Module keras.datasets.cifar10
CIFAR10 small images classification dataset.
Expand source code
# 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.
# ==============================================================================
"""CIFAR10 small images classification dataset."""
import os
import numpy as np
from keras import backend
from keras.datasets.cifar import load_batch
from keras.utils.data_utils import get_file
from tensorflow.python.util.tf_export import keras_export
@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)
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)