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my_datasets/my_cifar10.py

+# 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)