def testNeuralGPU(self):
hparams = common_hparams.basic_params1()
batch_size = 3
input_length = 5
target_length = input_length
input_vocab_size = 9
target_vocab_size = 11
p_hparams = problem_hparams.test_problem_hparams(
input_vocab_size, target_vocab_size, hparams)
inputs = np.random.randint(input_vocab_size,
size=(batch_size, input_length, 1, 1))
targets = np.random.randint(target_vocab_size,
size=(batch_size, target_length, 1, 1))
with self.test_session() as session:
features = {
"inputs": tf.constant(inputs, dtype=tf.int32),
"targets": tf.constant(targets, dtype=tf.int32)
}
model = neural_gpu.NeuralGPU(hparams, tf.estimator.ModeKeys.TRAIN,
p_hparams)
logits, _ = model(features)
session.run(tf.global_variables_initializer())
res = session.run(logits)
self.assertEqual(res.shape,
(batch_size, target_length, 1, 1, target_vocab_size))
def testNeuralGPU(self):
hparams = common_hparams.basic_params1()
batch_size = 3
input_length = 5
target_length = input_length
input_vocab_size = 9
target_vocab_size = 11
p_hparams = problem_hparams.test_problem_hparams(hparams, input_vocab_size,
target_vocab_size)
inputs = -1 + np.random.random_integers(
input_vocab_size, size=(batch_size, input_length, 1, 1))
targets = -1 + np.random.random_integers(
target_vocab_size, size=(batch_size, target_length, 1, 1))
with self.test_session() as session:
features = {
"inputs": tf.constant(inputs, dtype=tf.int32),
"targets": tf.constant(targets, dtype=tf.int32)
}
model = neural_gpu.NeuralGPU(hparams, p_hparams)
shadred_logits, _, _ = model.model_fn(features, True)
logits = tf.concat(shadred_logits, 0)
session.run(tf.global_variables_initializer())
res = session.run(logits)
self.assertEqual(res.shape, (batch_size, target_length, 1, 1,
target_vocab_size))
