| """CIFAR100 small images classification dataset.""" |
|
|
| import os |
|
|
| import numpy as np |
|
|
| from keras.src import backend |
| from keras.src.api_export import keras_export |
| from keras.src.datasets.cifar import load_batch |
| from keras.src.utils.file_utils import get_file |
|
|
|
|
| @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, and 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"`. ' |
| f"Received: label_mode={label_mode}." |
| ) |
|
|
| dirname = "cifar-100-python-target" |
| origin = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz" |
| path = get_file( |
| fname=dirname, |
| origin=origin, |
| extract=True, |
| file_hash=( |
| "85cd44d02ba6437773c5bbd22e183051d648de2e7d6b014e1ef29b855ba677a7" |
| ), |
| ) |
|
|
| path = os.path.join(path, "cifar-100-python") |
| 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) |
|
|
