deeppavlov/core/layers/tf_layers.py
def cudnn_bi_gru(units,
                 n_hidden,
                 seq_lengths=None,
                 n_layers=1,
                 trainable_initial_states=False,
                 name='cudnn_bi_gru',
                 reuse=False):
    """ Fast CuDNN Bi-GRU implementation

    Args:
        units: tf.Tensor with dimensions [B x T x F], where
            B - batch size
            T - number of tokens
            F - features
        n_hidden: dimensionality of hidden state
        seq_lengths: number of tokens in each sample in the batch
        n_layers: number of layers
        trainable_initial_states: whether to create a special trainable variable
                to initialize the hidden states of the network or use just zeros
        name: name of the variable scope to use
        reuse:whether to reuse already initialized variable


    Returns:
        h - all hidden states along T dimension,
            tf.Tensor with dimensionality [B x T x F]
        h_last - last hidden state, tf.Tensor with dimensionality [B x H * 2]
            where H - number of hidden units
    """

    with tf.variable_scope(name, reuse=reuse):
        if seq_lengths is None:
            seq_lengths = tf.ones([tf.shape(units)[0]], dtype=tf.int32) * tf.shape(units)[1]
        with tf.variable_scope('Forward'):
            h_fw, h_last_fw = cudnn_gru_wrapper(units,
                                                n_hidden,
                                                n_layers=n_layers,
                                                trainable_initial_states=trainable_initial_states,
                                                seq_lengths=seq_lengths,
                                                reuse=reuse)

        with tf.variable_scope('Backward'):
            reversed_units = tf.reverse_sequence(units, seq_lengths=seq_lengths, seq_dim=1, batch_dim=0)
            h_bw, h_last_bw = cudnn_gru_wrapper(reversed_units,
                                                n_hidden,
                                                n_layers=n_layers,
                                                trainable_initial_states=trainable_initial_states,
                                                seq_lengths=seq_lengths,
                                                reuse=reuse)
            h_bw = tf.reverse_sequence(h_bw, seq_lengths=seq_lengths, seq_dim=1, batch_dim=0)

    return (h_fw, h_bw), (h_last_fw, h_last_bw)
def cudnn_bi_gru(units,
                 n_hidden,
                 seq_lengths=None,
                 n_layers=1,
                 trainable_initial_states=False,
                 name='cudnn_bi_gru',
                 reuse=False):
    """ Fast CuDNN Bi-GRU implementation

    Args:
        units: tf.Tensor with dimensions [B x T x F], where
            B - batch size
            T - number of tokens
            F - features
        n_hidden: dimensionality of hidden state
        seq_lengths: number of tokens in each sample in the batch
        n_layers: number of layers
        trainable_initial_states: whether to create a special trainable variable
                to initialize the hidden states of the network or use just zeros
        name: name of the variable scope to use
        reuse:whether to reuse already initialized variable


    Returns:
        h - all hidden states along T dimension,
            tf.Tensor with dimensionality [B x T x F]
        h_last - last hidden state, tf.Tensor with dimensionality [B x H * 2]
            where H - number of hidden units
    """

    with tf.variable_scope(name, reuse=reuse):
        if seq_lengths is None:
            seq_lengths = tf.ones([tf.shape(units)[0]], dtype=tf.int32) * tf.shape(units)[1]
        with tf.variable_scope('Forward'):
            h_fw, h_last_fw = cudnn_gru_wrapper(units,
                                                n_hidden,
                                                n_layers=n_layers,
                                                trainable_initial_states=trainable_initial_states,
                                                seq_lengths=seq_lengths,
                                                reuse=reuse)

        with tf.variable_scope('Backward'):
            reversed_units = tf.reverse_sequence(units, seq_lengths=seq_lengths, seq_dim=1, batch_dim=0)
            h_bw, h_last_bw = cudnn_gru_wrapper(reversed_units,
                                                n_hidden,
                                                n_layers=n_layers,
                                                trainable_initial_states=trainable_initial_states,
                                                seq_lengths=seq_lengths,
                                                reuse=reuse)
            h_bw = tf.reverse_sequence(h_bw, seq_lengths=seq_lengths, seq_dim=1, batch_dim=0)

    return (h_fw, h_bw), (h_last_fw, h_last_bw)
def cudnn_bi_gru ( units , n_hidden , seq_lengths = None , n_layers = 1 , trainable_initial_states = False , name = 'cudnn_bi_gru' , reuse = False ) : with tf . variable_scope ( name , reuse = reuse ) : if seq_lengths is None : seq_lengths = tf . ones ( [ tf . shape ( units ) [ 0 ] ] , dtype = tf . int32 ) * tf . shape ( units ) [ 1 ] with tf . variable_scope ( 'Forward' ) : h_fw , h_last_fw = cudnn_gru_wrapper ( units , n_hidden , n_layers = n_layers , trainable_initial_states = trainable_initial_states , seq_lengths = seq_lengths , reuse = reuse ) with tf . variable_scope ( 'Backward' ) : reversed_units = tf . reverse_sequence ( units , seq_lengths = seq_lengths , seq_dim = 1 , batch_dim = 0 ) h_bw , h_last_bw = cudnn_gru_wrapper ( reversed_units , n_hidden , n_layers = n_layers , trainable_initial_states = trainable_initial_states , seq_lengths = seq_lengths , reuse = reuse ) h_bw = tf . reverse_sequence ( h_bw , seq_lengths = seq_lengths , seq_dim = 1 , batch_dim = 0 ) return ( h_fw , h_bw ) , ( h_last_fw , h_last_bw )
Fast CuDNN Bi-GRU implementation

    Args:
        units: tf.Tensor with dimensions [B x T x F], where
            B - batch size
            T - number of tokens
            F - features
        n_hidden: dimensionality of hidden state
        seq_lengths: number of tokens in each sample in the batch
        n_layers: number of layers
        trainable_initial_states: whether to create a special trainable variable
                to initialize the hidden states of the network or use just zeros
        name: name of the variable scope to use
        reuse:whether to reuse already initialized variable


    Returns:
        h - all hidden states along T dimension,
            tf.Tensor with dimensionality [B x T x F]
        h_last - last hidden state, tf.Tensor with dimensionality [B x H * 2]
            where H - number of hidden units
Fast CuDNN Bi - GRU implementation