ij ddZddlZddlZddlmZddlmZddlm Z ddl m Z eddZ y) z,CIFAR10 small images classification dataset.N)backend) keras_export) load_batch)get_filez keras.datasets.cifar10.load_datac xd}d}t||dd}d}tj|dddfd }tj|fd }tjj |d }t d d D]W}tjj |d|}t|\||d z dz|dzddddddf<||d z dz|dzYtjj |d}t|\}} tj|t|d f}tj| t| d f} tjdk(r(|jdddd }|jdddd }|j|j}| j|j} ||f|| ffS)a=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) ``` **Note**: The CIFAR-10 dataset is known to have a small percentage of mislabeled samples, which is inherent to the original dataset. This label noise may impact training and evaluation. For more details, refer to discussions in the research literature on CIFAR-10 label quality. zcifar-10-batches-py-targetz7https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gzT@6d958be074577803d12ecdefd02955f39262c83c16fe9348329d7fe0b5c001ce)fnameoriginextract file_hashiP uint8)dtypezcifar-10-batches-py data_batch_i'N test_batch channels_lastr)rnpemptyospathjoinrangerreshapelenrimage_data_format transposeastyper) dirnamer rnum_train_samplesx_trainy_trainifpathx_testy_tests o/var/www/html/emotional.easysim.app/public_html/venv/lib/python3.12/site-packages/keras/src/datasets/cifar10.py load_datar+ sn+G FF  N  Dhh)1b"5WEGhh)+7;G 77<<3 4D 1a[ T[#45 u  QUeOa%i/Aq8 9 QUeOa%i 0  GGLL| ,E&NFFjj3w<"34G ZZVa 0 1F  "o5##Aq!Q/!!!Q1- ]]7== )F ]]7== )F W / //) __doc__rnumpyr keras.srcrkeras.src.api_exportrkeras.src.datasets.cifarrkeras.src.utils.file_utilsrr+r,r*r4s62 -//01\02\0r,