def retrieve_seq_length_op(data):
    """An op to compute the length of a sequence from input shape of [batch_size, n_step(max), n_features],
    it can be used when the features of padding (on right hand side) are all zeros.

    Parameters
    -----------
    data : tensor
        [batch_size, n_step(max), n_features] with zero padding on right hand side.

    Examples
    ---------
    >>> data = [[[1],[2],[0],[0],[0]],
    ...         [[1],[2],[3],[0],[0]],
    ...         [[1],[2],[6],[1],[0]]]
    >>> data = np.asarray(data)
    >>> print(data.shape)
    (3, 5, 1)
    >>> data = tf.constant(data)
    >>> sl = retrieve_seq_length_op(data)
    >>> sess = tf.InteractiveSession()
    >>> tl.layers.initialize_global_variables(sess)
    >>> y = sl.eval()
    [2 3 4]

    Multiple features
    >>> data = [[[1,2],[2,2],[1,2],[1,2],[0,0]],
    ...         [[2,3],[2,4],[3,2],[0,0],[0,0]],
    ...         [[3,3],[2,2],[5,3],[1,2],[0,0]]]
    >>> print(sl)
    [4 3 4]

    References
    ------------
    Borrow from `TFlearn <https://github.com/tflearn/tflearn/blob/master/tflearn/layers/recurrent.py>`__.

    """
    with tf.name_scope('GetLength'):
        used = tf.sign(tf.reduce_max(tf.abs(data), 2))
        length = tf.reduce_sum(used, 1)

        return tf.cast(length, tf.int32)
def retrieve_seq_length_op(data):
    """An op to compute the length of a sequence from input shape of [batch_size, n_step(max), n_features],
    it can be used when the features of padding (on right hand side) are all zeros.

    Parameters
    -----------
    data : tensor
        [batch_size, n_step(max), n_features] with zero padding on right hand side.

    Examples
    ---------
    >>> data = [[[1],[2],[0],[0],[0]],
    ...         [[1],[2],[3],[0],[0]],
    ...         [[1],[2],[6],[1],[0]]]
    >>> data = np.asarray(data)
    >>> print(data.shape)
    (3, 5, 1)
    >>> data = tf.constant(data)
    >>> sl = retrieve_seq_length_op(data)
    >>> sess = tf.InteractiveSession()
    >>> tl.layers.initialize_global_variables(sess)
    >>> y = sl.eval()
    [2 3 4]

    Multiple features
    >>> data = [[[1,2],[2,2],[1,2],[1,2],[0,0]],
    ...         [[2,3],[2,4],[3,2],[0,0],[0,0]],
    ...         [[3,3],[2,2],[5,3],[1,2],[0,0]]]
    >>> print(sl)
    [4 3 4]

    References
    ------------
    Borrow from `TFlearn <https://github.com/tflearn/tflearn/blob/master/tflearn/layers/recurrent.py>`__.

    """
    with tf.name_scope('GetLength'):
        used = tf.sign(tf.reduce_max(tf.abs(data), 2))
        length = tf.reduce_sum(used, 1)

        return tf.cast(length, tf.int32)
def retrieve_seq_length_op ( data ) : with tf . name_scope ( 'GetLength' ) : used = tf . sign ( tf . reduce_max ( tf . abs ( data ) , 2 ) ) length = tf . reduce_sum ( used , 1 ) return tf . cast ( length , tf . int32 )
An op to compute the length of a sequence from input shape of [batch_size, n_step(max), n_features],
    it can be used when the features of padding (on right hand side) are all zeros.

    Parameters
    -----------
    data : tensor
        [batch_size, n_step(max), n_features] with zero padding on right hand side.

    Examples
    ---------
    >>> data = [[[1],[2],[0],[0],[0]],
    ...         [[1],[2],[3],[0],[0]],
    ...         [[1],[2],[6],[1],[0]]]
    >>> data = np.asarray(data)
    >>> print(data.shape)
    (3, 5, 1)
    >>> data = tf.constant(data)
    >>> sl = retrieve_seq_length_op(data)
    >>> sess = tf.InteractiveSession()
    >>> tl.layers.initialize_global_variables(sess)
    >>> y = sl.eval()
    [2 3 4]

    Multiple features
    >>> data = [[[1,2],[2,2],[1,2],[1,2],[0,0]],
    ...         [[2,3],[2,4],[3,2],[0,0],[0,0]],
    ...         [[3,3],[2,2],[5,3],[1,2],[0,0]]]
    >>> print(sl)
    [4 3 4]

    References
    ------------
    Borrow from `TFlearn <https://github.com/tflearn/tflearn/blob/master/tflearn/layers/recurrent.py>`__.
An op to compute the length of a sequence from input shape of [ batch_size n_step ( max ) n_features ] it can be used when the features of padding ( on right hand side ) are all zeros .