def validate(self, dw, dt, qw, qt, c, a, m_dw, m_qw, tt, tm, m_c, cl,
match_feat, use_char, use_char_q):
f = prepare_input(dw, qw)
if self.subsample != -1: m_dw = sub_sample(m_dw, m_c, self.subsample)
return self.validate_fn(dw, dt, qw, qt, c, a, m_dw.astype('int8'),
m_qw.astype('int8'), tt, tm.astype('int8'),
m_c.astype('int8'), f, cl, match_feat,
use_char, use_char_q)
def train(self, dw, dt, qw, qt, c, a, m_dw, m_qw, tt, tm, m_c, cl):
f = prepare_input(dw, qw)
if self.subsample != -1: m_dw = sub_sample(m_dw, m_c, self.subsample)
return self.train_fn(dw, dt, qw, qt, c, a, m_dw.astype('int8'),
m_qw.astype('int8'), tt, tm.astype('int8'),
m_c.astype('int8'), f, cl)
dv = L.get_output(l_doc, deterministic=True) # B x N x 2D
p = T.batched_dot(dv, q) # B x N
pm = T.nnet.softmax(p) * self.inps[10]
pm = pm / pm.sum(axis=1)[:, np.newaxis]
final_v = T.batched_dot(pm, self.inps[4])
return final, final_v, l_doc, l_qs, l_docembed.W
def load_model(self, load_path):
with open(load_path, 'r') as f:
data = pickle.load(f)
L.set_all_param_values([self.doc_net] + self.q_net, data)
def save_model(self, save_path):
data = L.get_all_param_values([self.doc_net] + self.q_net)
with open(save_path, 'w') as f:
pickle.dump(data, f)
if __name__ == "__main__":
m_d = np.asarray([[1, 1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1,
0]]).astype('int32')
m_c = np.asarray([[1, 1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 1, 0, 0, 1,
0]]).astype('int32')
print 'doc mask', m_d
print 'cand mask', m_c
print 'new mask (N=1)', sub_sample(m_d, m_c, 1)
print 'new mask (N=2)', sub_sample(m_d, m_c, 2)
print 'new mask (N=3)', sub_sample(m_d, m_c, 3)
def validate(self, d, q, c, a, m_d, m_q, m_c):
if self.subsample != -1: m_d = sub_sample(m_d, m_c, self.subsample)
return self.validate_fn(d, q, c, a, m_d.astype('int8'),
m_q.astype('int8'), m_c.astype('int8'))
def train(self, d, q, c, a, m_d, m_q, m_c):
if self.subsample != -1: m_d = sub_sample(m_d, m_c, self.subsample)
return self.train_fn(d, q, c, a, m_d.astype('int8'),
m_q.astype('int8'), m_c.astype('int8'))
