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])