def encode(self, inp, **flags):
"""
Takes symbolic input sequence, computes initial state
:param inp: matrix of input tokens [batch, time]
:return: a list of initial decoder state tensors
"""
inp_lengths = infer_length(inp, self.inp_voc.eos_ix)
inp_emb = self.emb_inp(inp)
if self.bidirectional:
outputs, states = tf.nn.bidirectional_dynamic_rnn(
self.enc0,
self.enc1,
inp_emb,
sequence_length=inp_lengths,
dtype=inp_emb.dtype)
return tf.concat(outputs, 2), tf.concat(states, 1)
else:
_, enc_last = tf.nn.dynamic_rnn(self.enc0,
inp_emb,
sequence_length=inp_lengths,
dtype=inp_emb.dtype)
# dec_start = self.dec_start(enc_last)
return _, enc_last
def encode(self, inp, **flags):
"""
Takes symbolic input sequence, computes initial state and embefings for attention
:param inp: matrix of input tokens [batch, time]
:return: a list of initial decoder state tensors
"""
inp_lengths = infer_length(inp, self.inp_voc.eos_ix)
inp_emb = self.emb_inp(inp)
outputs, final_state = tf.nn.bidirectional_dynamic_rnn(
self.encoder_forward_GRU, self.encoder_backward_GRU,
inp_emb, sequence_length=inp_lengths, dtype=inp_emb.dtype
)
return tf.concat(outputs, 2), tf.concat(final_state, 1)
def encode(self, inp, **flags):
"""
Takes symbolic input sequence, computes initial state
:param inp: matrix of input tokens [batch, time]
:return: a list of initial decoder state tensors
"""
inp_lengths = infer_length(inp, self.inp_voc.eos_ix)
inp_emb = self.emb_inp(inp)
_, enc_last = tf.nn.dynamic_rnn(
self.enc0, inp_emb,
sequence_length=inp_lengths,
dtype = inp_emb.dtype)
dec_start = self.dec_start(enc_last[1])
return [dec_start]