101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""CIFAR100 small images classification dataset."""
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import os
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import numpy as np
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from keras import backend
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from keras.datasets.cifar import load_batch
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from keras.utils.data_utils import get_file
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.datasets.cifar100.load_data")
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def load_data(label_mode="fine"):
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"""Loads the CIFAR100 dataset.
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This is a dataset of 50,000 32x32 color training images and
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10,000 test images, labeled over 100 fine-grained classes that are
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grouped into 20 coarse-grained classes. See more info at the
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[CIFAR homepage](https://www.cs.toronto.edu/~kriz/cifar.html).
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Args:
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label_mode: one of "fine", "coarse". If it is "fine" the category labels
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are the fine-grained labels, if it is "coarse" the output labels are the
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coarse-grained superclasses.
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Returns:
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Tuple of NumPy arrays: `(x_train, y_train), (x_test, y_test)`.
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**x_train**: uint8 NumPy array of grayscale image data with shapes
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`(50000, 32, 32, 3)`, containing the training data. Pixel values range
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from 0 to 255.
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**y_train**: uint8 NumPy array of labels (integers in range 0-99)
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with shape `(50000, 1)` for the training data.
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**x_test**: uint8 NumPy array of grayscale image data with shapes
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`(10000, 32, 32, 3)`, containing the test data. Pixel values range
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from 0 to 255.
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**y_test**: uint8 NumPy array of labels (integers in range 0-99)
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with shape `(10000, 1)` for the test data.
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Example:
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```python
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(x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()
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assert x_train.shape == (50000, 32, 32, 3)
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assert x_test.shape == (10000, 32, 32, 3)
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assert y_train.shape == (50000, 1)
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assert y_test.shape == (10000, 1)
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```
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"""
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if label_mode not in ["fine", "coarse"]:
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raise ValueError(
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'`label_mode` must be one of `"fine"`, `"coarse"`. '
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f"Received: label_mode={label_mode}."
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)
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dirname = "cifar-100-python"
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origin = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
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path = get_file(
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dirname,
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origin=origin,
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untar=True,
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file_hash=( # noqa: E501
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"85cd44d02ba6437773c5bbd22e183051d648de2e7d6b014e1ef29b855ba677a7"
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),
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)
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fpath = os.path.join(path, "train")
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x_train, y_train = load_batch(fpath, label_key=label_mode + "_labels")
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fpath = os.path.join(path, "test")
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x_test, y_test = load_batch(fpath, label_key=label_mode + "_labels")
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y_train = np.reshape(y_train, (len(y_train), 1))
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y_test = np.reshape(y_test, (len(y_test), 1))
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if backend.image_data_format() == "channels_last":
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x_train = x_train.transpose(0, 2, 3, 1)
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x_test = x_test.transpose(0, 2, 3, 1)
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return (x_train, y_train), (x_test, y_test)
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