From ef15e65bca6713fda4923410b3f7e15cd85d90f9 Mon Sep 17 00:00:00 2001 From: Scott Zhu Date: Wed, 23 Oct 2019 17:53:45 -0700 Subject: [PATCH] Move the init file content to API gen build rule. The final leftover piece is keras.layer. PiperOrigin-RevId: 276390172 Change-Id: Ie2efc73e9b987df15fb8085f82b8369eca8ce664 --- tensorflow/python/keras/__init__.py | 3 - tensorflow/python/keras/api/BUILD | 6 ++ .../distribute/keras_correctness_test_base.py | 3 +- .../python/keras/preprocessing/__init__.py | 4 - .../python/keras/preprocessing/image_test.py | 74 +++++++++---------- .../keras/preprocessing/sequence_test.py | 48 ++++++------ .../python/keras/preprocessing/text_test.py | 28 +++---- .../python/tools/api/generator/doc_srcs.py | 1 - 8 files changed, 83 insertions(+), 84 deletions(-) diff --git a/tensorflow/python/keras/__init__.py b/tensorflow/python/keras/__init__.py index 36ff3ca125e..7df3c2bba31 100644 --- a/tensorflow/python/keras/__init__.py +++ b/tensorflow/python/keras/__init__.py @@ -23,10 +23,7 @@ from __future__ import print_function 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 ca1b13a04d0..3c9e57bb28e 100644 --- a/tensorflow/python/keras/api/BUILD +++ b/tensorflow/python/keras/api/BUILD @@ -37,12 +37,18 @@ keras_packages = [ "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 73b899ba3cc..e8428c9b2d5 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 context 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 @@ class TestDistributionStrategyEmbeddingModelCorrectnessBase( 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 0842a8453a4..58b670d0b0e 100644 --- a/tensorflow/python/keras/preprocessing/__init__.py +++ b/tensorflow/python/keras/preprocessing/__init__.py @@ -29,10 +29,6 @@ from tensorflow.python.keras.utils import all_utils as utils 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 f7cbb589dc9..1245c1ecc8e 100644 --- a/tensorflow/python/keras/preprocessing/image_test.py +++ b/tensorflow/python/keras/preprocessing/image_test.py @@ -24,7 +24,7 @@ import tempfile 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 @@ def _generate_test_images(): 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 @@ class TestImage(test.TestCase): 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 @@ class TestImage(test.TestCase): 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 @@ class TestImage(test.TestCase): 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 @@ class TestImage(test.TestCase): # 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 @@ class TestImage(test.TestCase): # 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 @@ class TestImage(test.TestCase): 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 @@ class TestImage(test.TestCase): 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 @@ class TestImage(test.TestCase): # 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 @@ class TestImage(test.TestCase): 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 @@ class TestImage(test.TestCase): 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 ce26b207226..cb75b6ed7ba 100644 --- a/tensorflow/python/keras/preprocessing/sequence_test.py +++ b/tensorflow/python/keras/preprocessing/sequence_test.py @@ -22,7 +22,7 @@ from math import ceil 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 @@ class TestSequence(test.TestCase): 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 @@ class TestSequence(test.TestCase): 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 @@ class TestSequence(test.TestCase): 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 @@ class TestSequence(test.TestCase): [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 @@ class TestSequence(test.TestCase): [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 @@ class TestSequence(test.TestCase): [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 @@ class TestSequence(test.TestCase): [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 @@ class TestSequence(test.TestCase): 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 @@ class TestSequence(test.TestCase): 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 @@ class TestSequence(test.TestCase): 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 @@ class TestSequence(test.TestCase): 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 566fd3bb1a3..18bf2579c6a 100644 --- a/tensorflow/python/keras/preprocessing/text_test.py +++ b/tensorflow/python/keras/preprocessing/text_test.py @@ -21,7 +21,7 @@ from __future__ import print_function 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 @@ class TestText(test.TestCase): 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 @@ class TestText(test.TestCase): '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 @@ class TestText(test.TestCase): 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 @@ class TestText(test.TestCase): 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='') + tokenizer = preprocessing_text.Tokenizer(oov_token='') 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 @@ class TestText(test.TestCase): ] 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 @@ class TestText(test.TestCase): 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 28bf0e9d015..2f34db241a3 100644 --- a/tensorflow/python/tools/api/generator/doc_srcs.py +++ b/tensorflow/python/tools/api/generator/doc_srcs.py @@ -44,7 +44,6 @@ _TENSORFLOW_DOC_SOURCES = { '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'),