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  /external/tensorflow/tensorflow/contrib/learn/python/learn/datasets/
mnist.py 128 self._num_examples = 10000
133 self._num_examples = images.shape[0]
160 return self._num_examples
180 perm0 = numpy.arange(self._num_examples)
185 if start + batch_size > self._num_examples:
189 rest_num_examples = self._num_examples - start
190 images_rest_part = self._images[start:self._num_examples]
191 labels_rest_part = self._labels[start:self._num_examples]
194 perm = numpy.arange(self._num_examples)
  /external/tensorflow/tensorflow/contrib/factorization/python/ops/
gmm_ops.py 272 self._num_examples = array_ops.shape(shard)[0]
367 array_ops.stack([self._num_examples, self._num_classes]))

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