def __call__(self, H,is_train=True):
"""
:param xs: a list of ngrams (or words if win is set to 1)
:return: embeddings looked from tables
"""
seq_len = len(H)
if is_train:
# in the training phase, perform dropout
W1 = dy.dropout(self.W1, self.dropout_rate)
W2 = dy.dropout(self.W2, self.dropout_rate)
else:
W1= self.W1
W2 = self.W2
pool= dy.average(H)
aspect_attentions = []
Weights=[]
for t in range(seq_len):
ht = H[t]
scores = dy.tanh(dy.transpose(ht)*W1*pool+self.bd)
# print(scores.value())
Weights.append(scores.value() )
ht_hat=dy.cmult(dy.softmax(scores),ht)
# print(ht_hat.value())
aspect_attentions.append(ht_hat)
Weights_np=[]
return aspect_attentions,Weights_np
def enable_dropout(self):
self.fwdRNN.set_dropout(0.3)
self.bwdRNN.set_dropout(0.3)
self.cfwdRNN.set_dropout(0.3)
self.cbwdRNN.set_dropout(0.3)
self.w1 = dy.dropout(self.w1, 0.3)
self.b1 = dy.dropout(self.b1, 0.3)
def init_sequence(self, test=False):
self.test = test
if not test:
self.dropout_mask_x = dy.dropout(dy.ones((self.n_in, )),
self.dropout_x)
self.dropout_mask_h = dy.dropout(dy.ones((self.n_hidden, )),
self.dropout_h)
def expr_for_tree(self,xt,tree,node,is_train):
if is_train:
# in the training phase, perform dropout
W_dropout = dy.dropout(self.WP, self.dropout_rate)
WR_dropout = dy.dropout(self.WR, self.dropout_rate)
WC_dropout = dy.dropout(self.WC, self.dropout_rate)
else:
W_dropout = self.WP
WR_dropout = self.WR
WC_dropout = self.WC
if node is None or node.is_leaf():
Wx = W_dropout * xt
# h = dy.tanh(Wx + self.bc)
h = dy.tanh(dy.affine_transform([self.bc, self.WC, xt]))
return h
#get child nodes
children=tree.children(node.identifier)
children_sum=dy.zeros((self.n_out))
for i in range(len(children)):
hc=self.expr_for_tree(xt=xt,tree=tree,node=children[i],is_train=is_train)
rt = dy.logistic(self.WR * xt +self.UR*hc+self.br)
children_sum=children_sum+dy.cmult(rt, hc)
Wx = W_dropout * xt
h = dy.tanh(Wx + self.bp+self.UP*children_sum)
return h
def build_tagging_graph(self, sentence):
dy.renew_cg()
embeddings = [self.word_rep(w) for w in sentence]
lstm_out = self.bi_lstm.transduce(embeddings)
H = dy.parameter(self.lstm_to_tags_params)
Hb = dy.parameter(self.lstm_to_tags_bias)
O = dy.parameter(self.mlp_out)
Ob = dy.parameter(self.mlp_out_bias)
scores = []
if options.bigram:
for rep, word in zip(lstm_out, sentence):
bi1 = dy.lookup(self.bigram_lookup,
word[0],
update=self.we_update)
bi2 = dy.lookup(self.bigram_lookup,
word[1],
update=self.we_update)
if self.dropout is not None:
bi1 = dy.dropout(bi1, self.dropout)
bi2 = dy.dropout(bi2, self.dropout)
score_t = O * dy.tanh(H * dy.concatenate([bi1, rep, bi2]) +
Hb) + Ob
scores.append(score_t)
else:
for rep in lstm_out:
score_t = O * dy.tanh(H * rep + Hb) + Ob
scores.append(score_t)
return scores
def build_tagging_graph(self, batch):
self.initialize_paramerets()
# get the word vectors.
batch_embs = self.word_rep(batch)
# feed word vectors into biLSTM
fw_exps = self.f_init.transduce(batch_embs)
bw_exps = self.b_init.transduce(reversed(batch_embs))
# biLSTM states
bi_exps = [
dy.concatenate([f, b]) for f, b in zip(fw_exps, reversed(bw_exps))
]
# 2nd biLSTM
fw_exps = self.f2_init.transduce(bi_exps)
bw_exps = self.b2_init.transduce(reversed(bi_exps))
# biLSTM states
bi_exps = dy.concatenate([
dy.concatenate([f, b]) for f, b in zip(fw_exps, reversed(bw_exps))
],
d=1)
aT = self.meta.activation(self.aw * bi_exps + self.ab)
alpha = self.av * aT
attn = dy.softmax(alpha, 1)
weighted_sum = dy.reshape(bi_exps * dy.transpose(attn),
(self.meta.lstm_word_dim * 2, ))
if not self.eval:
weighted_sum = dy.dropout(weighted_sum, 0.3)
xh = self.meta.activation(self.w1 * weighted_sum + self.b1)
if not self.eval:
xh = dy.dropout(xh, 0.3)
xo = self.w2 * xh + self.b2
return xo
def apply(self, sent1, sent2):
eL = dy.parameter(self.linear)
sent1 = dy.inputTensor(self.embedding.all_embeds_from_ix(sent1)) * eL
sent2 = dy.inputTensor(self.embedding.all_embeds_from_ix(sent2)) * eL
out1, out2 = self.feed_F(sent1, sent2)
e_out = out1 * dy.transpose(out2)
prob_f_1 = dy.softmax(e_out)
score = dy.transpose(e_out)
prob_f_2 = dy.softmax(score)
sent1_allign = dy.concatenate_cols([sent1, prob_f_1 * sent2])
sent2_allign = dy.concatenate_cols([sent2, prob_f_2 * sent1])
out_g_1, out_g_2 = self.feed_G(sent1_allign, sent2_allign)
sent1_out_g = dy.sum_dim(out_g_1, [0])
sent2_out_g = dy.sum_dim(out_g_2, [0])
concat = dy.transpose(dy.concatenate([sent1_out_g, sent2_out_g]))
h_step_1 = dy.parameter(self.h_step_1)
sent_h = dy.rectify(dy.dropout(concat, 0.2) * h_step_1)
h_step_2 = dy.parameter(self.h_step_2)
sent_h = dy.rectify(dy.dropout(sent_h, 0.2) * h_step_2)
final = dy.parameter(self.linear2)
final = dy.transpose(sent_h * final)
return final
def recurrence(self, xt, hmtm1, h_history_tm1, dropout_flag):
"""
:param xt: input vector at the time step t
:param hmtm1: hidden memories in previous n_steps steps
:param h_tilde_tm1: previous hidden summary
:param dropout_flag: make a decision for conducting partial dropout
:return:
"""
score = dy.concatenate([dy.dot_product(self.u, dy.tanh( \
self.W_h * hmtm1[i] + self.W_x * xt + self.W_htilde * h_history_tm1)) for i in range(self.n_steps)])
# normalize the attention score
score = dy.softmax(score)
# shape: (1, n_out), history of [h[t-n_steps-1], ..., h[t-2]]
h_history_t = dy.reshape(dy.transpose(score) * hmtm1[:-1], d=(self.n_out,))
htm1 = hmtm1[-1]
#h_tilde_t = dy.concatenate([h_history_t, htm1])
h_tilde_t = htm1 + dy.rectify(h_history_t)
if dropout_flag:
# perform partial dropout, i.e., add dropout over the matrices W_x*
rt = dy.logistic(dy.dropout(self.W_xr, self.dropout_rate) * xt + self.W_hr * h_tilde_t + self.br)
zt = dy.logistic(dy.dropout(self.W_xz, self.dropout_rate) * xt + self.W_hz * h_tilde_t + self.bz)
ht_hat = dy.tanh(dy.dropout(self.W_xh, self.dropout_rate) * xt + self.W_hh * dy.cmult(rt, h_tilde_t) \
+ self.bh)
ht = dy.cmult(zt, h_tilde_t) + dy.cmult((1.0 - zt), ht_hat)
else:
rt = dy.logistic(self.W_xr * xt + self.W_hr * h_tilde_t + self.br)
zt = dy.logistic(self.W_xz * xt + self.W_hz * h_tilde_t + self.bz)
ht_hat = dy.tanh(self.W_xh * xt + self.W_hh * dy.cmult(rt, h_tilde_t) + self.bh)
ht = dy.cmult(zt, h_tilde_t) + dy.cmult((1.0 - zt), ht_hat)
hmt = dy.concatenate([hmtm1[1:], dy.reshape(ht, (1, self.n_out))])
return hmt, h_history_t
def word_assoc_score(self, source_idx, target_idx, relation):
"""
NOTE THAT DROPOUT IS BEING APPLIED HERE
:param source_idx: embedding index of source atom
:param target_idx: embedding index of target atom
:param relation: relation type
:return: score
"""
# prepare
s = self.embeddings[source_idx]
if self.no_assoc:
A = dy.const_parameter(self.word_assoc_weights[relation])
else:
A = dy.parameter(self.word_assoc_weights[relation])
dy.dropout(A, self.dropout)
t = self.embeddings[target_idx]
# compute
if self.mode == BILINEAR_MODE:
return dy.transpose(s) * A * t
elif self.mode == DIAG_RANK1_MODE:
diag_A = dyagonalize(A[0])
rank1_BC = A[1] * dy.transpose(A[2])
ABC = diag_A + rank1_BC
return dy.transpose(s) * ABC * t
elif self.mode == TRANSLATIONAL_EMBED_MODE:
return -dy.l2_norm(s - t + A)
elif self.mode == DISTMULT:
return dy.sum_elems(dy.cmult(dy.cmult(s, A), t))
def calc_loss(self, enc_sen, y_adv, vec_drop, train):
"""
the attacker core functionality.
mlp function, with (possibely) multi layers, and at least one.
:param enc_sen:
:param y_adv:
:param vec_drop:
:param train:
:return:
"""
w = dy.parameter(self._params["adv_w0"])
b = dy.parameter(self._params["adv_b0"])
if train:
drop = self._dropout
out = dy.dropout(enc_sen, vec_drop)
else:
drop = 0
out = enc_sen
out = dy.tanh(dy.dropout(dy.affine_transform([b, w, out]), drop))
if self._mlp_layers > 2:
for i in range(self._mlp_layers - 2):
w = dy.parameter(self._params["adv_w" + str(i + 1)])
b = dy.parameter(self._params["adv_b" + str(i + 1)])
out = dy.tanh(
dy.dropout(dy.affine_transform([b, w, out]), drop))
w = dy.parameter(self._params["adv_w" + str(self._mlp_layers - 1)])
b = dy.parameter(self._params["adv_b" + str(self._mlp_layers - 1)])
out = dy.affine_transform([b, w, out])
task_probs = dy.softmax(out)
adv_loss = dy.pickneglogsoftmax(out, y_adv)
return adv_loss, np.argmax(task_probs.npvalue())
def __call__(self, H1,H2,H3,is_train=True):
"""
:param xs: a list of ngrams (or words if win is set to 1)
:return: embeddings looked from tables
"""
seq_len = len(H1)
if is_train:
# in the training phase, perform dropout
W1 = dy.dropout(self.W1, self.dropout_rate)
W2 = dy.dropout(self.W2, self.dropout_rate)
W3 = dy.dropout(self.W3, self.dropout_rate)
else:
W1= self.W1
W2 = self.W2
W3 = self.W3
H = []
for t in range(seq_len):
ht_hat = dy.tanh(W1*H1[t]+W2*H2[t]+W3*H3[t]+self.bd)
H.append(ht_hat)
return H
def word_assoc_score(self, source_idx, target_idx, relation):
"""
NOTE THAT DROPOUT IS BEING APPLIED HERE
:param source_idx: embedding index of source atom
:param target_idx: embedding index of target atom
:param relation: relation type
:return: score
"""
# prepare
s = self.embeddings[source_idx]
if self.no_assoc:
A = dy.const_parameter(self.word_assoc_weights[relation])
else:
A = dy.parameter(self.word_assoc_weights[relation])
dy.dropout(A, self.dropout)
t = self.embeddings[target_idx]
# compute
if self.mode == BILINEAR_MODE:
return dy.transpose(s) * A * t
elif self.mode == DIAG_RANK1_MODE:
diag_A = dyagonalize(A[0])
rank1_BC = A[1] * dy.transpose(A[2])
ABC = diag_A + rank1_BC
return dy.transpose(s) * ABC * t
elif self.mode == TRANSLATIONAL_EMBED_MODE:
return -dy.l2_norm(s - t + A)
elif self.mode == DISTMULT:
return dy.sum_elems(dy.cmult(dy.cmult(s, A), t))
def adv_mlp(self, vec_sen, adv_ind, train, vec_drop):
"""
calculating the adversarial mlp over the sentence representation vector.
more than a single adversarial mlp is supported
"""
if train:
drop = self._dropout
out = dy.dropout(vec_sen, vec_drop)
else:
drop = 0
out = vec_sen
for i in range(self._adv_depth):
w = dy.parameter(self._params["adv_" + str(adv_ind) + "_w" +
str(i + 1)])
b = dy.parameter(self._params["adv_" + str(adv_ind) + "_b" +
str(i + 1)])
out = dy.tanh(dy.dropout(dy.affine_transform([b, w, out]), drop))
w = dy.parameter(self._params["adv_" + str(adv_ind) + "_w" +
str(self._adv_depth + 1)])
b = dy.parameter(self._params["adv_" + str(adv_ind) + "_b" +
str(self._adv_depth + 1)])
out = dy.affine_transform([b, w, out])
return out
def forward(self, features, dropout=False):
# extract ids for word, pos and label
word_ids = [self.vocab.word2id(w) for w in features[:20]]
pos_ids = [self.vocab.pos2id(p) for p in features[20:40]]
label_ids = [self.vocab.label2id(l) for l in features[40:52]]
# extract embedding from features
word_embeds = [self.word_embedding[wid] for wid in word_ids]
pos_embeds = [self.pos_embedding[pid] for pid in pos_ids]
label_embeds = [self.label_embedding[lid] for lid in label_ids]
# concatenating all features
embedding_layer = dynet.concatenate(word_embeds + pos_embeds +
label_embeds)
# calculating the hidden layers
hidden_1 = self.transfer(self.hidden_layer_1.expr() * embedding_layer +
self.hidden_layer_bias_1.expr())
if dropout:
hidden_1 = dynet.dropout(hidden_1, self.properties.dropout)
hidden_2 = self.transfer(self.hidden_layer_2.expr() * hidden_1 +
self.hidden_layer_bias_2.expr())
if dropout:
hidden_2 = dynet.dropout(hidden_2, self.properties.dropout)
# calculating the output layer
output = self.output_layer.expr() * hidden_2 + self.output_bias.expr()
return output
def _build_computation_graph(self, words, train_mode=True):
"""
Builds the computational graph.
"""
dy.renew_cg()
# turn parameters into expressions
softmax_weight_exp = dy.parameter(self.softmax_weight)
softmax_bias_exp = dy.parameter(self.softmax_bias)
word_reps = [self._word_rep(word) for word in words]
embs = dy.concatenate(word_reps, d=1)
if self.pooling_method == "average":
average_emb = dy.mean_dim(embs, d=1)
elif self.pooling_method == "max":
average_emb = dy.max_dim(embs, d=1)
else:
raise NotImplementedError
average_emb = dy.reshape(average_emb, (self.word_embedding_size,))
if self.average_dropout is not None:
dy.dropout(average_emb, p=self.average_dropout)
return softmax_weight_exp * average_emb + softmax_bias_exp
def evaluate_recurrent(self, fwd_bigrams, unigrams, test=False):
fwd1 = self.fwd_lstm1.initial_state()
back1 = self.back_lstm1.initial_state()
fwd2 = self.fwd_lstm2.initial_state()
back2 = self.back_lstm2.initial_state()
fwd_input = []
for i in range(len(unigrams)):
bivec = dynet.lookup(self.bigram_embed, fwd_bigrams[i])
univec = dynet.lookup(self.unigram_embed, unigrams[i])
vec = dynet.concatenate([bivec, univec])
# fwd_input.append(dynet.tanh(self.embed2lstm_W*vec))
fwd_input.append(vec)
back_input = []
for i in range(len(unigrams)):
bivec = dynet.lookup(self.bigram_embed, fwd_bigrams[i + 1])
univec = dynet.lookup(self.unigram_embed, unigrams[i])
vec = dynet.concatenate([bivec, univec])
# back_input.append(dynet.tanh(self.embed2lstm_W*vec))
back_input.append(vec)
fwd1_out = []
for vec in fwd_input:
fwd1 = fwd1.add_input(vec)
fwd_vec = fwd1.output()
fwd1_out.append(fwd_vec)
back1_out = []
for vec in reversed(back_input):
back = back1.add_input(vec)
back1_vec = back.output()
back1_out.append(back1_vec)
lsmt2_input = []
for (f, b) in zip(fwd1_out, reversed(back1_out)):
lsmt2_input.append(dynet.concatenate([f, b]))
fwd2_out = []
for vec in lsmt2_input:
if self.droprate > 0 and not test:
vec = dynet.dropout(vec, self.droprate)
fwd2 = fwd2.add_input(vec)
fwd_vec = fwd2.output()
fwd2_out.append(fwd_vec)
back2_out = []
for vec in reversed(lsmt2_input):
if self.droprate > 0 and not test:
vec = dynet.dropout(vec, self.droprate)
back2 = back2.add_input(vec)
back_vec = back2.output()
back2_out.append(back_vec)
# fwd_out = [dynet.concatenate([f1,f2]) for (f1,f2) in zip(fwd1_out,fwd2_out)]
# back_out = [dynet.concatenate([b1,b2]) for (b1,b2) in zip(back1_out,back2_out)]
return fwd2_out, back2_out[::-1]
def out_layer(self,x,dropout):
if dropout:
W = dy.dropout(self._W2,0.3)
b = dy.dropout(self._b2,0.3)
else:
W = self._W2
b = self._b2
return (W*x+b)
def set_dropouts(self, input_drop=0, recur_drop=0):
self.input_drop = input_drop
self.recur_drop = recur_drop
self.input_drop_mask = dy.dropout(dy.ones(self.input_size),
self.input_drop)
self.recur_drop_mask = dy.dropout(dy.ones(self.recur_size),
self.recur_drop)
def out_layer(self, x, dropout):
if dropout:
W = dy.dropout(dy.parameter(self._W2), 0.3)
b = dy.dropout(dy.parameter(self._b2), 0.3)
else:
W = dy.parameter(self._W2)
b = dy.parameter(self._b2)
return (W * x + b)
def hid_2_layer(self,x,dropout):
if dropout:
W = dy.dropout(self._W12,0.3)
b = dy.dropout(self._b12,0.3)
else:
W = self._W12
b = self._b12
return self.activation(W*x+b)
def hid_2_layer(self, x, dropout):
if dropout:
W = dy.dropout(dy.parameter(self._W12), 0.3)
b = dy.dropout(dy.parameter(self._b12), 0.3)
else:
W = dy.parameter(self._W12)
b = dy.parameter(self._b12)
return self.activation(W * x + b)
def hid_layer(self, x, dropout):
if dropout:
W = dy.dropout(dy.parameter(self._W1), 0.3)
b = dy.dropout(dy.parameter(self._b1), 0.3)
else:
W = dy.parameter(self._W1)
b = dy.parameter(self._b1)
return dy.rectify(W * x + b)
def evaluate_recurrent(self, word_inds, tag_inds, test=False):
fwd1 = self.fwd_lstm1.initial_state()
back1 = self.back_lstm1.initial_state()
fwd2 = self.fwd_lstm2.initial_state()
back2 = self.back_lstm2.initial_state()
sentence = []
for (w, t) in zip(word_inds, tag_inds):
wordvec = dynet.lookup(self.word_embed, w)
tagvec = dynet.lookup(self.tag_embed, t)
vec = dynet.concatenate([wordvec, tagvec])
sentence.append(vec)
fwd1_out = []
for vec in sentence:
fwd1 = fwd1.add_input(vec)
fwd_vec = fwd1.output()
fwd1_out.append(fwd_vec)
back1_out = []
for vec in reversed(sentence):
back1 = back1.add_input(vec)
back_vec = back1.output()
back1_out.append(back_vec)
lstm2_input = []
for (f, b) in zip(fwd1_out, reversed(back1_out)):
lstm2_input.append(dynet.concatenate([f, b]))
fwd2_out = []
for vec in lstm2_input:
if self.droprate > 0 and not test:
vec = dynet.dropout(vec, self.droprate)
fwd2 = fwd2.add_input(vec)
fwd_vec = fwd2.output()
fwd2_out.append(fwd_vec)
back2_out = []
for vec in reversed(lstm2_input):
if self.droprate > 0 and not test:
vec = dynet.dropout(vec, self.droprate)
back2 = back2.add_input(vec)
back_vec = back2.output()
back2_out.append(back_vec)
fwd_out = [
dynet.concatenate([f1, f2])
for (f1, f2) in zip(fwd1_out, fwd2_out)
]
back_out = [
dynet.concatenate([b1, b2])
for (b1, b2) in zip(back1_out, back2_out)
]
return fwd_out, back_out[::-1]
def _calculate_train_score(self, sentence):
"""Same as _calculate_score, but applies dropout after embedding and bi-lstm layers, used for training"""
embeddings = [self.lookup[w] for w in sentence]
embeddings = [dy.dropout(e, self.dropout_rate) for e in embeddings]
bi_lstm_output = self.bilstm.transduce(embeddings)
bi_lstm_output = [
dy.dropout(o, self.dropout_rate) for o in bi_lstm_output
]
return [self.w * o + self.b for o in bi_lstm_output]
def hid_layer(self,x,y,dropout):
if dropout:
W_h = dy.dropout(self._W1_h,0.3)
W_d = dy.dropout(self._W1_d,0.3)
b = dy.dropout(self._b1,0.3)
else:
W_h = self._W1_h
W_d = self._W1_d
b = self._b1
return self.activation(W_h*x+W_d*y+b)
def hid_layer(self, x, y, dropout):
if dropout:
W_h = dy.dropout(dy.parameter(self._W1_h), 0.3)
W_d = dy.dropout(dy.parameter(self._W1_d), 0.3)
b = dy.dropout(dy.parameter(self._b1), 0.3)
else:
W_h = dy.parameter(self._W1_h)
W_d = dy.parameter(self._W1_d)
b = dy.parameter(self._b1)
return self.activation(W_h * x + W_d * y + b)
def __call__(self, sentence1, sentence2):
W_1 = dy.parameter(self.W_1)
# relu activation with dropout
out1 = dy.rectify(dy.dropout(sentence1, self.drop_param) * W_1)
out2 = dy.rectify(dy.dropout(sentence2, self.drop_param) * W_1)
W_2 = dy.parameter(self.W_2)
out1 = dy.rectify(dy.dropout(out1, self.drop_param) * W_2)
out2 = dy.rectify(dy.dropout(out2, self.drop_param) * W_2)
return out1, out2
def cal_scores(self, src_encodings, masks, train):
src_len = len(src_encodings)
batch_size = src_encodings[0].dim()[1]
heads_LRlayer = []
mods_LRlayer = []
for encoding in src_encodings:
heads_LRlayer.append(
self.leaky_ReLu(self.b_head.expr() +
self.W_head.expr() * encoding))
mods_LRlayer.append(
self.leaky_ReLu(self.b_mod.expr() +
self.W_mod.expr() * encoding))
heads_labels = []
heads = []
labels = []
neg_inf = dy.constant(1, -float("inf"))
for row in range(
1, src_len
): #exclude root @ index=0 since roots do not have heads
scores_idx = []
for col in range(src_len):
dist = col - row
mdist = self.dist_max
dist_i = (min(dist, mdist - 1) + mdist if dist >= 0 else int(
min(-1.0 * dist, mdist - 1)))
dist_vec = dy.lookup_batch(self.dlookup, [dist_i] * batch_size)
if train:
input_vec = dy.concatenate([
dy.esum([
dy.dropout(heads_LRlayer[col], self.dropout),
dy.dropout(mods_LRlayer[row], self.dropout)
]), dist_vec
])
else:
input_vec = dy.concatenate([
dy.esum([heads_LRlayer[col], mods_LRlayer[row]]),
dist_vec
])
score = self.scoreHeadModLabel(input_vec, train)
mask = masks[row] and masks[col]
join_scores = []
for bdx in range(batch_size):
if (mask[bdx] == 1):
join_scores.append(dy.pick_batch_elem(score, bdx))
else:
join_scores.append(
dy.concatenate([neg_inf] * self.n_labels))
scores_idx.append(dy.concatenate_to_batch(join_scores))
heads_labels.append(dy.concatenate(scores_idx))
return heads_labels
def forward(self, s1, s2, label=None):
eL = dy.parameter(self.embeddingLinear)
s1 = dy.inputTensor(s1) * eL
s2 = dy.inputTensor(s2) * eL
# F step
Lf1 = dy.parameter(self.mlpF1)
Fs1 = dy.rectify(dy.dropout(s1, 0.2) * Lf1)
Fs2 = dy.rectify(dy.dropout(s2, 0.2) * Lf1)
Lf2 = dy.parameter(self.mlpF2)
Fs1 = dy.rectify(dy.dropout(Fs1, 0.2) * Lf2)
Fs2 = dy.rectify(dy.dropout(Fs2, 0.2) * Lf2)
# Attention scoring
score1 = Fs1 * dy.transpose(Fs2)
prob1 = dy.softmax(score1)
score2 = dy.transpose(score1)
prob2 = dy.softmax(score2)
# Align pairs using attention
s1Pairs = dy.concatenate_cols([s1, prob1 * s2])
s2Pairs = dy.concatenate_cols([s2, prob2 * s1])
# G step
Lg1 = dy.parameter(self.mlpG1)
Gs1 = dy.rectify(dy.dropout(s1Pairs, 0.2) * Lg1)
Gs2 = dy.rectify(dy.dropout(s2Pairs, 0.2) * Lg1)
Lg2 = dy.parameter(self.mlpG2)
Gs1 = dy.rectify(dy.dropout(Gs1, 0.2) * Lg2)
Gs2 = dy.rectify(dy.dropout(Gs2, 0.2) * Lg2)
# Sum
Ss1 = dy.sum_dim(Gs1, [0])
Ss2 = dy.sum_dim(Gs2, [0])
concatS12 = dy.transpose(dy.concatenate([Ss1, Ss2]))
# H step
Lh1 = dy.parameter(self.mlpH1)
Hs = dy.rectify(dy.dropout(concatS12, 0.2) * Lh1)
Lh2 = dy.parameter(self.mlpH2)
Hs = dy.rectify(dy.dropout(Hs, 0.2) * Lh2)
# Final layer
final_layer = dy.parameter(self.final_layer)
final = dy.transpose(Hs * final_layer)
# Label can be 0...
if label != None:
return dy.pickneglogsoftmax(final, label)
else:
out = dy.softmax(final)
return np.argmax(out.npvalue())
def greedy_search(self, char_seq, truth = None, mu =0.):
init_state = self.params['lstm'].initial_state().add_input(self.param_exprs['<bos>'])
init_y = dy.tanh(self.param_exprs['pW'] * init_state.output() + self.param_exprs['pb'])
init_score = dy.scalarInput(0.)
init_sentence = Sentence(score=init_score.scalar_value(),score_expr=init_score,LSTMState =init_state, y= init_y , prevState = None, wlen=None, golden=True)
if truth is not None:
cembs = [ dy.dropout(dy.lookup(self.params['embed'],char),self.options['dropout_rate']) for char in char_seq ]
else:
cembs = [dy.lookup(self.params['embed'],char) for char in char_seq ]
#cembs = [ dy.dropout(dy.lookup(self.params['embed'],char),self.options['dropout_rate']) for char in char_seq ]
start_agenda = init_sentence
agenda = [start_agenda]
for idx, _ in enumerate(char_seq,1): # from left to right, character by character
now = None
for wlen in range(1,min(idx,self.options['max_word_len'])+1): # generate word candidate vectors
# join segmentation sent + word
word = self.word_repr(char_seq[idx-wlen:idx], cembs[idx-wlen:idx])
sent = agenda[idx-wlen]
if truth is not None:
word = dy.dropout(word,self.options['dropout_rate'])
word_score = dy.dot_product(word,self.param_exprs['U'])
if truth is not None:
golden = sent.golden and truth[idx-1]==wlen
margin = dy.scalarInput(mu*wlen if truth[idx-1]!=wlen else 0.)
score = margin + sent.score_expr + dy.dot_product(sent.y, word) + word_score
else:
golden = False
score = sent.score_expr + dy.dot_product(sent.y, word) + word_score
good = (now is None or now.score < score.scalar_value())
if golden or good:
new_state = sent.LSTMState.add_input(word)
new_y = dy.tanh(self.param_exprs['pW'] * new_state.output() + self.param_exprs['pb'])
new_sent = Sentence(score=score.scalar_value(),score_expr=score,LSTMState=new_state,y=new_y, prevState=sent, wlen=wlen, golden=golden)
if good:
now = new_sent
if golden:
golden_sent = new_sent
agenda.append(now)
if truth is not None and truth[idx-1]>0 and (not now.golden):
return (now.score_expr - golden_sent.score_expr)
if truth is not None:
return (now.score_expr - golden_sent.score_expr)
return agenda
def __convolve__(self, embeddings, F, b, W1, bW1):
sntlen = len(embeddings)
emb = dy.concatenate_cols(embeddings)
x = dy.conv2d_bias(emb, F, b, [1, 1], is_valid=False)
x = dy.rectify(x)
x = dy.maxpooling2d(x, [1, sntlen], [1, 1], is_valid=True)
if self.DROPOUT > 0:
dy.dropout(x, self.DROPOUT)
f = dy.reshape(x, (self.EMB_DIM * 1 * 100, ))
return W1 * f + bW1
def __call__(self, x, mask=None, train=False):
"""Input: ((H, T), B)"""
x = self.ln1(x)
y = self.self_attn(x, x, x, mask, train)
y = dy.dropout(y, self.pdrop) if train else y
x = x + y
x = self.ln2(x)
y = self.ffn(x, train)
y = dy.dropout(y, self.pdrop) if train else y
x = x + y
return x
def build_graph(self, features):
# extract word and tags ids
word_ids = [self.vocab.word2id(word_feat) for word_feat in features[0:20]]
tag_ids = [self.vocab.tag2id(tag_feat) for tag_feat in features[20:40]]
dep_ids = [self.vocab.dep2id(tag_feat) for tag_feat in features[40:]]
# extract word embeddings and tag embeddings from features
word_embeds = [self.word_embedding[wid] for wid in word_ids]
tag_embeds = [self.tag_embedding[tid] for tid in tag_ids]
dep_embeds = [self.dep_embedding[tid] for tid in dep_ids]
# concatenating all features (recall that '+' for lists is equivalent to appending two lists)
embedding_layer = dynet.concatenate(word_embeds + tag_embeds + dep_embeds)
# calculating the hidden layer
# .expr() converts a parameter to a matrix expression in dynet (its a dynet-specific syntax).
hidden1 = self.transfer(self.hidden_layer1 * embedding_layer + self.hidden_layer_bias1)
dropout1 = dynet.dropout(hidden1, 0.1)
hidden2 = self.transfer(self.hidden_layer2 * dropout1 + self.hidden_layer_bias2)
# To implement network without dropout, remove the line with dropout1 and change hidden2 to:
# hidden2 = self.transfer(self.hidden_layer2 * hidden1 + self.hidden_layer_bias2)
# calculating the output layer
output = self.output_layer * hidden2 + self.output_bias
# return the output as a dynet vector (expression)
return output
def __call__(self, x, memory, src_mask, tgt_mask, train=False):
"""Input shape: ((H, T), B)"""
x = self.ln1(x)
y = self.self_attn(x, x, x, tgt_mask, train)
y = dy.dropout(y, self.pdrop) if train else y
x = x + y
x = self.ln2(x)
y = self.src_attn(x, memory, memory, src_mask)
y = dy.dropout(y, self.pdrop) if train else y
x = x + y
x = self.ln3(x)
y = self.ffn(x, train)
y = dy.dropout(y, self.pdrop) if train else y
x = x + y
return x
def calc_score_of_history(words, dropout=0.0):
# Lookup the embeddings and concatenate them
emb = dy.concatenate([W_emb[x] for x in words])
# Create the hidden layer
h = dy.tanh(dy.affine_transform([b_h, W_h, emb]))
# CHANGE 2: perform dropout
if dropout != 0.0:
h = dy.dropout(h, dropout)
# Calculate the score and return
return dy.affine_transform([b_sm, W_sm, h])
def calc_score_of_histories(words, dropout=0.0):
# This will change from a list of histories, to a list of words in each history position
words = np.transpose(words)
# Lookup the embeddings and concatenate them
emb = dy.concatenate([dy.lookup_batch(W_emb, x) for x in words])
# Create the hidden layer
h = dy.tanh(dy.affine_transform([b_h, W_h, emb]))
# Perform dropout
if dropout != 0.0:
h = dy.dropout(h, dropout)
# Calculate the score and return
return dy.affine_transform([b_sm, W_sm, h])
def dot_product_attention(query, key, value, mask=None, dropout=None):
"""Input Shape: ((D, T, H), B)"""
scores = batch_matmul(transpose(key, 0, 1), query)
if mask is not None:
scores = dy.cmult(scores, mask[0]) + (mask[1] * -1e9)
weights = folded_softmax(scores)
if dropout is not None:
weights = dy.dropout(weights, dropout)
return batch_matmul(value, weights)
def __call__(self, x, dropout=False):
if args.conv:
x = dy.reshape(x, (28, 28, 1))
x = dy.conv2d_bias(x, self.F1, self.b1, [1, 1], is_valid=False)
x = dy.rectify(dy.maxpooling2d(x, [2, 2], [2, 2]))
x = dy.conv2d_bias(x, self.F2, self.b2, [1, 1], is_valid=False)
x = dy.rectify(dy.maxpooling2d(x, [2, 2], [2, 2])) # 7x7x64
x = dy.reshape(x, (7 * 7 * 64,))
h = dy.rectify(self.W1 * x + self.hbias)
if dropout:
h = dy.dropout(h, DROPOUT_RATE)
logits = self.W2 * h
return logits
def evaluate(self, inputs, train=False):
"""
Apply all MLP layers to concatenated input
:param inputs: (key, vector) per feature type
:param train: are we training now?
:return: output vector of size self.output_dim
"""
input_keys, inputs = list(map(list, zip(*list(inputs))))
if self.input_keys:
assert input_keys == self.input_keys, "Got: %s\nBut expected input keys: %s" % (
self.input_keys_str(self.input_keys), self.input_keys_str(input_keys))
else:
self.input_keys = input_keys
if self.gated:
gates = self.params.get("gates")
if gates is None: # FIXME attention weights should not be just parameters, but based on biaffine product?
gates = self.params["gates"] = self.model.add_parameters((len(inputs), self.gated),
init=dy.UniformInitializer(1))
input_dims = [i.dim()[0][0] for i in inputs]
max_dim = max(input_dims)
x = dy.concatenate_cols([dy.concatenate([i, dy.zeroes(max_dim - d)]) # Pad with zeros to get uniform dim
if d < max_dim else i for i, d in zip(inputs, input_dims)]) * gates
# Possibly multiple "attention heads" -- concatenate outputs to one vector
inputs = [dy.reshape(x, (x.dim()[0][0] * x.dim()[0][1],))]
x = dy.concatenate(inputs)
assert len(x.dim()[0]) == 1, "Input should be a vector, but has dimension " + str(x.dim()[0])
dim = x.dim()[0][0]
if self.input_dim:
assert dim == self.input_dim, "Input dim mismatch: %d != %d" % (dim, self.input_dim)
else:
self.init_params(dim)
self.config.print(self, level=4)
if self.total_layers:
if self.weights is None:
self.weights = [[self.params[prefix + str(i)] for prefix in ("W", "b")]
for i in range(self.total_layers)]
if self.weights[0][0].dim()[0][1] < dim: # number of columns in W0
self.weights[0][0] = dy.concatenate_cols([self.weights[0][0], self.params["W0+"]])
for i, (W, b) in enumerate(self.weights):
self.config.print(lambda: x.npvalue().tolist(), level=4)
try:
if train and self.dropout:
x = dy.dropout(x, self.dropout)
x = self.activation()(W * x + b)
except ValueError as e:
raise ValueError("Error in evaluating layer %d of %d" % (i + 1, self.total_layers)) from e
self.config.print(lambda: x.npvalue().tolist(), level=4)
return x
def __call__(self, inputs, dropout=False):
x = dy.inputTensor(inputs)
conv1 = dy.parameter(self.pConv1)
b1 = dy.parameter(self.pB1)
x = dy.conv2d_bias(x, conv1, b1, [1, 1], is_valid=False)
x = dy.rectify(dy.maxpooling2d(x, [2, 2], [2, 2]))
conv2 = dy.parameter(self.pConv2)
b2 = dy.parameter(self.pB2)
x = dy.conv2d_bias(x, conv2, b2, [1, 1], is_valid=False)
x = dy.rectify(dy.maxpooling2d(x, [2, 2], [2, 2]))
x = dy.reshape(x, (7*7*64, 1))
w1 = dy.parameter(self.pW1)
b3 = dy.parameter(self.pB3)
h = dy.rectify(w1*x+b3)
if dropout:
h = dy.dropout(h, DROPOUT_RATE)
w2 = dy.parameter(self.pW2)
output = w2*h
# output = dy.softmax(w2*h)
return output
def backward(self, char_seq, truth):
self.renew_cg()
cembs = [ dy.dropout(dy.lookup(self.params['embed'],char),self.options['dropout_rate']) for char in char_seq ]
word_seq,word = [],[]
for char,label in zip(cembs,truth):
word.append(char)
if label > 0:
word_seq.append(word)
word = []
score = self.truth_score(word_seq)
score_plus_margin_loss = self.beam_search(cembs,truth,self.options['margin_loss_discount'])
loss = score_plus_margin_loss - score
res = loss.scalar_value()
loss.backward()
return res
def __call__(self, x, train=False):
"""Input: ((H, T), B) Output: ((H, T), B)."""
x = self.act(self.expand(x))
x = dy.dropout(x, self.pdrop) if train else x
return self.contract(x)
def encode(input_, train):
x = conv(input_)
x = dy.dropout(x, pdrop) if train else x
return x
def dropout(self, input_):
if self.train:
return dy.dropout(input_, self.pdrop)
return input_
