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my_cifar10.py 2,11 ko
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    # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
    #
    # Licensed under the Apache License, Version 2.0 (the "License");
    # you may not use this file except in compliance with the License.
    # You may obtain a copy of the License at
    #
    #     http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    # See the License for the specific language governing permissions and
    # limitations under the License.
    # ==============================================================================
    """CIFAR10 small images classification dataset.
    """
    from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function
    
    import os
    
    import numpy as np
    
    from keras import backend as K
    from keras.datasets.cifar import load_batch
    from keras.utils.data_utils import get_file
    from tensorflow.python.util.tf_export import tf_export
    
    
    #@tf_export('keras.datasets.cifar10.load_data')
    def load_data():
      """Loads CIFAR10 dataset.
    
      Returns:
          Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
      """
      dirname = 'cifar-10-batches-py'
      origin = './'
      #path = get_file(dirname, origin=origin, untar=True)
      path = '/export/home/adkneip/Documents/PhD/Python3/IMC_Modeling/qnn/my_datasets/cifar-10-batches-py';
    
      num_train_samples = 50000
    
      x_train = np.empty((num_train_samples, 3, 32, 32), dtype='uint8')
      y_train = np.empty((num_train_samples,), dtype='uint8')
    
      for i in range(1, 6):
        fpath = os.path.join(path, 'data_batch_' + str(i))
        (x_train[(i - 1) * 10000:i * 10000, :, :, :],
         y_train[(i - 1) * 10000:i * 10000]) = load_batch(fpath)
    
      fpath = os.path.join(path, 'test_batch')
      x_test, y_test = load_batch(fpath)
    
      y_train = np.reshape(y_train, (len(y_train), 1))
      y_test = np.reshape(y_test, (len(y_test), 1))
    
      if K.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)