HenryAI/KerasAPIReference.txt · Datasets at Hugging Face

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test_split: Float between 0 and 1. Fraction of the dataset to be used as test data. Defaults to 0.2, meaning 20% of the dataset is used as test data.
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).

Note: 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.reuters.get_word_index(path="reuters_word_index.json")
Retrieves a dict mapping words to their index in the Reuters 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.

Boston Housing price regression dataset
load_data function
tf.keras.datasets.boston_housing.load_data(
path="boston_housing.npz", test_split=0.2, seed=113
)
Loads the Boston Housing dataset.

This is a dataset taken from the StatLib library which is maintained at Carnegie Mellon University.

Samples contain 13 attributes of houses at different locations around the Boston suburbs in the late 1970s. Targets are the median values of the houses at a location (in k$).

The attributes themselves are defined in the StatLib website.

Arguments

path: path where to cache the dataset locally (relative to ~/.keras/datasets).
test_split: fraction of the data to reserve as test set.
seed: Random seed for shuffling the data before computing the test split.
Returns

Tuple of Numpy arrays: (x_train, y_train), (x_test, y_test).
x_train, x_test: numpy arrays with shape (num_samples, 13) containing either the training samples (for x_train), or test samples (for y_train).

y_train, y_test: numpy arrays of shape (num_samples,) containing the target scalars. The targets are float scalars typically between 10 and 50 that represent the home prices in k$.CIFAR10 small images classification dataset
load_data function
tf.keras.datasets.cifar10.load_data()
Loads the CIFAR10 dataset.

This is a dataset of 50,000 32x32 color training images and 10,000 test images, labeled over 10 categories. See more info at the CIFAR homepage.

The classes are:

Label Description
0 airplane
1 automobile
2 bird
3 cat
4 deer
5 dog
6 frog
7 horse
8 ship
9 truck
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 0-9) 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 0-9) with shape (10000, 1) for the test data.

Example

(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.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)IMDB movie review sentiment classification dataset
load_data function
tf.keras.datasets.imdb.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.

test_split: Float between 0 and 1. Fraction of the dataset to be used as test data. Defaults to 0.2, meaning 20% of the dataset is used as test data.

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).

Note: 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.reuters.get_word_index(path="reuters_word_index.json")

Retrieves a dict mapping words to their index in the Reuters 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.

Boston Housing price regression dataset

load_data function

tf.keras.datasets.boston_housing.load_data(

path="boston_housing.npz", test_split=0.2, seed=113

)

Loads the Boston Housing dataset.

This is a dataset taken from the StatLib library which is maintained at Carnegie Mellon University.

Samples contain 13 attributes of houses at different locations around the Boston suburbs in the late 1970s. Targets are the median values of the houses at a location (in k$).

The attributes themselves are defined in the StatLib website.

Arguments

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

test_split: fraction of the data to reserve as test set.

seed: Random seed for shuffling the data before computing the test split.

Returns

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

x_train, x_test: numpy arrays with shape (num_samples, 13) containing either the training samples (for x_train), or test samples (for y_train).

y_train, y_test: numpy arrays of shape (num_samples,) containing the target scalars. The targets are float scalars typically between 10 and 50 that represent the home prices in k$.CIFAR10 small images classification dataset

load_data function

tf.keras.datasets.cifar10.load_data()

Loads the CIFAR10 dataset.

This is a dataset of 50,000 32x32 color training images and 10,000 test images, labeled over 10 categories. See more info at the CIFAR homepage.

The classes are:

Label Description

0 airplane

1 automobile

2 bird

3 cat

4 deer

5 dog

6 frog

7 horse

8 ship

9 truck

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 0-9) 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 0-9) with shape (10000, 1) for the test data.

Example

(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.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)IMDB movie review sentiment classification dataset

load_data function

tf.keras.datasets.imdb.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.