save_model("full_test.model", model)
# p_sents_y, p_sents_t = model.predict(test_data_x_y[0][0], test_data_x_y[0][1],test_data_x_y[0][4], test_batch)
# print ".........................predict....................."
# get_data.print_sentence(p_sents_y, dim_y, i2w)
# get_data.print_sentence(p_sents_t, dim_y, i2t)
# print ".....................predict done...................."
# ^ predict example ^
t_bleu = []
for tlen in xrange(len(test_data_x_y)):
p_sents_y, p_sents_t = model.predict(test_data_x_y[tlen][0], test_data_x_y[tlen][1],test_data_x_y[tlen][4], test_batch)
candidate_dic = get_data.get_candidate_dic_for_test_pos(sents_y, dim_y, i2w)
batch_bleu, _ = print_bleu_normal_batch(candidate_dic, reference_dic, tlen * test_batch)
t_bleu.append(batch_bleu)
print "~~~~~~~~~~~~~Test Bleu is ", float(sum(t_bleu))/len(t_bleu), "~~~~~~~~~~~~~~~~"
get_data.print_sentence_last_n(p_sents_y, dim_y, i2w, 1)
get_data.print_sentence_last_n(p_sents_t, dim_tag, i2t, 1)
print "read batch train last :"
get_data.print_sentence_last_n(sents_y, dim_y, i2w, 1)
get_data.print_sentence_last_n(sents_t, dim_tag, i2t, 1)
E = float(error)/batch_split
# if E < g_error:
# g_error = E
# print 'new smaller cost, save param...'
# save_model("0405-GRU-all-4hidden1000_best.model", model)
# if error < 3.0:
# l,r = model.predict(data_t1[0][0], data_t1[0][1],data_t1[0][3], data_t1[0][5], 1)
# l2,r2 = model.predict(data_t2[0][0], data_t2[0][1],data_t2[0][3], data_t2[0][5], 1)
# l3,r3 = model.predict(data_4[0][0], data_4[0][1],data_4[0][3], data_4[0][5], 1)
# t_sents = model.predict(test_data_x_y[0][0], test_data_x_y[0][1],test_data_x_y[0][3], batch_size)
#打印结果
# print "Test : "
# get_data.print_sentence(l, dim_y, i2w)
# get_data.print_sentence(l2, dim_y, i2w)
# get_data.print_sentence_last_n(t_sents[0], dim_y, i2w, 5)
error /= len(data_x_y);
print "Iter = " + str(i)+ " Error = " + str(error) + ", Time = " + str(in_b_time)
if error < g_error:
g_error = error
print 'new smaller cost, save param...'
save_model("GRU-200_best.model", model)
if error < 2.0:
print "train_last :"
get_data.print_sentence_last_n(sents, dim_y, i2w, 20)
if error <= e:
break
# print "Finished. Time = " + str(time.time() - start)
# print "save model..."
# save_model("GRU-200_best-final.model", model)