116 lines
3.7 KiB
Python
116 lines
3.7 KiB
Python
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# 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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"""CIFAR10 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.cifar10.load_data")
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def load_data():
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"""Loads the CIFAR10 dataset.
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This is a dataset of 50,000 32x32 color training images and 10,000 test
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images, labeled over 10 categories. See more info at the
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[CIFAR homepage](https://www.cs.toronto.edu/~kriz/cifar.html).
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The classes are:
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| Label | Description |
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|:-----:|-------------|
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| 0 | airplane |
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| 1 | automobile |
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| 2 | bird |
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| 3 | cat |
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| 4 | deer |
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| 5 | dog |
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| 6 | frog |
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| 7 | horse |
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| 8 | ship |
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| 9 | truck |
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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 0-9)
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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 0-9)
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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.cifar10.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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dirname = "cifar-10-batches-py"
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origin = "https://www.cs.toronto.edu/~kriz/cifar-10-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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"6d958be074577803d12ecdefd02955f39262c83c16fe9348329d7fe0b5c001ce"
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),
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)
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num_train_samples = 50000
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x_train = np.empty((num_train_samples, 3, 32, 32), dtype="uint8")
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y_train = np.empty((num_train_samples,), dtype="uint8")
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for i in range(1, 6):
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fpath = os.path.join(path, "data_batch_" + str(i))
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(
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x_train[(i - 1) * 10000 : i * 10000, :, :, :],
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y_train[(i - 1) * 10000 : i * 10000],
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) = load_batch(fpath)
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fpath = os.path.join(path, "test_batch")
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x_test, y_test = load_batch(fpath)
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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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x_test = x_test.astype(x_train.dtype)
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y_test = y_test.astype(y_train.dtype)
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return (x_train, y_train), (x_test, y_test)
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