tensorflow/python/keras/datasets/imdb.py - platform/external/tensorflow - Git at Google

 # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
 """IMDB sentiment classification dataset."""

 import json

 import numpy as np

 from tensorflow.python.keras.preprocessing.sequence import _remove_long_seq
 from tensorflow.python.keras.utils.data_utils import get_file
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util.tf_export import keras_export


 @keras_export('keras.datasets.imdb.load_data')
 def load_data(path='imdb.npz',
               num_words=None,
               skip_top=0,
               maxlen=None,
               seed=113,
               start_char=1,
               oov_char=2,
               index_from=3,
               **kwargs):
   """Loads the [IMDB dataset](https://ai.stanford.edu/~amaas/data/sentiment/).

   This is a dataset of 25,000 movies reviews from IMDB, labeled by sentiment
   (positive/negative). Reviews have been preprocessed, and each review is
   encoded as a list of word indexes (integers).
   For convenience, words are indexed by overall frequency in the dataset,
   so that for instance the integer "3" encodes the 3rd most frequent word in
   the data. This allows for quick filtering operations such as:
   "only consider the top 10,000 most
   common words, but eliminate the top 20 most common words".

   As a convention, "0" does not stand for a specific word, but instead is used
   to encode any unknown word.

   Args:
     path: where to cache the data (relative to `~/.keras/dataset`).
     num_words: integer or None. Words are
         ranked by how often they occur (in the training set) and only
         the `num_words` most frequent words are kept. Any less frequent word
         will appear as `oov_char` value in the sequence data. If None,
         all words are kept. Defaults to None, so all words are kept.
     skip_top: skip the top N most frequently occurring words
         (which may not be informative). These words will appear as
         `oov_char` value in the dataset. Defaults to 0, so no words are
         skipped.
     maxlen: int or None. Maximum sequence length.
         Any longer sequence will be truncated. Defaults to None, which
         means no truncation.
     seed: int. Seed for reproducible data shuffling.
     start_char: int. The start of a sequence will be marked with this
         character. Defaults to 1 because 0 is usually the padding character.
     oov_char: int. The out-of-vocabulary character.
         Words that were cut out because of the `num_words` or
         `skip_top` limits will be replaced with this character.
     index_from: int. Index actual words with this index and higher.
     **kwargs: Used for backwards compatibility.

   Returns:
     Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.

   **x_train, x_test**: lists of sequences, which are lists of indexes
     (integers). If the num_words argument was specific, the maximum
     possible index value is `num_words - 1`. If the `maxlen` argument was
     specified, the largest possible sequence length is `maxlen`.

   **y_train, y_test**: lists of integer labels (1 or 0).

   Raises:
     ValueError: in case `maxlen` is so low
         that no input sequence could be kept.

   Note that the 'out of vocabulary' character is only used for
   words that were present in the training set but are not included
   because they're not making the `num_words` cut here.
   Words that were not seen in the training set but are in the test set
   have simply been skipped.
   """
   # Legacy support
   if 'nb_words' in kwargs:
     logging.warning('The `nb_words` argument in `load_data` '
                     'has been renamed `num_words`.')
     num_words = kwargs.pop('nb_words')
   if kwargs:
     raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))

   origin_folder = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
   path = get_file(
       path,
       origin=origin_folder + 'imdb.npz',
       file_hash=
       '69664113be75683a8fe16e3ed0ab59fda8886cb3cd7ada244f7d9544e4676b9f')
   with np.load(path, allow_pickle=True) as f:
     x_train, labels_train = f['x_train'], f['y_train']
     x_test, labels_test = f['x_test'], f['y_test']

   rng = np.random.RandomState(seed)
   indices = np.arange(len(x_train))
   rng.shuffle(indices)
   x_train = x_train[indices]
   labels_train = labels_train[indices]

   indices = np.arange(len(x_test))
   rng.shuffle(indices)
   x_test = x_test[indices]
   labels_test = labels_test[indices]

   if start_char is not None:
     x_train = [[start_char] + [w + index_from for w in x] for x in x_train]
     x_test = [[start_char] + [w + index_from for w in x] for x in x_test]
   elif index_from:
     x_train = [[w + index_from for w in x] for x in x_train]
     x_test = [[w + index_from for w in x] for x in x_test]

   if maxlen:
     x_train, labels_train = _remove_long_seq(maxlen, x_train, labels_train)
     x_test, labels_test = _remove_long_seq(maxlen, x_test, labels_test)
     if not x_train or not x_test:
       raise ValueError('After filtering for sequences shorter than maxlen=' +
                        str(maxlen) + ', no sequence was kept. '
                        'Increase maxlen.')

   xs = np.concatenate([x_train, x_test])
   labels = np.concatenate([labels_train, labels_test])

   if not num_words:
     num_words = max(max(x) for x in xs)

   # by convention, use 2 as OOV word
   # reserve 'index_from' (=3 by default) characters:
   # 0 (padding), 1 (start), 2 (OOV)
   if oov_char is not None:
     xs = [
         [w if (skip_top <= w < num_words) else oov_char for w in x] for x in xs
     ]
   else:
     xs = [[w for w in x if skip_top <= w < num_words] for x in xs]

   idx = len(x_train)
   x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])
   x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])

   return (x_train, y_train), (x_test, y_test)


 @keras_export('keras.datasets.imdb.get_word_index')
 def get_word_index(path='imdb_word_index.json'):
   """Retrieves a dict mapping words to their index in the IMDB dataset.

   Args:
       path: where to cache the data (relative to `~/.keras/dataset`).

   Returns:
       The word index dictionary. Keys are word strings, values are their index.

   Example:

   ```python
   # Retrieve the training sequences.
   (x_train, _), _ = keras.datasets.imdb.load_data()
   # Retrieve the word index file mapping words to indices
   word_index = keras.datasets.imdb.get_word_index()
   # Reverse the word index to obtain a dict mapping indices to words
   inverted_word_index = dict((i, word) for (word, i) in word_index.items())
   # Decode the first sequence in the dataset
   decoded_sequence = " ".join(inverted_word_index[i] for i in x_train[0])
   ```
   """
   origin_folder = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
   path = get_file(
       path,
       origin=origin_folder + 'imdb_word_index.json',
       file_hash='bfafd718b763782e994055a2d397834f')
   with open(path) as f:
     return json.load(f)
	# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	# ==============================================================================
	"""IMDB sentiment classification dataset."""

	import json

	import numpy as np

	from tensorflow.python.keras.preprocessing.sequence import _remove_long_seq
	from tensorflow.python.keras.utils.data_utils import get_file
	from tensorflow.python.platform import tf_logging as logging
	from tensorflow.python.util.tf_export import keras_export


	@keras_export('keras.datasets.imdb.load_data')
	def load_data(path='imdb.npz',
	num_words=None,
	skip_top=0,
	maxlen=None,
	seed=113,
	start_char=1,
	oov_char=2,
	index_from=3,
	**kwargs):
	"""Loads the [IMDB dataset](https://ai.stanford.edu/~amaas/data/sentiment/).

	This is a dataset of 25,000 movies reviews from IMDB, labeled by sentiment
	(positive/negative). Reviews have been preprocessed, and each review is
	encoded as a list of word indexes (integers).
	For convenience, words are indexed by overall frequency in the dataset,
	so that for instance the integer "3" encodes the 3rd most frequent word in
	the data. This allows for quick filtering operations such as:
	"only consider the top 10,000 most
	common words, but eliminate the top 20 most common words".

	As a convention, "0" does not stand for a specific word, but instead is used
	to encode any unknown word.

	Args:
	path: where to cache the data (relative to `~/.keras/dataset`).
	num_words: integer or None. Words are
	ranked by how often they occur (in the training set) and only
	the `num_words` most frequent words are kept. Any less frequent word
	will appear as `oov_char` value in the sequence data. If None,
	all words are kept. Defaults to None, so all words are kept.
	skip_top: skip the top N most frequently occurring words
	(which may not be informative). These words will appear as
	`oov_char` value in the dataset. Defaults to 0, so no words are
	skipped.
	maxlen: int or None. Maximum sequence length.
	Any longer sequence will be truncated. Defaults to None, which
	means no truncation.
	seed: int. Seed for reproducible data shuffling.
	start_char: int. The start of a sequence will be marked with this
	character. Defaults to 1 because 0 is usually the padding character.
	oov_char: int. The out-of-vocabulary character.
	Words that were cut out because of the `num_words` or
	`skip_top` limits will be replaced with this character.
	index_from: int. Index actual words with this index and higher.
	**kwargs: Used for backwards compatibility.

	Returns:
	Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.

	x_train, x_test: lists of sequences, which are lists of indexes
	(integers). If the num_words argument was specific, the maximum
	possible index value is `num_words - 1`. If the `maxlen` argument was
	specified, the largest possible sequence length is `maxlen`.

	y_train, y_test: lists of integer labels (1 or 0).

	Raises:
	ValueError: in case `maxlen` is so low
	that no input sequence could be kept.

	Note that the 'out of vocabulary' character is only used for
	words that were present in the training set but are not included
	because they're not making the `num_words` cut here.
	Words that were not seen in the training set but are in the test set
	have simply been skipped.
	"""
	# Legacy support
	if 'nb_words' in kwargs:
	logging.warning('The `nb_words` argument in `load_data` '
	'has been renamed `num_words`.')
	num_words = kwargs.pop('nb_words')
	if kwargs:
	raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))

	origin_folder = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
	path = get_file(
	path,
	origin=origin_folder + 'imdb.npz',
	file_hash=
	'69664113be75683a8fe16e3ed0ab59fda8886cb3cd7ada244f7d9544e4676b9f')
	with np.load(path, allow_pickle=True) as f:
	x_train, labels_train = f['x_train'], f['y_train']
	x_test, labels_test = f['x_test'], f['y_test']

	rng = np.random.RandomState(seed)
	indices = np.arange(len(x_train))
	rng.shuffle(indices)
	x_train = x_train[indices]
	labels_train = labels_train[indices]

	indices = np.arange(len(x_test))
	rng.shuffle(indices)
	x_test = x_test[indices]
	labels_test = labels_test[indices]

	if start_char is not None:
	x_train = [[start_char] + [w + index_from for w in x] for x in x_train]
	x_test = [[start_char] + [w + index_from for w in x] for x in x_test]
	elif index_from:
	x_train = [[w + index_from for w in x] for x in x_train]
	x_test = [[w + index_from for w in x] for x in x_test]

	if maxlen:
	x_train, labels_train = _remove_long_seq(maxlen, x_train, labels_train)
	x_test, labels_test = _remove_long_seq(maxlen, x_test, labels_test)
	if not x_train or not x_test:
	raise ValueError('After filtering for sequences shorter than maxlen=' +
	str(maxlen) + ', no sequence was kept. '
	'Increase maxlen.')

	xs = np.concatenate([x_train, x_test])
	labels = np.concatenate([labels_train, labels_test])

	if not num_words:
	num_words = max(max(x) for x in xs)

	# by convention, use 2 as OOV word
	# reserve 'index_from' (=3 by default) characters:
	# 0 (padding), 1 (start), 2 (OOV)
	if oov_char is not None:
	xs = [
	[w if (skip_top <= w < num_words) else oov_char for w in x] for x in xs
	]
	else:
	xs = [[w for w in x if skip_top <= w < num_words] for x in xs]

	idx = len(x_train)
	x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])
	x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])

	return (x_train, y_train), (x_test, y_test)


	@keras_export('keras.datasets.imdb.get_word_index')
	def get_word_index(path='imdb_word_index.json'):
	"""Retrieves a dict mapping words to their index in the IMDB dataset.

	Args:
	path: where to cache the data (relative to `~/.keras/dataset`).

	Returns:
	The word index dictionary. Keys are word strings, values are their index.

	Example:

	```python
	# Retrieve the training sequences.
	(x_train, _), _ = keras.datasets.imdb.load_data()
	# Retrieve the word index file mapping words to indices
	word_index = keras.datasets.imdb.get_word_index()
	# Reverse the word index to obtain a dict mapping indices to words
	inverted_word_index = dict((i, word) for (word, i) in word_index.items())
	# Decode the first sequence in the dataset
	decoded_sequence = " ".join(inverted_word_index[i] for i in x_train[0])
	```
	"""
	origin_folder = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
	path = get_file(
	path,
	origin=origin_folder + 'imdb_word_index.json',
	file_hash='bfafd718b763782e994055a2d397834f')
	with open(path) as f:
	return json.load(f)