def _gen(self, node):
# input word embedding
wv_t = sigmoid(self.Wemb_np[node.wordid, :])
# attention
b_t = np.zeros((node.sv.shape[0]))
for j in range(node.sv.shape[0]):
b_t[j] = np.dot(
tanh(
np.dot(np.concatenate([wv_t, node.h, node.sv[j]], axis=0),
self.Wha_np)), self.Vha_np)
b_t = softmax(b_t)
sv_emb_t = np.dot(b_t, node.sv)
da_emb_t = tanh(node.a + sv_emb_t)
# compute ig, fg, og together and slice it
gates_t = np.dot(np.concatenate([wv_t, node.h, da_emb_t], axis=0),
self.Wgate_np)
ig = sigmoid(gates_t[:self.dh])
fg = sigmoid(gates_t[self.dh:self.dh * 2])
og = sigmoid(gates_t[self.dh * 2:self.dh * 3])
cx_t = tanh(gates_t[self.dh * 3:])
# update lstm internal state
c_t = np.multiply(ig, cx_t) + np.multiply(fg, node.c)
# obtain new hiddne layer
h_t = np.multiply(og, tanh(c_t))
# compute output distribution target word prob
o_t = softmax(np.dot(h_t, self.Who_np))
# make sure we won't sample unknown word
o_t[0] = 0.0
selected_words = np.argsort(o_t)[::-1][:self.beamwidth].tolist()
# return results
return selected_words, o_t[selected_words], c_t, h_t
def _sample_from_posterior(self, belief_t, degree_t, intent_t,
masked_source_t, masked_target_t):
# Posterior
# response encoding
target_intent_t = bidirectional_read(self.tfEncoder, self.tbEncoder,
masked_target_t)
source_intent_t = bidirectional_read(self.sfEncoder, self.sbEncoder,
masked_source_t)
# posterior parameterisation
q_logit_t = np.dot(
tanh(
np.dot(belief_t, self.Wq1_backup) +
np.dot(degree_t, self.Wq2_backup) +
np.dot(source_intent_t, self.Wq3_backup) +
np.dot(target_intent_t, self.Wq4_backup)), self.Wq5_backup)
# sampling from a scaled posterior
sortedIndex = np.argsort(q_logit_t)[::-1][:self.topN]
topN_posterior_t = softmax(q_logit_t[sortedIndex])
z_t = sortedIndex[np.argmax(
np.random.multinomial(n=1, pvals=topN_posterior_t))]
#z_t = sortedIndex[0]
z_t = np.expand_dims(z_t, axis=0)
print sortedIndex[:3]
print softmax(q_logit_t)[sortedIndex][:3]
print 'Posterior : %s' % sortedIndex
print 'probability: %s' % topN_posterior_t
return z_t, softmax(q_logit_t)
def _gen(self,node):
# input word embedding
wv_t = sigmoid(self.Wemb_np[node.wordid,:])
# attention
b_t = np.zeros((node.sv.shape[0]))
for j in range(node.sv.shape[0]):
b_t[j] = np.dot(tanh(np.dot(
np.concatenate([wv_t,node.h,node.sv[j]],axis=0),
self.Wha_np)),self.Vha_np)
b_t = softmax(b_t)
sv_emb_t = np.dot(b_t,node.sv)
da_emb_t = tanh( node.a+sv_emb_t )
# compute ig, fg, og together and slice it
gates_t = np.dot( np.concatenate([wv_t,node.h,da_emb_t],axis=0),
self.Wgate_np)
ig = sigmoid(gates_t[:self.dh])
fg = sigmoid(gates_t[self.dh:self.dh*2])
og = sigmoid(gates_t[self.dh*2:self.dh*3])
cx_t= tanh( gates_t[self.dh*3:] )
# update lstm internal state
c_t = np.multiply(ig,cx_t) + np.multiply(fg,node.c)
# obtain new hiddne layer
h_t = np.multiply(og,tanh(c_t))
# compute output distribution target word prob
o_t = softmax( np.dot(h_t,self.Who_np) )
# make sure we won't sample unknown word
o_t[0] = 0.0
selected_words = np.argsort(o_t)[::-1][:self.beamwidth].tolist()
# return results
return selected_words, o_t[selected_words], c_t, h_t
def _sample_from_posterior(self, belief_t, degree_t, intent_t,
masked_source_t, masked_target_t):
# Posterior
# response encoding
target_intent_t = bidirectional_read(
self.tfEncoder, self.tbEncoder, masked_target_t)
source_intent_t = bidirectional_read(
self.sfEncoder, self.sbEncoder, masked_source_t)
# posterior parameterisation
q_logit_t = np.dot(tanh(
np.dot(belief_t,self.Wq1_backup)+
np.dot(degree_t,self.Wq2_backup)+
np.dot(source_intent_t,self.Wq3_backup)+
np.dot(target_intent_t,self.Wq4_backup)),
self.Wq5_backup )
# sampling from a scaled posterior
sortedIndex = np.argsort(q_logit_t)[::-1][:self.topN]
topN_posterior_t= softmax(q_logit_t[sortedIndex])
z_t = sortedIndex[ np.argmax( np.random.multinomial(n=1,
pvals=topN_posterior_t)) ]
#z_t = sortedIndex[0]
z_t = np.expand_dims(z_t,axis=0)
print sortedIndex[:3]
print softmax(q_logit_t)[sortedIndex][:3]
print 'Posterior : %s' % sortedIndex
print 'probability: %s' % topN_posterior_t
return z_t, softmax(q_logit_t)
def track(self, b_jm1, ngs_j, ngt_j):
# padding dummy
Wfbs = np.concatenate([self.Wfbs_backup,\
np.zeros_like(self.Wfbs_backup[-1:,:])],axis=0)
Wfbt = np.concatenate([self.Wfbt_backup,\
np.zeros_like(self.Wfbt_backup[-1:,:])],axis=0)
# new belief
g_j = np.zeros(self.dbm1)
for v in range(self.dbm1):
ngsidx = ngs_j[v]
ngtidx = ngt_j[v]
fembs_v = np.sum(Wfbs[ngsidx,:],axis=0)
fembt_v = np.sum(Wfbt[ngtidx,:],axis=0)
g_jv = np.dot( self.Whb_backup, sigmoid(
fembs_v + fembt_v +
b_jm1[v] *self.Wrec_backup +
b_jm1[-1]*self.Wnon_backup +
self.B0_backup ))
g_j[v] = g_jv
g_j = np.concatenate([g_j,self.B_backup],axis=0)
b_j = softmax( g_j )
return b_j
def track(self, ms_j, mt_jm1,
ssrcpos_js, vsrcpos_js, starpos_js, vtarpos_js ):
# cnn encoding
ngms_j, uttms_j = self.sCNN.read(ms_j)
ngmt_jm1,uttmt_jm1 = self.tCNN.read(mt_jm1)
# padding dummy vector
ngms_j = np.concatenate([ngms_j,np.zeros_like(ngms_j[-1:,:])],axis=0)
ngmt_jm1 = np.concatenate([ngmt_jm1,np.zeros_like(ngmt_jm1[-1:,:])],axis=0)
# source features
ssrcemb_js = np.sum(ngms_j[ssrcpos_js,:],axis=0)
vsrcemb_js = np.sum(ngms_j[vsrcpos_js,:],axis=0)
src_js = np.concatenate([ssrcemb_js,vsrcemb_js,uttms_j],axis=0)
# target features
staremb_js = np.sum(ngmt_jm1[starpos_js,:],axis=0)
vtaremb_js = np.sum(ngmt_jm1[vtarpos_js,:],axis=0)
tar_js = np.concatenate([staremb_js,vtaremb_js,uttmt_jm1],axis=0)
# update g_j
g_j = np.dot( self.Whb_backup, sigmoid(
np.dot(src_js,self.Wfbs_backup) +
np.dot(tar_js,self.Wfbt_backup) +
self.B0_backup ))
# update b_j
g_j = np.array([g_j,self.B_backup])
b_j = softmax( g_j )
return b_j
def _gen(self, node):
# input word embedding
wv_t = sigmoid(self.Wemb_np[node.wordid, :])
# compute ig, fg, og together and slice it
gates_t = np.dot(np.concatenate([wv_t, node.h, node.sv], axis=0),
self.Wgate_np)
ig = sigmoid(gates_t[:self.dh])
fg = sigmoid(gates_t[self.dh:self.dh * 2])
og = sigmoid(gates_t[self.dh * 2:self.dh * 3])
# compute reading rg
rg = sigmoid(
np.dot(np.concatenate([wv_t, node.h, node.sv], axis=0),
self.Wrgate_np))
# compute proposed cell value
cx_t = np.tanh(
np.dot(np.concatenate([wv_t, node.h], axis=0), self.Wcx_np))
# update DA 1-hot vector
sv_t = np.multiply(rg, node.sv)
# update lstm internal state
c_t = np.multiply(ig, cx_t) + \
np.multiply(fg, node.c) + \
tanh(np.dot(np.concatenate([node.a, sv_t], axis=0), self.Wfc_np))
# obtain new hiddne layer
h_t = np.multiply(og, tanh(c_t))
# compute output distribution target word prob
o_t = softmax(np.dot(h_t, self.Who_np))
# make sure we won't sample unknown word
o_t[0] = 0.0
selected_words = np.argsort(o_t)[::-1][:self.beamwidth].tolist()
# return results
return selected_words, o_t[selected_words], sv_t, c_t, h_t
def track(self, b_jm1, ngs_j, ngt_j):
# padding dummy
Wfbs = np.concatenate([self.Wfbs_backup,\
np.zeros_like(self.Wfbs_backup[-1:,:])],axis=0)
Wfbt = np.concatenate([self.Wfbt_backup,\
np.zeros_like(self.Wfbt_backup[-1:,:])],axis=0)
# new belief
g_j = np.zeros(self.dbm1)
for v in range(self.dbm1):
ngsidx = ngs_j[v]
ngtidx = ngt_j[v]
fembs_v = np.sum(Wfbs[ngsidx, :], axis=0)
fembt_v = np.sum(Wfbt[ngtidx, :], axis=0)
g_jv = np.dot(
self.Whb_backup,
sigmoid(fembs_v + fembt_v + b_jm1[v] * self.Wrec_backup +
b_jm1[-1] * self.Wnon_backup + self.B0_backup))
g_j[v] = g_jv
g_j = np.concatenate([g_j, self.B_backup], axis=0)
b_j = softmax(g_j)
return b_j
def track(self, ms_j, mt_jm1, ssrcpos_js, vsrcpos_js, starpos_js,
vtarpos_js):
# cnn encoding
ngms_j, uttms_j = self.sCNN.read(ms_j)
ngmt_jm1, uttmt_jm1 = self.tCNN.read(mt_jm1)
# padding dummy vector
ngms_j = np.concatenate([ngms_j, np.zeros_like(ngms_j[-1:, :])],
axis=0)
ngmt_jm1 = np.concatenate(
[ngmt_jm1, np.zeros_like(ngmt_jm1[-1:, :])], axis=0)
# source features
ssrcemb_js = np.sum(ngms_j[ssrcpos_js, :], axis=0)
vsrcemb_js = np.sum(ngms_j[vsrcpos_js, :], axis=0)
src_js = np.concatenate([ssrcemb_js, vsrcemb_js, uttms_j], axis=0)
# target features
staremb_js = np.sum(ngmt_jm1[starpos_js, :], axis=0)
vtaremb_js = np.sum(ngmt_jm1[vtarpos_js, :], axis=0)
tar_js = np.concatenate([staremb_js, vtaremb_js, uttmt_jm1], axis=0)
# update g_j
g_j = np.dot(
self.Whb_backup,
sigmoid(
np.dot(src_js, self.Wfbs_backup) +
np.dot(tar_js, self.Wfbt_backup) + self.B0_backup))
# update b_j
g_j = np.array([g_j, self.B_backup])
b_j = softmax(g_j)
return b_j
def _gen(self,node):
# input word embedding
wv_t = sigmoid(self.Wemb_np[node.wordid,:])
# compute ig, fg, og together and slice it
gates_t = np.dot( np.concatenate(
[wv_t,node.h,node.sv],axis=0),self.Wgate_np)
ig = sigmoid(gates_t[:self.dh])
fg = sigmoid(gates_t[self.dh:self.dh*2])
og = sigmoid(gates_t[self.dh*2:self.dh*3])
# compute reading rg
rg = sigmoid(np.dot(np.concatenate(
[wv_t,node.h,node.sv],axis=0),self.Wrgate_np))
# compute proposed cell value
cx_t= np.tanh(np.dot(np.concatenate(
[wv_t,node.h],axis=0),self.Wcx_np))
# update DA 1-hot vector
sv_t = np.multiply(rg,node.sv)
# update lstm internal state
c_t = np.multiply(ig,cx_t) +\
np.multiply(fg,node.c)+\
tanh(np.dot(np.concatenate([node.a,sv_t],axis=0),self.Wfc_np))
# obtain new hiddne layer
h_t = np.multiply(og,tanh(c_t))
# compute output distribution target word prob
o_t = softmax( np.dot(h_t,self.Who_np) )
# make sure we won't sample unknown word
o_t[0] = 0.0
selected_words = np.argsort(o_t)[::-1][:self.beamwidth].tolist()
# return results
return selected_words, o_t[selected_words], sv_t, c_t, h_t
def _sample_from_prior(self, belief_t, degree_t, intent_t):
# prior parameterisarion
hidden_t = tanh(np.dot(belief_t,self.Ws1_backup)+
np.dot(degree_t,self.Ws2_backup)+
np.dot(intent_t,self.Ws3_backup) )
p_logit_t = np.dot(
tanh(np.dot(hidden_t,self.Wp1_backup)+self.bp1_backup),
self.Wp2_backup)
# sampling from prior
sortedIndex = np.argsort(p_logit_t)[::-1][:self.topN]
topN_prior_t= softmax(p_logit_t[sortedIndex])
z_t = sortedIndex[ np.argmax( np.random.multinomial(n=1,
pvals=topN_prior_t)) ]
z_t = np.expand_dims(z_t,axis=0)
# choose the top N samples
print 'Sample : %s' % z_t
print 'Prior dist.: %s' % sortedIndex
print 'probability: %s' % topN_prior_t
print
return z_t, softmax(p_logit_t)
def _sample_from_prior(self, belief_t, degree_t, intent_t):
# prior parameterisarion
hidden_t = tanh(np.dot(belief_t, self.Ws1_backup) +
np.dot(degree_t, self.Ws2_backup) +
np.dot(intent_t, self.Ws3_backup))
p_logit_t = np.dot(
tanh(np.dot(hidden_t, self.Wp1_backup) + self.bp1_backup),
self.Wp2_backup)
# sampling from prior
sortedIndex = np.argsort(p_logit_t)[::-1][:self.topN]
topN_prior_t = softmax(p_logit_t[sortedIndex])
z_t = sortedIndex[np.argmax(np.random.multinomial(n=1,
pvals=topN_prior_t))]
z_t = np.expand_dims(z_t, axis=0)
# choose the top N samples
print 'Sample : %s' % z_t
print 'Prior dist.: %s' % sortedIndex
print 'probability: %s' % topN_prior_t
print
return z_t, softmax(p_logit_t)
def decide(self, belief_t, degree_t, intent_t, ohidden_tjm1, wemb_tj):
# embed
degree_t = tanh(np.dot(degree_t, self.Ws2_backup))
intent_t = tanh(np.dot(intent_t, self.Ws3_backup))
# score bias
score_t=np.dot(ohidden_tjm1,self.Wa1_backup)+\
np.dot(wemb_tj, self.Wa2_backup)+\
np.dot(belief_t,self.Wa3_backup)
# attention mechanism
atten_t = softmax(np.dot(sigmoid(score_t), self.Va1_backup))
actEmb = tanh(np.dot(atten_t, belief_t) + degree_t + intent_t)
return np.expand_dims(actEmb, axis=0)
def decide(self, belief_t, degree_t, intent_t, ohidden_tjm1, wemb_tj):
# embed
degree_t = tanh(np.dot(degree_t,self.Ws2_backup))
intent_t = tanh(np.dot(intent_t,self.Ws3_backup))
# score bias
score_t=np.dot(ohidden_tjm1,self.Wa1_backup)+\
np.dot(wemb_tj, self.Wa2_backup)+\
np.dot(belief_t,self.Wa3_backup)
# attention mechanism
atten_t= softmax(np.dot(sigmoid(score_t),self.Va1_backup))
actEmb = tanh(np.dot(atten_t,belief_t)+degree_t+intent_t)
return np.expand_dims(actEmb,axis=0)
def track(self, ngs_j, ngt_j):
# padding dummy
Wfbs = np.concatenate([self.Wfbs_backup, \
np.zeros_like(self.Wfbs_backup[-1:, :])], axis=0)
Wfbt = np.concatenate([self.Wfbt_backup, \
np.zeros_like(self.Wfbt_backup[-1:, :])], axis=0)
# new belief
fembs_v = np.sum(Wfbs[ngs_j, :], axis=0)
fembt_v = np.sum(Wfbt[ngt_j, :], axis=0)
g_j = np.dot(self.Whb_backup,
sigmoid(fembs_v + fembt_v + self.B0_backup))
g_j = np.array([g_j, self.B_backup])
b_j = softmax(g_j)
return b_j
def track(self, ngs_j, ngt_j):
# padding dummy
Wfbs = np.concatenate([self.Wfbs_backup,\
np.zeros_like(self.Wfbs_backup[-1:,:])],axis=0)
Wfbt = np.concatenate([self.Wfbt_backup,\
np.zeros_like(self.Wfbt_backup[-1:,:])],axis=0)
# new belief
fembs_v = np.sum(Wfbs[ngs_j,:],axis=0)
fembt_v = np.sum(Wfbt[ngt_j,:],axis=0)
g_j = np.dot( self.Whb_backup, sigmoid(
fembs_v + fembt_v + self.B0_backup ))
g_j = np.array([g_j,self.B_backup])
b_j = softmax( g_j )
return b_j
def track(self, b_jm1, ms_j, mt_jm1, ssrcpos_js, vsrcpos_js, starpos_js,
vtarpos_js):
# cnn encoding
ngms_j, uttms_j = self.sCNN.read(ms_j)
ngmt_jm1, uttmt_jm1 = self.tCNN.read(mt_jm1)
# padding dummy vector
ngms_j = np.concatenate([ngms_j, np.zeros_like(ngms_j[-1:, :])],
axis=0)
ngmt_jm1 = np.concatenate(
[ngmt_jm1, np.zeros_like(ngmt_jm1[-1:, :])], axis=0)
# new belief
g_j = np.zeros(self.dbm1)
for v in range(self.dbm1):
# source features
ssrcemb_jsv = np.sum(ngms_j[ssrcpos_js[v], :], axis=0)
vsrcemb_jsv = np.sum(ngms_j[vsrcpos_js[v], :], axis=0)
src_jsv = np.concatenate([ssrcemb_jsv, vsrcemb_jsv, uttms_j],
axis=0)
# target features
staremb_jsv = np.sum(ngmt_jm1[starpos_js[v], :], axis=0)
vtaremb_jsv = np.sum(ngmt_jm1[vtarpos_js[v], :], axis=0)
tar_jsv = np.concatenate([staremb_jsv, vtaremb_jsv, uttmt_jm1],
axis=0)
# update g_jv value
g_jv = np.dot(
self.Whb_backup,
sigmoid(
np.dot(src_jsv, self.Wfbs_backup) +
np.dot(tar_jsv, self.Wfbt_backup) +
b_jm1[v] * self.Wrec_backup +
b_jm1[-1] * self.Wnon_backup + self.B0_backup))
g_j[v] = g_jv
# produce new belief b_j
g_j = np.concatenate([g_j, self.B_backup], axis=0)
b_j = softmax(g_j)
return b_j
def track(self, b_jm1, ms_j, mt_jm1,
ssrcpos_js, vsrcpos_js, starpos_js, vtarpos_js ):
# cnn encoding
ngms_j, uttms_j = self.sCNN.read(ms_j)
ngmt_jm1,uttmt_jm1 = self.tCNN.read(mt_jm1)
# padding dummy vector
ngms_j = np.concatenate([ngms_j,np.zeros_like(ngms_j[-1:,:])],axis=0)
ngmt_jm1 = np.concatenate([ngmt_jm1,np.zeros_like(ngmt_jm1[-1:,:])],axis=0)
# new belief
g_j = np.zeros(self.dbm1)
for v in range(self.dbm1):
# source features
ssrcemb_jsv = np.sum(ngms_j[ssrcpos_js[v],:],axis=0)
vsrcemb_jsv = np.sum(ngms_j[vsrcpos_js[v],:],axis=0)
src_jsv = np.concatenate([ssrcemb_jsv,vsrcemb_jsv,uttms_j],axis=0)
# target features
staremb_jsv = np.sum(ngmt_jm1[starpos_js[v],:],axis=0)
vtaremb_jsv = np.sum(ngmt_jm1[vtarpos_js[v],:],axis=0)
tar_jsv = np.concatenate([staremb_jsv,vtaremb_jsv,uttmt_jm1],axis=0)
# update g_jv value
g_jv = np.dot( self.Whb_backup, sigmoid(
np.dot(src_jsv,self.Wfbs_backup) +
np.dot(tar_jsv,self.Wfbt_backup) +
b_jm1[v] *self.Wrec_backup +
b_jm1[-1]*self.Wnon_backup +
self.B0_backup ))
g_j[v] = g_jv
# produce new belief b_j
g_j = np.concatenate([g_j,self.B_backup],axis=0)
b_j = softmax( g_j )
return b_j
def _forwardpass(self, n, intent_t, belief_vec_t, degree_t, actEmb_t,
scoreTable):
# forward pass
in_j = sigmoid(self.Wemb_backup[n.wordid])
# action embedding
if self.ply == 'attention':
actEmb_tj = self.policy.decide(belief_vec_t, degree_t, intent_t,
n.h, in_j)[0]
else: # fixed action embedding
actEmb_tj = actEmb_t
# syntatic memory cell and gate
# compute i, f, o, c together and slice it
bundle_j = np.dot(in_j, self.oWgate_backup) + \
np.dot(n.h, self.oUgate_backup)
bundle_aj = np.dot(actEmb_tj, self.Wzh_backup)
# input gate
ig = sigmoid(bundle_j[:self.doh] + bundle_aj[:self.doh] +
self.b_backup[:self.doh])
# use forget bias or not
fg = sigmoid(bundle_j[self.doh:self.doh * 2] +
bundle_aj[self.doh:self.doh * 2] +
self.b_backup[self.doh:self.doh * 2])
# output gate
og = sigmoid(bundle_j[self.doh * 2:self.doh * 3] +
bundle_aj[self.doh * 2:self.doh * 3] +
self.b_backup[self.doh * 2:self.doh * 3])
# proposed memory cell
# reading gate, memory cell, hidden layer
if self.struct == 'lstm_cond': # reading gate control signal
rg = sigmoid(bundle_j[self.doh * 4:self.doh * 5] +
bundle_aj[self.doh * 4:self.doh * 5] +
self.b_backup[self.doh * 3:])
cx_j = tanh(bundle_j[self.doh * 3:self.doh * 4])
oc_j = np.multiply(ig, cx_j) + \
np.multiply(fg, n.c) + \
np.multiply(rg, tanh(bundle_aj[self.doh * 3:self.doh * 4]))
oh_j = np.multiply(og, tanh(oc_j))
o_j = softmax(np.dot(oh_j, self.Who_backup))
elif self.struct == 'lstm_mix': # two signals
rg = sigmoid(bundle_j[self.doh * 4:self.doh * 5] +
bundle_aj[self.doh * 4:self.doh * 5] +
self.b_backup[self.doh * 3:])
cx_j = tanh(bundle_j[self.doh * 3:self.doh * 4])
oc_j = np.multiply(ig, cx_j) + \
np.multiply(fg, n.c)
oh_j = np.multiply(og, tanh(oc_j)) + \
np.multiply(rg, tanh(bundle_aj[self.doh * 3:self.doh * 4]))
o_j = softmax(np.dot(oh_j, self.Who_backup))
elif self.struct == 'lstm_lm': # lm style
cx_j = tanh(bundle_j[self.doh * 3:self.doh * 4] +
bundle_aj[self.doh * 3:self.doh * 4])
oc_j = np.multiply(ig, cx_j) + \
np.multiply(fg, n.c)
oh_j = np.multiply(og, tanh(oc_j))
o_j = softmax(np.dot(oh_j, self.Who_backup))
else:
sys.exit('[ERROR]: Unseen decoder structure ' + self.struct)
# compute output distribution, logp, and sample
# make sure we won't sample unknown word
o_j[0] = 0.0
selected_words = np.argsort(o_j)[::-1][:self.beamwidth]
# expand nodes and add additional reward
nextnodes = []
for wid in selected_words: # ignore <unk> token
# loglikelihood of current word
logp = np.log10(o_j[wid])
# update record for new node
new_record = deepcopy(n.record)
if new_record['s'].has_key(wid):
new_record['s'][wid] += 1
if new_record['v'].has_key(wid):
new_record['v'][wid] += 1
# create new node and score it
node = BeamSearchNode(oh_j, oc_j, n, wid, \
n.logp + logp, n.leng + 1, new_record)
# store nodes
nextnodes.append( \
(-node.eval(self.repeat_penalty, self.token_reward, \
scoreTable, self.alpha), node))
return nextnodes
def _forwardpass(self, n, intent_t, belief_vec_t, degree_t, actEmb_t,
scoreTable):
# forward pass
in_j = sigmoid( self.Wemb_backup[n.wordid] )
# action embedding
if self.ply=='attention':
actEmb_tj = self.policy.decide(belief_vec_t,
degree_t, intent_t, n.h, in_j)[0]
else: # fixed action embedding
actEmb_tj = actEmb_t
# syntatic memory cell and gate
# compute i, f, o, c together and slice it
bundle_j = np.dot(in_j,self.oWgate_backup) +\
np.dot(n.h, self.oUgate_backup)
bundle_aj= np.dot(actEmb_tj,self.Wzh_backup)
# input gate
ig = sigmoid( bundle_j[:self.doh]+
bundle_aj[:self.doh]+
self.b_backup[:self.doh])
# use forget bias or not
fg = sigmoid( bundle_j[self.doh:self.doh*2]+
bundle_aj[self.doh:self.doh*2]+
self.b_backup[self.doh:self.doh*2])
# output gate
og = sigmoid( bundle_j[self.doh*2:self.doh*3]+
bundle_aj[self.doh*2:self.doh*3]+
self.b_backup[self.doh*2:self.doh*3])
# proposed memory cell
# reading gate, memory cell, hidden layer
if self.struct=='lstm_cond': # reading gate control signal
rg = sigmoid( bundle_j[self.doh*4:self.doh*5]+
bundle_aj[self.doh*4:self.doh*5]+
self.b_backup[self.doh*3:])
cx_j = tanh(bundle_j[self.doh*3:self.doh*4])
oc_j = np.multiply(ig,cx_j)+\
np.multiply(fg,n.c)+\
np.multiply(rg,tanh(bundle_aj[self.doh*3:self.doh*4]))
oh_j = np.multiply(og,tanh(oc_j))
o_j = softmax( np.dot(oh_j, self.Who_backup) )
elif self.struct=='lstm_mix':# two signals
rg = sigmoid( bundle_j[self.doh*4:self.doh*5]+
bundle_aj[self.doh*4:self.doh*5]+
self.b_backup[self.doh*3:])
cx_j = tanh(bundle_j[self.doh*3:self.doh*4])
oc_j = np.multiply(ig,cx_j)+\
np.multiply(fg,n.c)
oh_j = np.multiply(og,tanh(oc_j))+\
np.multiply(rg,tanh(bundle_aj[self.doh*3:self.doh*4]))
o_j = softmax( np.dot(oh_j, self.Who_backup) )
elif self.struct=='lstm_lm': # lm style
cx_j = tanh( bundle_j[self.doh*3:self.doh*4]+
bundle_aj[self.doh*3:self.doh*4])
oc_j = np.multiply(ig,cx_j)+\
np.multiply(fg,n.c)
oh_j = np.multiply(og,tanh(oc_j))
o_j = softmax( np.dot(oh_j, self.Who_backup) )
else:
sys.exit('[ERROR]: Unseen decoder structure '+self.struct)
# compute output distribution, logp, and sample
# make sure we won't sample unknown word
o_j[0] = 0.0
selected_words = np.argsort(o_j)[::-1][:self.beamwidth]
# expand nodes and add additional reward
nextnodes = []
for wid in selected_words: # ignore <unk> token
# loglikelihood of current word
logp = np.log10(o_j[wid])
# update record for new node
new_record = deepcopy(n.record)
if new_record['s'].has_key(wid):
new_record['s'][wid] += 1
if new_record['v'].has_key(wid):
new_record['v'][wid] += 1
# create new node and score it
node = BeamSearchNode(oh_j,oc_j,n,wid,\
n.logp+logp,n.leng+1,new_record)
# store nodes
nextnodes.append( \
(-node.eval(self.repeat_penalty,self.token_reward,\
scoreTable,self.alpha), node))
return nextnodes
