Hands-On Natural Language Processing with Python

Encoder network

For the encoder component, we utilize a bidirectional RNN with GRU cells. In place of the GRU cell, we could also use an LSTM. The reader may experiment with an LSTM to look at the differences in the model's performance:

def get_cell(csize,dprob):    rnc = GRUCell(csize)    rnc = DropoutWrapper(rnc, input_keep_prob = dprob)    return rncdef encoding_layer(csize, len_s, nl, rinp, dprob):    for l in range(nl):        with tf.variable_scope('encoding_l_{}'.format(l)):            rnn_frnt = get_cell(csize,dprob)            rnn_bkwd = get_cell(csize,dprob)            eop, est = tf.nn.bidirectional_dynamic_rnn(rnn_frnt, rnn_bkwd,                                                                     rinp,                                                                    len_s,                                                                    dtype=tf.float32)    eop = tf.concat(eop,2)    return eop, est

Note that we have concatenated the encoder's output, as this is bidirectional.