def testOneHotMultiplyExactHard(self):
inputs = tf.constant([[0., 1., 0.], [0., 0., 1.]])
scale = tf.constant([[0., 1., 0.], [0., 0., 1.]])
outputs = reversible.one_hot_multiply(inputs, scale)
outputs_val = self.evaluate(outputs)
self.assertAllEqual(outputs_val, np.array([[0., 1., 0.], [0., 1.,
0.]]))
def testOneHotMultiplyExactSoft(self):
inputs = tf.constant([[0., 1., 0.], [0., 0., 1.]])
scale = tf.constant([[0.1, 0.6, 0.3], [0.2, 0.4, 0.4]])
outputs = reversible.one_hot_multiply(inputs, scale)
scale_zero = np.array([[0., 0., 0.], [0., 0., 0.]])
scale_one = inputs
scale_two = np.array([[0., 0., 1.], [0., 1., 0.]])
expected_outputs = (scale[..., 0][..., tf.newaxis] * scale_zero +
scale[..., 1][..., tf.newaxis] * scale_one +
scale[..., 2][..., tf.newaxis] * scale_two)
actual_outputs_val, expected_outputs_val = self.evaluate(
[outputs, expected_outputs])
self.assertAllEqual(actual_outputs_val, expected_outputs_val)