([0.00315225, 0.00315225, 0.00547597]), rtol=.1)
def test_skipgrams(self):
# test with no window size and binary labels
- couples, labels = keras.preprocessing.sequence.skipgrams(
+ couples, labels = preprocessing_sequence.skipgrams(
np.arange(3), vocabulary_size=3)
for couple in couples:
self.assertIn(couple[0], [0, 1, 2])
self.assertIn(couple[1], [0, 1, 2])
# test window size and categorical labels
- couples, labels = keras.preprocessing.sequence.skipgrams(
+ couples, labels = preprocessing_sequence.skipgrams(
np.arange(5), vocabulary_size=5, window_size=1, categorical=True)
for couple in couples:
self.assertLessEqual(couple[0] - couple[1], 3)
@@ -100,7 +100,7 @@
def test_remove_long_seq(self):
a = [[[1, 1]], [[2, 1], [2, 2]], [[3, 1], [3, 2], [3, 3]]]
- new_seq, new_label = keras.preprocessing.sequence._remove_long_seq(
+ new_seq, new_label = preprocessing_sequence._remove_long_seq(
maxlen=3, seq=a, label=['a', 'b', ['c', 'd']])
self.assertEqual(new_seq, [[[1, 1]], [[2, 1], [2, 2]]])
self.assertEqual(new_label, ['a', 'b'])
@@ -109,7 +109,7 @@
data = np.array([[i] for i in range(50)])
targets = np.array([[i] for i in range(50)])
- data_gen = keras.preprocessing.sequence.TimeseriesGenerator(
+ data_gen = preprocessing_sequence.TimeseriesGenerator(
data, targets, length=10, sampling_rate=2, batch_size=2)
self.assertEqual(len(data_gen), 20)
self.assertAllClose(data_gen[0][0],
@@ -121,7 +121,7 @@
[9], [11]]]))
self.assertAllClose(data_gen[1][1], np.array([[12], [13]]))
- data_gen = keras.preprocessing.sequence.TimeseriesGenerator(
+ data_gen = preprocessing_sequence.TimeseriesGenerator(
data, targets, length=10, sampling_rate=2, reverse=True, batch_size=2)
self.assertEqual(len(data_gen), 20)
self.assertAllClose(data_gen[0][0],
@@ -129,7 +129,7 @@
[3], [1]]]))
self.assertAllClose(data_gen[0][1], np.array([[10], [11]]))
- data_gen = keras.preprocessing.sequence.TimeseriesGenerator(
+ data_gen = preprocessing_sequence.TimeseriesGenerator(
data, targets, length=10, sampling_rate=2, shuffle=True, batch_size=1)
batch = data_gen[0]
r = batch[1][0][0]
@@ -140,7 +140,7 @@
[r],
]))
- data_gen = keras.preprocessing.sequence.TimeseriesGenerator(
+ data_gen = preprocessing_sequence.TimeseriesGenerator(
data, targets, length=10, sampling_rate=2, stride=2, batch_size=2)
self.assertEqual(len(data_gen), 10)
self.assertAllClose(data_gen[1][0],
@@ -148,7 +148,7 @@
[12], [14]]]))
self.assertAllClose(data_gen[1][1], np.array([[14], [16]]))
- data_gen = keras.preprocessing.sequence.TimeseriesGenerator(
+ data_gen = preprocessing_sequence.TimeseriesGenerator(
data,
targets,
length=10,
@@ -164,7 +164,7 @@
data = np.array([np.random.random_sample((1, 2, 3, 4)) for i in range(50)])
targets = np.array([np.random.random_sample((3, 2, 1)) for i in range(50)])
- data_gen = keras.preprocessing.sequence.TimeseriesGenerator(
+ data_gen = preprocessing_sequence.TimeseriesGenerator(
data,
targets,
length=10,
@@ -181,7 +181,7 @@
self.assertAllClose(data_gen[0][1], np.array([targets[20], targets[21]]))
with self.assertRaises(ValueError) as context:
- keras.preprocessing.sequence.TimeseriesGenerator(data, targets, length=50)
+ preprocessing_sequence.TimeseriesGenerator(data, targets, length=50)
error = str(context.exception)
self.assertIn('`start_index+length=50 > end_index=49` is disallowed', error)
@@ -189,7 +189,7 @@
x = np.array([[i] for i in range(10)])
for length in range(3, 10):
- g = keras.preprocessing.sequence.TimeseriesGenerator(
+ g = preprocessing_sequence.TimeseriesGenerator(
x, x, length=length, batch_size=1)
expected = max(0, len(x) - length)
actual = len(g)
@@ -211,7 +211,7 @@
for stride, length, batch_size, shuffle in zip(strides, lengths,
batch_sizes, shuffles):
- g = keras.preprocessing.sequence.TimeseriesGenerator(
+ g = preprocessing_sequence.TimeseriesGenerator(
x,
x,
length=length,
diff --git a/tensorflow/python/keras/preprocessing/text_test.py b/tensorflow/python/keras/preprocessing/text_test.py
index 566fd3b..18bf257 100644
--- a/tensorflow/python/keras/preprocessing/text_test.py
+++ b/tensorflow/python/keras/preprocessing/text_test.py
@@ -21,7 +21,7 @@
import numpy as np
-from tensorflow.python import keras
+from tensorflow.python.keras.preprocessing import text as preprocessing_text
from tensorflow.python.platform import test
@@ -29,14 +29,14 @@
def test_one_hot(self):
text = 'The cat sat on the mat.'
- encoded = keras.preprocessing.text.one_hot(text, 5)
+ encoded = preprocessing_text.one_hot(text, 5)
self.assertEqual(len(encoded), 6)
self.assertLessEqual(np.max(encoded), 4)
self.assertGreaterEqual(np.min(encoded), 0)
# Test on unicode.
text = u'The cat sat on the mat.'
- encoded = keras.preprocessing.text.one_hot(text, 5)
+ encoded = preprocessing_text.one_hot(text, 5)
self.assertEqual(len(encoded), 6)
self.assertLessEqual(np.max(encoded), 4)
self.assertGreaterEqual(np.min(encoded), 0)
@@ -47,7 +47,7 @@
'The dog sat on the log.',
'Dogs and cats living together.'
]
- tokenizer = keras.preprocessing.text.Tokenizer(num_words=10)
+ tokenizer = preprocessing_text.Tokenizer(num_words=10)
tokenizer.fit_on_texts(texts)
sequences = []
@@ -64,14 +64,14 @@
def test_hashing_trick_hash(self):
text = 'The cat sat on the mat.'
- encoded = keras.preprocessing.text.hashing_trick(text, 5)
+ encoded = preprocessing_text.hashing_trick(text, 5)
self.assertEqual(len(encoded), 6)
self.assertLessEqual(np.max(encoded), 4)
self.assertGreaterEqual(np.min(encoded), 1)
def test_hashing_trick_md5(self):
text = 'The cat sat on the mat.'
- encoded = keras.preprocessing.text.hashing_trick(
+ encoded = preprocessing_text.hashing_trick(
text, 5, hash_function='md5')
self.assertEqual(len(encoded), 6)
self.assertLessEqual(np.max(encoded), 4)
@@ -82,13 +82,13 @@
x_test = ['This text has some unknown words'] # 2 OOVs: some, unknown
# Default, without OOV flag
- tokenizer = keras.preprocessing.text.Tokenizer()
+ tokenizer = preprocessing_text.Tokenizer()
tokenizer.fit_on_texts(x_train)
x_test_seq = tokenizer.texts_to_sequences(x_test)
self.assertEqual(len(x_test_seq[0]), 4) # discards 2 OOVs
# With OOV feature
- tokenizer = keras.preprocessing.text.Tokenizer(oov_token='<unk>')
+ tokenizer = preprocessing_text.Tokenizer(oov_token='<unk>')
tokenizer.fit_on_texts(x_train)
x_test_seq = tokenizer.texts_to_sequences(x_test)
self.assertEqual(len(x_test_seq[0]), 6) # OOVs marked in place
@@ -100,7 +100,7 @@
]
word_sequences = [['The', 'cat', 'is', 'sitting'],
['The', 'dog', 'is', 'standing']]
- tokenizer = keras.preprocessing.text.Tokenizer()
+ tokenizer = preprocessing_text.Tokenizer()
tokenizer.fit_on_texts(texts)
tokenizer.fit_on_texts(word_sequences)
@@ -111,29 +111,29 @@
def test_text_to_word_sequence(self):
text = 'hello! ? world!'
- seq = keras.preprocessing.text.text_to_word_sequence(text)
+ seq = preprocessing_text.text_to_word_sequence(text)
self.assertEqual(seq, ['hello', 'world'])
def test_text_to_word_sequence_multichar_split(self):
text = 'hello!stop?world!'
- seq = keras.preprocessing.text.text_to_word_sequence(text, split='stop')
+ seq = preprocessing_text.text_to_word_sequence(text, split='stop')
self.assertEqual(seq, ['hello', 'world'])
def test_text_to_word_sequence_unicode(self):
text = u'ali! veli? kırk dokuz elli'
- seq = keras.preprocessing.text.text_to_word_sequence(text)
+ seq = preprocessing_text.text_to_word_sequence(text)
self.assertEqual(seq, [u'ali', u'veli', u'kırk', u'dokuz', u'elli'])
def test_text_to_word_sequence_unicode_multichar_split(self):
text = u'ali!stopveli?stopkırkstopdokuzstopelli'
- seq = keras.preprocessing.text.text_to_word_sequence(text, split='stop')
+ seq = preprocessing_text.text_to_word_sequence(text, split='stop')
self.assertEqual(seq, [u'ali', u'veli', u'kırk', u'dokuz', u'elli'])
def test_tokenizer_unicode(self):
texts = [
u'ali veli kırk dokuz elli', u'ali veli kırk dokuz elli veli kırk dokuz'
]
- tokenizer = keras.preprocessing.text.Tokenizer(num_words=5)
+ tokenizer = preprocessing_text.Tokenizer(num_words=5)
tokenizer.fit_on_texts(texts)
self.assertEqual(len(tokenizer.word_counts), 5)
diff --git a/tensorflow/python/tools/api/generator/doc_srcs.py b/tensorflow/python/tools/api/generator/doc_srcs.py
index 28bf0e9..2f34db2 100644
--- a/tensorflow/python/tools/api/generator/doc_srcs.py
+++ b/tensorflow/python/tools/api/generator/doc_srcs.py
@@ -44,7 +44,6 @@
'gfile': DocSource(docstring_module_name='platform.gfile'),
'graph_util': DocSource(docstring_module_name='framework.graph_util'),
'image': DocSource(docstring_module_name='ops.image_ops'),
- 'keras.estimator': DocSource(docstring_module_name='keras.estimator'),
'linalg': DocSource(docstring_module_name='ops.linalg_ops'),
'logging': DocSource(docstring_module_name='ops.logging_ops'),
'losses': DocSource(docstring_module_name='ops.losses.losses'),
