Module keras.datasets.cifar100
CIFAR100 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.
# ==============================================================================
"""CIFAR100 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.cifar100.load_data')
def 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](https://www.cs.toronto.edu/~kriz/cifar.html).
Args:
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:
```python
(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)
```
"""
if label_mode not in ['fine', 'coarse']:
raise ValueError('`label_mode` must be one of `"fine"`, `"coarse"`.')
dirname = 'cifar-100-python'
origin = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
path = get_file(
dirname,
origin=origin,
untar=True,
file_hash=
'85cd44d02ba6437773c5bbd22e183051d648de2e7d6b014e1ef29b855ba677a7')
fpath = os.path.join(path, 'train')
x_train, y_train = load_batch(fpath, label_key=label_mode + '_labels')
fpath = os.path.join(path, 'test')
x_test, y_test = load_batch(fpath, label_key=label_mode + '_labels')
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)
return (x_train, y_train), (x_test, y_test)
Functions
def 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.
Args
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
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 0-99) 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)
Expand source code
@keras_export('keras.datasets.cifar100.load_data') def 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](https://www.cs.toronto.edu/~kriz/cifar.html). Args: 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: ```python (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) ``` """ if label_mode not in ['fine', 'coarse']: raise ValueError('`label_mode` must be one of `"fine"`, `"coarse"`.') dirname = 'cifar-100-python' origin = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz' path = get_file( dirname, origin=origin, untar=True, file_hash= '85cd44d02ba6437773c5bbd22e183051d648de2e7d6b014e1ef29b855ba677a7') fpath = os.path.join(path, 'train') x_train, y_train = load_batch(fpath, label_key=label_mode + '_labels') fpath = os.path.join(path, 'test') x_test, y_test = load_batch(fpath, label_key=label_mode + '_labels') 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) return (x_train, y_train), (x_test, y_test)