def generate(model, input, enc_fwd_lstm, enc_bwd_lstm, dec_lstm):
def sample(probs):
rnd = random.random()
for i, p in enumerate(probs):
rnd -= p
if rnd <= 0: break
return i
embedded = embedd_sentence(model, input)
encoded = encode_sentence(model, enc_fwd_lstm, enc_bwd_lstm, embedded)
w = pc.parameter(model["decoder_w"])
b = pc.parameter(model["decoder_b"])
s = dec_lstm.initial_state().add_input(pc.vecInput(STATE_SIZE * 2))
out = ''
count_EOS = 0
for i in range(len(input)*2):
if count_EOS == 2: break
vector = attend(model, encoded, s)
s = s.add_input(vector)
out_vector = w * s.output() + b
probs = pc.softmax(out_vector)
probs = probs.vec_value()
next_char = sample(probs)
if int2char[next_char] == EOS:
count_EOS += 1
continue
out += int2char[next_char]
return out
def generate(model, input, enc_fwd_lstm, enc_bwd_lstm, dec_lstm):
def sample(probs):
rnd = random.random()
for i, p in enumerate(probs):
rnd -= p
if rnd <= 0: break
return i
embedded = embedd_sentence(model, input)
encoded = encode_sentence(model, enc_fwd_lstm, enc_bwd_lstm, embedded)
w = pc.parameter(model["decoder_w"])
b = pc.parameter(model["decoder_b"])
s = dec_lstm.initial_state().add_input(pc.vecInput(STATE_SIZE * 2))
out = ''
count_EOS = 0
for i in range(len(input) * 2):
if count_EOS == 2: break
vector = attend(model, encoded, s)
s = s.add_input(vector)
out_vector = w * s.output() + b
probs = pc.softmax(out_vector)
probs = probs.vec_value()
next_char = sample(probs)
if int2char[next_char] == EOS:
count_EOS += 1
continue
out += int2char[next_char]
return out
def decode(model, dec_lstm, vectors, output):
output = [EOS] + list(output) + [EOS]
output = [char2int[c] for c in output]
w = pc.parameter(model["decoder_w"])
b = pc.parameter(model["decoder_b"])
s = dec_lstm.initial_state().add_input(pc.vecInput(STATE_SIZE * 2))
loss = []
for char in output:
vector = attend(model, vectors, s)
s = s.add_input(vector)
out_vector = w * s.output() + b
probs = pc.softmax(out_vector)
loss.append(-pc.log(pc.pick(probs, char)))
loss = pc.esum(loss)
return loss
def decode(model, dec_lstm, vectors, output):
output = [EOS] + list(output) + [EOS]
output = [char2int[c] for c in output]
w = pc.parameter(model["decoder_w"])
b = pc.parameter(model["decoder_b"])
s = dec_lstm.initial_state().add_input(pc.vecInput(STATE_SIZE*2))
loss = []
for char in output:
vector = attend(model, vectors, s)
s = s.add_input(vector)
out_vector = w * s.output() + b
probs = pc.softmax(out_vector)
loss.append(-pc.log(pc.pick(probs, char)))
loss = pc.esum(loss)
return loss
