Python random_flip_up_down Examples

Example #1

 def random_flipped_inputs():
   flipped_outputs = inputs
   if self.horizontal:
     flipped_outputs = image_ops.random_flip_up_down(flipped_outputs,
                                                     self.seed)
   if self.vertical:
     flipped_outputs = image_ops.random_flip_left_right(
         flipped_outputs, self.seed)
   return flipped_outputs

Example #2

File: image_ops_test.py Project: vurte/tensorflow

    def testRandomUpDown(self):
        x_np = np.array([0, 1], dtype=np.uint8).reshape([2, 1, 1])
        num_iterations = 500

        hist = [0, 0]
        with self.test_session():
            x_tf = constant_op.constant(x_np, shape=x_np.shape)
            y = image_ops.random_flip_up_down(x_tf)
            for _ in xrange(num_iterations):
                y_np = y.eval().flatten()[0]
                hist[y_np] += 1

        # Ensure that each entry is observed within 4 standard deviations.
        four_stddev = 4.0 * np.sqrt(num_iterations / 2.0)
        self.assertAllClose(hist, [num_iterations / 2.0] * 2, atol=four_stddev)

Example #3

File: image_ops_test.py Project: hbali-sara/tensorflow

  def testRandomUpDown(self):
    x_np = np.array([0, 1], dtype=np.uint8).reshape([2, 1, 1])
    num_iterations = 500

    hist = [0, 0]
    with self.test_session():
      x_tf = constant_op.constant(x_np, shape=x_np.shape)
      y = image_ops.random_flip_up_down(x_tf)
      for _ in xrange(num_iterations):
        y_np = y.eval().flatten()[0]
        hist[y_np] += 1

    # Ensure that each entry is observed within 4 standard deviations.
    four_stddev = 4.0 * np.sqrt(num_iterations / 2.0)
    self.assertAllClose(hist, [num_iterations / 2.0] * 2, atol=four_stddev)