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Re-inputting the generated output #190

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Nov 22, 2016
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Re-inputting the generated output #190

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24 changes: 13 additions & 11 deletions pyexamples/attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,23 +20,24 @@
enc_fwd_lstm = pc.LSTMBuilder(LSTM_NUM_OF_LAYERS, EMBEDDINGS_SIZE, STATE_SIZE, model)
enc_bwd_lstm = pc.LSTMBuilder(LSTM_NUM_OF_LAYERS, EMBEDDINGS_SIZE, STATE_SIZE, model)

dec_lstm = pc.LSTMBuilder(LSTM_NUM_OF_LAYERS, STATE_SIZE*2, STATE_SIZE, model)
dec_lstm = pc.LSTMBuilder(LSTM_NUM_OF_LAYERS, STATE_SIZE*2+EMBEDDINGS_SIZE, STATE_SIZE, model)

lookup = model.add_lookup_parameters( (VOCAB_SIZE, EMBEDDINGS_SIZE))
input_lookup = model.add_lookup_parameters((VOCAB_SIZE, EMBEDDINGS_SIZE))
attention_w1 = model.add_parameters( (ATTENTION_SIZE, STATE_SIZE*2))
attention_w2 = model.add_parameters( (ATTENTION_SIZE, STATE_SIZE*LSTM_NUM_OF_LAYERS*2))
attention_v = model.add_parameters( (1, ATTENTION_SIZE))
decoder_w = model.add_parameters( (VOCAB_SIZE, STATE_SIZE))
decoder_b = model.add_parameters( (VOCAB_SIZE))
output_lookup = model.add_lookup_parameters((VOCAB_SIZE, EMBEDDINGS_SIZE))


def embed_sentence(sentence):
sentence = [EOS] + list(sentence) + [EOS]
sentence = [char2int[c] for c in sentence]

global lookup
global input_lookup

return [lookup[char] for char in sentence]
return [input_lookup[char] for char in sentence]


def run_lstm(init_state, input_vecs):
Expand Down Expand Up @@ -86,15 +87,16 @@ def decode(dec_lstm, vectors, output):
w = pc.parameter(decoder_w)
b = pc.parameter(decoder_b)

s = dec_lstm.initial_state().add_input(pc.vecInput(STATE_SIZE*2))

last_output_embeddings = output_lookup[char2int[EOS]]
s = dec_lstm.initial_state().add_input(pc.concatenate([pc.vecInput(STATE_SIZE*2), last_output_embeddings]))
loss = []
for char in output:
vector = attend(vectors, s)
vector = pc.concatenate([attend(vectors, s), last_output_embeddings])

s = s.add_input(vector)
out_vector = w * s.output() + b
probs = pc.softmax(out_vector)
last_output_embeddings = output_lookup[char]
loss.append(-pc.log(pc.pick(probs, char)))
loss = pc.esum(loss)
return loss
Expand All @@ -114,18 +116,20 @@ def sample(probs):
w = pc.parameter(decoder_w)
b = pc.parameter(decoder_b)

s = dec_lstm.initial_state().add_input(pc.vecInput(STATE_SIZE * 2))
last_output_embeddings = output_lookup[char2int[EOS]]
s = dec_lstm.initial_state().add_input(pc.concatenate([pc.vecInput(STATE_SIZE * 2), last_output_embeddings]))
out = ''
count_EOS = 0
for i in range(len(input)*2):
if count_EOS == 2: break
vector = attend(encoded, s)
vector = pc.concatenate([attend(encoded, s), last_output_embeddings])

s = s.add_input(vector)
out_vector = w * s.output() + b
probs = pc.softmax(out_vector)
probs = probs.vec_value()
next_char = sample(probs)
last_output_embeddings = output_lookup[next_char]
if int2char[next_char] == EOS:
count_EOS += 1
continue
Expand Down Expand Up @@ -155,5 +159,3 @@ def train(model, sentence):

train(model, "it is working")