tf-keras-skeleton.py 文件源码

def ctc_label_dense_to_sparse(labels, label_lengths):
      """Converts CTC labels from dense to sparse.

      Arguments:
          labels: dense CTC labels.
          label_lengths: length of the labels.

      Returns:
          A sparse tensor representation of the lablels.
      """
      label_shape = array_ops.shape(labels)
      num_batches_tns = array_ops.stack([label_shape[0]])
      max_num_labels_tns = array_ops.stack([label_shape[1]])

      def range_less_than(_, current_input):
        return array_ops.expand_dims(
            math_ops.range(label_shape[1]), 0) < array_ops.fill(
                max_num_labels_tns, current_input)

      init = math_ops.cast(
          array_ops.fill([1, label_shape[1]], 0), dtypes_module.bool)
      dense_mask = functional_ops.scan(
          range_less_than, label_lengths, initializer=init, parallel_iterations=1)
      dense_mask = dense_mask[:, 0, :]

      label_array = array_ops.reshape(
          array_ops.tile(math_ops.range(0, label_shape[1]), num_batches_tns),
          label_shape)
      label_ind = array_ops.boolean_mask(label_array, dense_mask)

      batch_array = array_ops.transpose(
          array_ops.reshape(
              array_ops.tile(math_ops.range(0, label_shape[0]), max_num_labels_tns),
              reverse(label_shape, 0)))
      batch_ind = array_ops.boolean_mask(batch_array, dense_mask)
      indices = array_ops.transpose(
          array_ops.reshape(concatenate([batch_ind, label_ind], axis=0), [2, -1]))

      vals_sparse = array_ops.gather_nd(labels, indices)

      return sparse_tensor.SparseTensor(
          math_ops.to_int64(indices), vals_sparse, math_ops.to_int64(label_shape))