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    ~Wêhµ  ã                   ór   — d Z ddlZddlZddlmZ ddlmZ ddlm	Z	 ddl
mZ  ed¦  «        d„ ¦   «         ZdS )	z,CIFAR10 small images classification dataset.é    N)Úbackend)Ú
load_batch)Úget_file)Úkeras_exportz keras.datasets.cifar10.load_datac            	      ó†  — d} d}t          | |dd¬¦  «        }d}t          j        |dddfd	¬
¦  «        }t          j        |fd	¬
¦  «        }t          dd¦  «        D ]j}t          j                             |dt          |¦  «        z   ¦  «        }t          |¦  «        \  ||dz
  dz  |dz  …dd…dd…dd…f<   ||dz
  dz  |dz  …<   Œkt          j                             |d¦  «        }t          |¦  «        \  }}	t          j	        |t          |¦  «        df¦  «        }t          j	        |	t          |	¦  «        df¦  «        }	t          j        ¦   «         dk    r0|                     dddd¦  «        }|                     dddd¦  «        }|                     |j        ¦  «        }|	                     |j        ¦  «        }	||f||	ffS )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)
    ```
    zcifar-10-batches-pyz7https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gzTÚ@6d958be074577803d12ecdefd02955f39262c83c16fe9348329d7fe0b5c001ce)ÚoriginÚuntarÚ	file_hashiPÃ  é   é    Úuint8)Údtypeé   é   Údata_batch_i'  NÚ
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Údirnamer	   r   Únum_train_samplesÚx_trainÚy_trainÚiÚfpathÚx_testÚy_tests
             ú\/var/www/html/movieo_spanner_bot/venv/lib/python3.11/site-packages/keras/datasets/cifar10.pyÚ	load_datar+      sá  € ðd $€GØF€FÝØØØàNðñ ô €Dð ÐåŒhÐ)¨1¨b°"Ð5¸WÐEÑEÔE€GÝŒhÐ)Ð+°7Ð;Ñ;Ô;€Gå1a‰[Œ[ð ð ˆÝ”—’˜T =µ3°q±6´6Ñ#9Ñ:Ô:ˆõ uÑÔñ	
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