Module keras.api.keras.datasets.cifar10

Public API for tf.keras.datasets.cifar10 namespace.

Expand source code
# This file is MACHINE GENERATED! Do not edit.
# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
"""Public API for tf.keras.datasets.cifar10 namespace.
"""

from __future__ import print_function as _print_function

import sys as _sys

from keras.datasets.cifar10 import load_data

del _print_function

from tensorflow.python.util import module_wrapper as _module_wrapper

if not isinstance(_sys.modules[__name__], _module_wrapper.TFModuleWrapper):
  _sys.modules[__name__] = _module_wrapper.TFModuleWrapper(
      _sys.modules[__name__], "keras.datasets.cifar10", public_apis=None, deprecation=True,
      has_lite=False)

Functions

def 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)
Expand source code
@keras_export('keras.datasets.cifar10.load_data')
def 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](https://www.cs.toronto.edu/~kriz/cifar.html).

  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:

  ```python
  (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)
  ```
  """
  dirname = 'cifar-10-batches-py'
  origin = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
  path = get_file(
      dirname,
      origin=origin,
      untar=True,
      file_hash=
      '6d958be074577803d12ecdefd02955f39262c83c16fe9348329d7fe0b5c001ce')

  num_train_samples = 50000

  x_train = np.empty((num_train_samples, 3, 32, 32), dtype='uint8')
  y_train = np.empty((num_train_samples,), dtype='uint8')

  for i in range(1, 6):
    fpath = os.path.join(path, 'data_batch_' + str(i))
    (x_train[(i - 1) * 10000:i * 10000, :, :, :],
     y_train[(i - 1) * 10000:i * 10000]) = load_batch(fpath)

  fpath = os.path.join(path, 'test_batch')
  x_test, y_test = load_batch(fpath)

  y_train = np.reshape(y_train, (len(y_train), 1))
  y_test = np.reshape(y_test, (len(y_test), 1))

  if backend.image_data_format() == 'channels_last':
    x_train = x_train.transpose(0, 2, 3, 1)
    x_test = x_test.transpose(0, 2, 3, 1)

  x_test = x_test.astype(x_train.dtype)
  y_test = y_test.astype(y_train.dtype)

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