HenryAI/KerasAPIReference.txt · Datasets at Hugging Face

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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.

Arguments

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.

get_word_index function
tf.keras.datasets.imdb.get_word_index(path="imdb_word_index.json")
Retrieves a dict mapping words to their index in the IMDB dataset.

Arguments

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

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

Example

# 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])CIFAR100 small images classification dataset
load_data function
tf.keras.datasets.cifar100.load_data(label_mode="fine")
Loads the CIFAR100 dataset.

This is a dataset of 50,000 32x32 color training images and 10,000 test images, labeled over 100 fine-grained classes that are grouped into 20 coarse-grained classes. See more info at the CIFAR homepage.

Arguments

label_mode: one of "fine", "coarse". If it is "fine" the category labels are the fine-grained labels, if it is "coarse" the output labels are the coarse-grained superclasses.
Returns

Tuple of NumPy arrays: (x_train, y_train), (x_test, y_test).
x_train: uint8 NumPy array of grayscale image data with shapes (50000, 32, 32, 3), containing the training data. Pixel values range from 0 to 255.

y_train: uint8 NumPy array of labels (integers in range 0-99) with shape (50000, 1) for the training data.

x_test: uint8 NumPy array of grayscale image data with shapes (10000, 32, 32, 3), containing the test data. Pixel values range from 0 to 255.

y_test: uint8 NumPy array of labels (integers in range 0-99) with shape (10000, 1) for the test data.

Example

(x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()
assert x_train.shape == (50000, 32, 32, 3)
assert x_test.shape == (10000, 32, 32, 3)
assert y_train.shape == (50000, 1)
assert y_test.shape == (10000, 1)
ResNet and ResNetV2
ResNet50 function
tf.keras.applications.ResNet50(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs
)
Instantiates the ResNet50 architecture.

Reference

Deep Residual Learning for Image Recognition (CVPR 2015)
For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call tf.keras.applications.resnet.preprocess_input on your inputs before passing them to the model. resnet.preprocess_input will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.

Arguments

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.

Arguments

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.

get_word_index function

tf.keras.datasets.imdb.get_word_index(path="imdb_word_index.json")

Retrieves a dict mapping words to their index in the IMDB dataset.

Arguments

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

Returns

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

Example

# 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])CIFAR100 small images classification dataset

load_data function

tf.keras.datasets.cifar100.load_data(label_mode="fine")

Loads the CIFAR100 dataset.

This is a dataset of 50,000 32x32 color training images and 10,000 test images, labeled over 100 fine-grained classes that are grouped into 20 coarse-grained classes. See more info at the CIFAR homepage.

Arguments

label_mode: one of "fine", "coarse". If it is "fine" the category labels are the fine-grained labels, if it is "coarse" the output labels are the coarse-grained superclasses.

Returns

Tuple of NumPy arrays: (x_train, y_train), (x_test, y_test).

x_train: uint8 NumPy array of grayscale image data with shapes (50000, 32, 32, 3), containing the training data. Pixel values range from 0 to 255.

y_train: uint8 NumPy array of labels (integers in range 0-99) with shape (50000, 1) for the training data.

x_test: uint8 NumPy array of grayscale image data with shapes (10000, 32, 32, 3), containing the test data. Pixel values range from 0 to 255.

y_test: uint8 NumPy array of labels (integers in range 0-99) with shape (10000, 1) for the test data.

Example

(x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()

assert x_train.shape == (50000, 32, 32, 3)

assert x_test.shape == (10000, 32, 32, 3)

assert y_train.shape == (50000, 1)

assert y_test.shape == (10000, 1)

ResNet and ResNetV2

ResNet50 function

tf.keras.applications.ResNet50(

include_top=True,

weights="imagenet",

input_tensor=None,

input_shape=None,

pooling=None,

classes=1000,

**kwargs

)

Instantiates the ResNet50 architecture.

Reference

Deep Residual Learning for Image Recognition (CVPR 2015)

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call tf.keras.applications.resnet.preprocess_input on your inputs before passing them to the model. resnet.preprocess_input will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.

Arguments