def testByteNet(self):
vocab_size = 9
x = np.random.randint(1, high=vocab_size, size=(3, 5, 1, 1))
y = np.random.randint(1, high=vocab_size, size=(3, 6, 1, 1))
hparams = bytenet.bytenet_base()
p_hparams = problem_hparams.test_problem_hparams(
vocab_size, vocab_size, hparams)
with self.test_session() as session:
features = {
"inputs": tf.constant(x, dtype=tf.int32),
"targets": tf.constant(y, dtype=tf.int32),
}
model = bytenet.ByteNet(hparams, tf.estimator.ModeKeys.TRAIN,
p_hparams)
logits, _ = model(features)
session.run(tf.global_variables_initializer())
res = session.run(logits)
self.assertEqual(res.shape, (3, 50, 1, 1, vocab_size))
def testByteNet(self):
vocab_size = 9
x = np.random.random_integers(1,
high=vocab_size - 1,
size=(3, 5, 1, 1))
y = np.random.random_integers(1,
high=vocab_size - 1,
size=(3, 6, 1, 1))
hparams = bytenet.bytenet_base()
p_hparams = problem_hparams.test_problem_hparams(
hparams, vocab_size, vocab_size)
with self.test_session() as session:
features = {
"inputs": tf.constant(x, dtype=tf.int32),
"targets": tf.constant(y, dtype=tf.int32),
}
model = bytenet.ByteNet(hparams, p_hparams)
sharded_logits, _, _ = model.model_fn(features, True)
logits = tf.concat(sharded_logits, 0)
session.run(tf.global_variables_initializer())
res = session.run(logits)
self.assertEqual(res.shape, (3, 50, 1, 1, vocab_size))