'bidirectional_rnn' question about ner-lstm HOT 8 CLOSED

def multi_layer_birnn_static(config, input, seq_len, dropout):
    nhidden = config.nb_hidden
    ntags   = config.out_dim
    nsteps  = config.nb_steps
    nlayers = config.nb_layers
    cell    = rnn_cell(config.cell_type)

    # input shape: (batch_size, nsteps, in_dim)
    # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input)
    input = tf.unstack(input, nsteps, 1)
    
    def _single_cell():
        _cell = cell(num_units=nhidden, state_is_tuple=True)
        _cell = tf.contrib.rnn.DropoutWrapper(_cell, output_keep_prob=dropout)
        return _cell
        
    fw_cell = tf.contrib.rnn.MultiRNNCell(cells=[_single_cell() for _ in range(nlayers)], state_is_tuple = True)
    bw_cell = tf.contrib.rnn.MultiRNNCell(cells=[_single_cell() for _ in range(nlayers)], state_is_tuple = True)
    
    output, _, _ = tf.contrib.rnn.static_bidirectional_rnn(fw_cell, bw_cell, input ,dtype=tf.float32)
    output = tf.stack(output, 1)
    return output


def multi_layer_birnn_dynamic(config, input, seq_len, dropout):
    nhidden = config.nb_hidden
    ntags   = config.out_dim
    nsteps  = config.nb_steps
    nlayers = config.nb_layers
    cell    = rnn_cell(config.cell_type)

   # permute n_steps and batch_size
   input = tf.transpose(input, [1, 0, 2]) 

    def _single_cell():
        _cell = cell(num_units=nhidden, state_is_tuple=True)
        _cell = tf.contrib.rnn.DropoutWrapper(_cell, output_keep_prob=dropout)
        return _cell
        
    fw_cell = tf.contrib.rnn.MultiRNNCell(cells=[_single_cell() for _ in range(nlayers)], state_is_tuple = True)
    bw_cell = tf.contrib.rnn.MultiRNNCell(cells=[_single_cell() for _ in range(nlayers)], state_is_tuple = True)

    outputs, states = tf.nn.bidirectional_dynamic_rnn(
        cell_fw=fw_cell, 
        cell_bw=bw_cell,
        dtype=tf.float32,
        inputs=input,
        time_major=True, 
        sequence_length=seq_len)
    out_fw, out_bw = outputs
    output = tf.concat([out_fw, out_bw], axis=-1)
    output = tf.transpose(output, [1, 0 ,2])
    return output