tensorflow_probability/examples/cifar10_bnn.py
def build_fake_data():
  """Build fake CIFAR10-style data for unit testing."""
  num_examples = 10
  x_train = np.random.rand(num_examples, *IMAGE_SHAPE).astype(np.float32)
  y_train = np.random.permutation(np.arange(num_examples)).astype(np.int32)
  x_test = np.random.rand(num_examples, *IMAGE_SHAPE).astype(np.float32)
  y_test = np.random.permutation(np.arange(num_examples)).astype(np.int32)
  return (x_train, y_train), (x_test, y_test)
def build_fake_data():
  """Build fake CIFAR10-style data for unit testing."""
  num_examples = 10
  x_train = np.random.rand(num_examples, *IMAGE_SHAPE).astype(np.float32)
  y_train = np.random.permutation(np.arange(num_examples)).astype(np.int32)
  x_test = np.random.rand(num_examples, *IMAGE_SHAPE).astype(np.float32)
  y_test = np.random.permutation(np.arange(num_examples)).astype(np.int32)
  return (x_train, y_train), (x_test, y_test)
def build_fake_data ( ) : num_examples = 10 x_train = np . random . rand ( num_examples , * IMAGE_SHAPE ) . astype ( np . float32 ) y_train = np . random . permutation ( np . arange ( num_examples ) ) . astype ( np . int32 ) x_test = np . random . rand ( num_examples , * IMAGE_SHAPE ) . astype ( np . float32 ) y_test = np . random . permutation ( np . arange ( num_examples ) ) . astype ( np . int32 ) return ( x_train , y_train ) , ( x_test , y_test )
Build fake CIFAR10-style data for unit testing.
Build fake CIFAR10 - style data for unit testing .