def testOneHotMinusExactHard(self):
inputs = tf.constant([[0., 1., 0.], [0., 0., 1.]])
shift = tf.constant([[0., 1., 0.], [1., 0., 0.]])
outputs = reversible.one_hot_minus(inputs, shift)
outputs_val = self.evaluate(outputs)
self.assertAllEqual(outputs_val, np.array([[1., 0., 0.], [0., 0.,
1.]]))
def testOneHotMinusExactSoft(self):
inputs = tf.constant([[0., 1., 0.], [0., 0., 1.]])
shift = tf.constant([[0.1, 0.6, 0.3], [0.2, 0.4, 0.4]])
outputs = reversible.one_hot_minus(inputs, shift)
shift_zero = inputs
shift_one = np.array([[1., 0., 0.], [0., 1., 0.]])
shift_two = np.array([[0., 0., 1.], [1., 0., 0.]])
expected_outputs = (shift[..., 0][..., tf.newaxis] * shift_zero +
shift[..., 1][..., tf.newaxis] * shift_one +
shift[..., 2][..., tf.newaxis] * shift_two)
actual_outputs_val, expected_outputs_val = self.evaluate(
[outputs, expected_outputs])
self.assertAllEqual(actual_outputs_val, expected_outputs_val)