Пример #1
0
    def input_fn(labeled: DataSet, unlabeled: DataSet, labeled_size,
                 unlabeled_size):
        input_dict = {}

        # labeled data
        labeled = labeled.get_batch(labeled_size)
        input_dict['labeled_inputs'] = tf.constant(np.array(labeled.inputs()))
        input_dict['labeled_sequence_length'] = tf.constant(labeled.lengths())
        input_dict['labeled_mask'] = tf.constant(labeled.masks())
        labels = tf.constant(labeled.labels())

        # unlabeled data
        unlabeled = unlabeled.get_batch(unlabeled_size)
        input_dict['unlabeled_inputs'] = tf.constant(
            np.array(unlabeled.inputs()))
        input_dict['unlabeled_sequence_length'] = tf.constant(
            unlabeled.lengths())
        input_dict['unlabeled_mask'] = tf.constant(unlabeled.masks())

        return input_dict, labels
Пример #2
0
    def input_fn(data_set: DataSet, size):
        input_dict = {}

        # labeled data
        data_set = data_set.get_batch(size)
        input_dict['inputs'] = tf.constant(np.array(data_set.inputs()))
        input_dict['sequence_length'] = tf.constant(data_set.lengths())
        input_dict['mask'] = tf.constant(data_set.masks())
        labels = tf.constant(data_set.labels())

        return input_dict, labels
Пример #3
0
    def input_fn(labeled: DataSet, unlabeled: DataSet = None, size: int = BATCH_SIZE):
        input_dict = {
        }

        if unlabeled is not None and unlabeled.size() == 0:
            unlabeled = None

        # labeled data
        labeled = labeled.get_batch(size)
        input_dict['labeled_inputs'] = tf.constant(np.array(labeled.inputs()))
        input_dict['labeled_sequence_length'] = tf.constant(labeled.lengths())
        input_dict['labeled_mask'] = tf.constant(labeled.masks())
        labels = tf.constant(labeled.labels())

        # unlabeled data
        unlabeled = unlabeled is None and labeled or unlabeled.get_batch(labeled.size())
        input_dict['unlabeled_inputs'] = tf.constant(np.array(unlabeled.inputs()))
        input_dict['unlabeled_sequence_length'] = tf.constant(unlabeled.lengths())
        input_dict['unlabeled_mask'] = tf.constant(unlabeled.masks())
        input_dict['unlabeled_size'] = tf.constant(unlabeled.size())
        input_dict['unlabeled_target'] = tf.constant(unlabeled.labels())

        return input_dict, labels