def decode(dec_lstm, vectors, output): #Convert the words to word-ids w = dy.parameter(decoder_w) b = dy.parameter(decoder_b) w1 = dy.parameter(attention_w1) input_mat = dy.concatenate_cols(vectors) w1dt = None last_output_embeddings = output_lookup[output[-1]] #s = dec_lstm.initial_state().add_input(dy.concatenate([dy.vecInput(state_size*2), last_output_embeddings])) #s = dec_lstm.initial_state([vectors[-1]]) s = dec_lstm.initial_state() c_t_minus_1 = dy.vecInput(state_size*2) loss = [] for word in output: w1dt = w1dt or w1 * input_mat vector = dy.concatenate([last_output_embeddings, c_t_minus_1]) s = s.add_input(vector) h_t = s.output() c_t = attend(input_mat, s, w1dt) predicted = dy.affine_transform([b, w, dy.concatenate([h_t, c_t])]) cur_loss = dy.pickneglogsoftmax(predicted, word) last_output_embeddings = output_lookup[word] loss.append(cur_loss) c_t_minus_1 = c_t loss = dy.esum(loss) #print "Loss = ", loss return loss
def update_batch(self, words_batch, tags_batch):
dynet.renew_cg()
length = max(len(words) for words in words_batch)
word_ids = np.zeros((length, len(words_batch)), dtype='int32')
for j, words in enumerate(words_batch):
for i, word in enumerate(words):
word_ids[i, j] = self.vw.w2i.get(word, self.UNK)
tag_ids = np.zeros((length, len(words_batch)), dtype='int32')
for j, tags in enumerate(tags_batch):
for i, tag in enumerate(tags):
tag_ids[i, j] = self.vt.w2i.get(tag, self.UNK)
wembs = [dynet.lookup_batch(self._E, word_ids[i]) for i in range(length)]
wembs = [dynet.noise(we, 0.1) for we in wembs]
f_state = self._fwd_lstm.initial_state()
b_state = self._bwd_lstm.initial_state()
fw = [x.output() for x in f_state.add_inputs(wembs)]
bw = [x.output() for x in b_state.add_inputs(reversed(wembs))]
H = dynet.parameter(self._pH)
O = dynet.parameter(self._pO)
errs = []
for i, (f, b) in enumerate(zip(fw, reversed(bw))):
f_b = dynet.concatenate([f,b])
r_t = O * (dynet.tanh(H * f_b))
err = dynet.pickneglogsoftmax_batch(r_t, tag_ids[i])
errs.append(dynet.sum_batches(err))
sum_errs = dynet.esum(errs)
squared = -sum_errs # * sum_errs
losses = sum_errs.scalar_value()
sum_errs.backward()
self._sgd.update()
return losses
def decode(self, dec_lstm, vectors, output):
# output = [EOS] + list(output) + [EOS]
# output = [char2int[c] for c in output]
w = dy.parameter(decoder_w)
b = dy.parameter(decoder_b)
w1 = dy.parameter(attention_w1)
input_mat = dy.concatenate_cols(vectors)
w1dt = w1 * input_mat
last_output_embeddings = output_lookup[EOS]
s = dec_lstm.initial_state()
c_t_previous = dy.vecInput(STATE_SIZE * 2)
loss = []
for word in output:
vector = dy.concatenate([last_output_embeddings, c_t_previous])
s = s.add_input(vector)
h_t = s.output()
c_t, alpha_t = self.attend(input_mat, s, w1dt)
h_c_concat = dy.concatenate([h_t, c_t])
out_vector = dy.affine_transform([b, w, h_c_concat])
loss_current = dy.pickneglogsoftmax(out_vector, word)
last_output_embeddings = output_lookup[word]
loss.append(loss_current)
c_t_previous = c_t
loss = dy.esum(loss)
return loss
def decode(self, dec_lstm, vectors, output_batch):
# output = [EOS] + list(output) + [EOS]
# output = [char2int[c] for c in output]
w = dy.parameter(decoder_w)
b = dy.parameter(decoder_b)
w1 = dy.parameter(attention_w1)
input_mat = dy.concatenate_cols(vectors)
input_len = len(vectors)
w1dt = w1 * input_mat
curr_bsize = len(output_batch)
#n_batch_start = [1] * curr_bsize
#last_output_embeddings = dy.lookup_batch(output_lookup, n_batch_start)
s = dec_lstm.initial_state()
c_t_previous = dy.vecInput(STATE_SIZE * 2)
loss = []
output_batch, masks = self.create_sentence_batch(output_batch)
for i, (word_batch,
mask_word) in enumerate(zip(output_batch[1:], masks[1:]),
start=1):
last_output_embeddings = dy.lookup_batch(output_lookup,
output_batch[i - 1])
vector = dy.concatenate([last_output_embeddings, c_t_previous])
s = s.add_input(vector)
h_t = s.output()
c_t, alpha_t = self.attend(input_mat, s, w1dt, input_len,
curr_bsize)
h_c_concat = dy.concatenate([h_t, c_t])
out_vector = dy.affine_transform([b, w, h_c_concat])
if DROPOUT > 0.0:
out_vector = dy.dropout(out_vector, DROPOUT)
loss_current = dy.pickneglogsoftmax_batch(out_vector,
output_batch[i])
if 0 in mask_word:
mask_vals = dy.inputVector(mask_word)
mask_vals = dy.reshape(mask_vals, (1, ), curr_bsize)
loss_current = loss_current * mask_vals
loss.append(loss_current)
c_t_previous = c_t
loss = dy.esum(loss)
loss = dy.sum_batches(loss) / curr_bsize
#perplexity = loss.value() * curr_bsize / float(sum([x.count(1) for x in masks[1:]]))
return loss
def decode_batch(dec_lstm, input_encodings, output_sentences):
#print "in decode batch"
w = dy.parameter(decoder_w)
b = dy.parameter(decoder_b)
w1 = dy.parameter(attention_w1)
output_words, masks = sentences_to_batch(output_sentences)
decoder_target_input = zip(output_words[1:], masks[1:])
batch_size = len(output_sentences)
input_length = len(input_encodings)
input_mat = dy.concatenate_cols(input_encodings)
#print "Computing w1dt"
w1dt = w1 * input_mat
s = dec_lstm.initial_state()
c_t_minus_1 = dy.vecInput(state_size * 2)
loss = []
for t, (words_t, mask_t) in enumerate(decoder_target_input, start = 1):
last_output_embeddings = dy.lookup_batch(output_lookup, output_words[t-1])
vector = dy.concatenate([last_output_embeddings, c_t_minus_1])
s = s.add_input(vector)
h_t = s.output()
#print "Calling attend"
c_t = attend_batch(input_mat, s, w1dt, batch_size, input_length)
predicted = dy.affine_transform([b, w, dy.concatenate([h_t, c_t])])
if(dropout > 0.):
predicted = dy.dropout(predicted, dropout)
cur_loss = dy.pickneglogsoftmax_batch(predicted, words_t)
c_t_minus_1 = c_t
#Mask the loss in case mask == 0
if 0 in mask_t:
mask = dy.inputVector(mask_t)
mask = dy.reshape(mask, (1, ), batch_size)
cur_loss = cur_loss * mask
loss.append(cur_loss)
#Get the average batch loss
loss = dy.esum(loss)
loss = dy.sum_batches(loss) / batch_size
return loss
def build_sentence_graph(self, sents, labels):
renew_cg()
f_init = self.builder.initial_state()
STOP = vocab.w2i["<stop>"]
START = vocab.w2i["<start>"]
W_exp = parameter(self.R)
b_exp = parameter(self.bias)
state = f_init
# get the wids and masks for each step
tot_words = 0
wids = []
masks = []
for i in range(len(sents[0])):
wids.append([(START if len(sents[0]) - len(sent) > i else
vocab.w2i[sent[i - len(sents[0]) + len(sent)]])
for sent in sents])
#wids.append([(vocab.w2i[sent[i]] if len(sent) > i else STOP) for sent in sents])
mask = [(1 if len(sent) > i else 0) for sent in sents]
masks.append(mask)
tot_words += sum(mask)
#print "wids:"
#print wids
# start the rnn by inputting "<start>"
init_ids = [START] * len(sents)
s = f_init.add_input(dy.lookup_batch(self.lookup, init_ids))
# feed word vectors into the RNN and predict the next word
losses = []
for wid in wids:
# calculate the softmax and loss
score = W_exp * s.output() + b_exp
# update the state of the RNN
wemb = dy.lookup_batch(self.lookup, wid)
s = s.add_input(wemb)
loss = dy.pickneglogsoftmax_batch(score, labels)
losses.append(loss)
return dy.sum_batches(dy.esum(losses))
def myDecode(self, dec_lstm, h_encodings, target_sents):
w = dy.parameter(decoder_w)
b = dy.parameter(decoder_b)
w1 = dy.parameter(attention_w1)
dec_wrds, dec_mask = self.pad_zero(target_sents)
curr_bsize = len(target_sents)
h_len = len(h_encodings)
H_source = dy.concatenate_cols(h_encodings)
s = dec_lstm.initial_state()
ctx_t0 = dy.vecInput(hidden_size * 2)
w1dt = w1 * H_source
loss = []
#print curr_bsize
for sent in range(1, len(dec_wrds)):
last_output_embeddings = dy.lookup_batch(output_lookup, dec_wrds[sent-1])
x = dy.concatenate([ctx_t0, last_output_embeddings])
s = s.add_input(x)
h_t = s.output()
ctx_t, alpha_t = self.attend(H_source, s, w1dt, h_len, curr_bsize)
output_vector = w * dy.concatenate([h_t, ctx_t]) + b
#probs = dy.softmax(output_vector)
ctx_t0 = ctx_t
if dropout_config:
output_vector = dy.dropout(output_vector, dropout_val)
temp_loss = dy.pickneglogsoftmax_batch(output_vector, dec_wrds[sent])
if 0 in dec_mask[sent]:
mask_expr = dy.inputVector(dec_mask[sent])
mask_expr = dy.reshape(mask_expr, (1, ), curr_bsize)
temp_loss = temp_loss * mask_expr
loss.append(temp_loss)
loss = dy.esum(loss)
loss = dy.sum_batches(loss) / batch_size
return loss
def calc_lm_loss(sents):
dy.renew_cg()
# parameters -> expressions
W_exp = dy.parameter(W_sm)
b_exp = dy.parameter(b_sm)
# initialize the RNN
f_init = RNN.initial_state()
# get the wids and masks for each step
tot_words = 0
wids = []
masks = []
for i in range(len(sents[0])):
wids.append([(vw.w2i[sent[i]] if len(sent) > i else STOP) for sent in sents])
mask = [(1 if len(sent) > i else 0) for sent in sents]
masks.append(mask)
tot_words += sum(mask)
# start the rnn by inputting "<start>"
init_ids = [START] * len(sents)
s = f_init.add_input(dy.lookup_batch(WORDS_LOOKUP, init_ids))
# feed word vectors into the RNN and predict the next word
losses = []
for wid, mask in zip(wids, masks):
# calculate the softmax and loss
score = W_exp * s.output() + b_exp
loss = dy.pickneglogsoftmax_batch(score, wid)
# mask the loss if at least one sentence is shorter
if mask[-1] != 1:
mask_expr = dy.inputVector(mask)
mask_expr = dy.reshape(mask_expr, (1,), MB_SIZE)
loss = loss * mask_expr
losses.append(loss)
# update the state of the RNN
wemb = dy.lookup_batch(WORDS_LOOKUP, wid)
s = s.add_input(wemb)
return dy.sum_batches(dy.esum(losses)), tot_words
def build_sentence_graph(self, sents, labels):
renew_cg()
f_init = self.builder.initial_state()
START = vocab.w2i["<start>"]
W_exp = parameter(self.R)
b_exp = parameter(self.bias)
state = f_init
# get the wids and masks for each step
tot_words = 0
wids = []
masks = []
#pad the sequences that are shorter, with a START at the beginning
for i in range(len(sents[0])):
wids.append([(START if len(sents[0]) - len(sent) > i else
vocab.w2i[sent[i - len(sents[0]) + len(sent)]])
for sent in sents])
mask = [(1 if len(sent) > i else 0) for sent in sents]
masks.append(mask)
tot_words += sum(mask)
init_ids = [START] * len(sents)
s = f_init.add_input(dy.lookup_batch(self.lookup, init_ids))
losses = []
for wid in wids:
# calculate the softmax and loss
score = W_exp * s.output() + b_exp
# update the state of the RNN
wemb = dy.lookup_batch(self.lookup, wid)
s = s.add_input(wemb)
loss = dy.pickneglogsoftmax_batch(score, labels)
losses.append(loss)
return dy.sum_batches(dy.esum(losses))
def build_sentence_graph(self, sents, labels):
renew_cg()
f_init = self.builder.initial_state()
START = vocab.w2i["<start>"]
W_exp = parameter(self.R)
b_exp = parameter(self.bias)
state = f_init
# get the wids and masks for each step
tot_words = 0
wids = []
masks = []
#pad the sequences that are shorter, with a START at the beginning
for i in range(len(sents[0])):
wids.append([(START if len(sents[0])-len(sent) > i else vocab.w2i[sent[i - len(sents[0])+len(sent)]]) for sent in sents])
mask = [(1 if len(sent) > i else 0) for sent in sents]
masks.append(mask)
tot_words += sum(mask)
init_ids = [START] * len(sents)
s = f_init.add_input(dy.lookup_batch(self.lookup, init_ids))
losses = []
for wid in wids:
# calculate the softmax and loss
score = W_exp * s.output() + b_exp
# update the state of the RNN
wemb = dy.lookup_batch(self.lookup, wid)
s = s.add_input(wemb)
loss = dy.pickneglogsoftmax_batch(score, labels)
losses.append(loss)
return dy.sum_batches(dy.esum(losses))
predicted = w * dy.concatenate([h_t, c_t]) + b
if(dropout > 0.):
predicted = dy.dropout(predicted, dropout)
cur_loss = dy.pickneglogsoftmax_batch(predicted, output_words[t])
c_t_minus_1 = c_t
#Mask the loss in case mask == 0
if 0 in masks[t]:
mask = dy.inputVector(masks[t])
mask = dy.reshape(mask, (1, ), batch_size)
cur_loss = cur_loss * mask
loss.append(cur_loss)
#Get the average batch loss
loss = dy.esum(loss)
loss = dy.sum_batches(loss) / batch_size
return loss
#Generate the translations from the current trained state of the model
def generate(in_seq, enc_fwd_lstm, enc_bwd_lstm, dec_lstm):
#print "in generate"
dy.renew_cg()
embedded = embed_sentence(in_seq)
encoded = encode_batch(enc_fwd_lstm, enc_bwd_lstm, embedded)
w = dy.parameter(decoder_w)
b = dy.parameter(decoder_b)
w1 = dy.parameter(attention_w1)
input_mat = dy.concatenate_cols(encoded)
w1dt = None
def __step_batch(self, batch):
dy.renew_cg()
W_y = dy.parameter(self.W_y)
b_y = dy.parameter(self.b_y)
F = len(batch[0][0])
num_words = F * len(batch)
src_batch = [x[0] for x in batch]
tgt_batch = [x[1] for x in batch]
src_rev_batch = [list(reversed(x)) for x in src_batch]
# batch = [ [a1,a2,a3,a4,a5], [b1,b2,b3,b4,b5], [c1,c2,c3,c4] ..]
# transpose the batch into
# src_cws: [[a1,b1,c1,..], [a2,b2,c2,..], .. [a5,b5,</S>]]
src_cws = map(list, zip(*src_batch)) # transpose
src_rev_cws = map(list, zip(*src_rev_batch))
# Bidirectional representations
l2r_state = self.l2r_builder.initial_state()
r2l_state = self.r2l_builder.initial_state()
l2r_contexts = []
r2l_contexts = []
for (cw_l2r_list, cw_r2l_list) in zip(src_cws, src_rev_cws):
l2r_state = l2r_state.add_input(
dy.lookup_batch(self.src_lookup, [
self.src_token_to_id.get(cw_l2r, 0)
for cw_l2r in cw_l2r_list
]))
r2l_state = r2l_state.add_input(
dy.lookup_batch(self.src_lookup, [
self.src_token_to_id.get(cw_r2l, 0)
for cw_r2l in cw_r2l_list
]))
l2r_contexts.append(
l2r_state.output()) #[<S>, x_1, x_2, ..., </S>]
r2l_contexts.append(
r2l_state.output()) #[</S> x_n, x_{n-1}, ... <S>]
r2l_contexts.reverse() #[<S>, x_1, x_2, ..., </S>]
# Combine the left and right representations for every word
h_fs = []
for (l2r_i, r2l_i) in zip(l2r_contexts, r2l_contexts):
h_fs.append(dy.concatenate([l2r_i, r2l_i]))
h_fs_matrix = dy.concatenate_cols(h_fs)
losses = []
# Decoder
# batch = [ [a1,a2,a3,a4,a5], [b1,b2,b3,b4,b5], [c1,c2,c3,c4] ..]
# transpose the batch into
# tgt_cws: [[a1,b1,c1,..], [a2,b2,c2,..], .. [a5,b5,</S>]]
# masks: [1,1,1,..], [1,1,1,..], ...[1,1,0,..]]
tgt_cws = []
masks = []
maxLen = max([len(l) for l in tgt_batch])
for i in range(maxLen):
tgt_cws.append([])
masks.append([])
for sentence in tgt_batch:
for j in range(maxLen):
if j > len(sentence) - 1:
tgt_cws[j].append('</S>')
masks[j].append(0)
else:
tgt_cws[j].append(sentence[j])
masks[j].append(1)
c_t = dy.vecInput(self.hidden_size * 2)
start = dy.concatenate(
[dy.lookup(self.tgt_lookup, self.tgt_token_to_id['<S>']), c_t])
dec_state = self.dec_builder.initial_state().add_input(start)
for (cws, nws, mask) in zip(tgt_cws, tgt_cws[1:], masks):
h_e = dec_state.output()
_, c_t = self.__attention_mlp(h_fs_matrix, h_e, F)
# Get the embedding for the current target word
embed_t = dy.lookup_batch(
self.tgt_lookup,
[self.tgt_token_to_id.get(cw, 0) for cw in cws])
# Create input vector to the decoder
x_t = dy.concatenate([embed_t, c_t])
dec_state = dec_state.add_input(x_t)
y_star = dy.affine_transform([b_y, W_y, dec_state.output()])
loss = dy.pickneglogsoftmax_batch(
y_star, [self.tgt_token_to_id.get(nw, 0) for nw in nws])
mask_exp = dy.reshape(dy.inputVector(mask), (1, ), len(mask))
loss = loss * mask_exp
losses.append(loss)
return dy.sum_batches(dy.esum(losses)), num_words