def test_normalization(self):
"""Test the normalization constant."""
activations = tf.random.normal(shape=[100, 50000])
with self.cached_session():
normalization_constants = loss.compute_normalization(
activations, 1.01, num_iters=5)
self.assertEqual(normalization_constants.shape, [100, 1])
probabilities = tf.reduce_sum(
loss.exp_t(activations - normalization_constants, 1.01), -1)
self.assertAllClose(probabilities.eval(), [1.0] * 100, atol=1e-5)
normalization_constants = loss.compute_normalization(
activations, 2.0, num_iters=5)
probabilities = tf.reduce_sum(
loss.exp_t(activations - normalization_constants, 2.0), -1)
self.assertAllClose(probabilities.eval(), [1.0] * 100, atol=1e-5)
def test_normalization(self):
"""Test the normalization constant."""
activations = tf.random.normal(shape=[100, 50000])
for t in [0.99, 1.01]:
normalization_constants = loss.compute_normalization(activations,
t,
num_iters=20)
self.assertEqual(normalization_constants.shape, [100, 1])
probabilities = tf.reduce_sum(
loss.exp_t(activations - normalization_constants, t), -1)
self.assertAllClose(probabilities.numpy(), [1.0] * 100, atol=1e-5)
for t in [0.1, 2.0]:
normalization_constants = loss.compute_normalization(activations,
t,
num_iters=20)
probabilities = tf.reduce_sum(
loss.exp_t(activations - normalization_constants, t), -1)
self.assertAllClose(probabilities.numpy(), [1.0] * 100, atol=1e-5)