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
ofNumPy 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)