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    2ÆVhu  ã                   ód   — 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y)	z,CIFAR10 small images classification dataset.é    N)Úbackend)Úkeras_export)Ú
load_batch)Úget_filez keras.datasets.cifar10.load_datac            	      óŠ  — d} d}t        | |dd¬«      }d}t        j                  |dddfd	¬
«      }t        j                  |fd	¬
«      }t        j                  j                  |d«      }t        dd«      D ]`  }t        j                  j                  |dt        |«      z   «      }t        |«      \  ||dz
  dz  |dz  …dd…dd…dd…f<   ||dz
  dz  |dz   Œb t        j                  j                  |d«      }t        |«      \  }}	t        j                  |t        |«      df«      }t        j                  |	t        |	«      df«      }	t        j                  «       dk(  r(|j                  dddd«      }|j                  dddd«      }|j                  |j                  «      }|	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-py-targetz7https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gzTÚ@6d958be074577803d12ecdefd02955f39262c83c16fe9348329d7fe0b5c001ce)ÚfnameÚoriginÚextractÚ	file_hashiPÃ  é   é    Úuint8)Údtypezcifar-10-batches-pyé   é   Údata_batch_i'  NÚ
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