def testTransformerWithoutProblem(self):
hparams = transformer.transformer_test()
embedded_inputs = np.random.random_sample(
(BATCH_SIZE, INPUT_LENGTH, 1, hparams.hidden_size))
embedded_targets = np.random.random_sample(
(BATCH_SIZE, TARGET_LENGTH, 1, hparams.hidden_size))
transformed_features = {
"inputs": tf.constant(embedded_inputs, dtype=tf.float32),
"targets": tf.constant(embedded_targets, dtype=tf.float32)
}
model = transformer.Transformer(hparams)
body_out, _ = model(transformed_features)
self.assertAllEqual(
body_out.get_shape().as_list(),
[BATCH_SIZE, TARGET_LENGTH, 1, hparams.hidden_size])
def testTransformerWithoutProblem(self):
hparams = transformer.transformer_test()
embedded_inputs = np.random.random_sample(
(BATCH_SIZE, INPUT_LENGTH, 1, hparams.hidden_size))
embedded_targets = np.random.random_sample(
(BATCH_SIZE, TARGET_LENGTH, 1, hparams.hidden_size))
transformed_features = {
"inputs": tf.constant(embedded_inputs, dtype=tf.float32),
"targets": tf.constant(embedded_targets, dtype=tf.float32)
}
model = transformer.Transformer(hparams)
body_out, _ = model(transformed_features)
self.assertAllEqual(
body_out.get_shape().as_list(),
[BATCH_SIZE, TARGET_LENGTH, 1, hparams.hidden_size])
