From 02dfc926084dd04cb19555957fd0b9f079af6da0 Mon Sep 17 00:00:00 2001 From: codekansas Date: Fri, 22 Jul 2016 23:57:32 -0700 Subject: [PATCH] randomization --- seq2seq/answer_to_question.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/seq2seq/answer_to_question.py b/seq2seq/answer_to_question.py index 981fb89..d67fb15 100644 --- a/seq2seq/answer_to_question.py +++ b/seq2seq/answer_to_question.py @@ -6,6 +6,7 @@ and generalizes to other questions and answers. from __future__ import print_function import os +import random import sys import numpy as np @@ -70,7 +71,6 @@ class InsuranceQA: def get_model(question_maxlen, answer_maxlen, vocab_len, n_hidden, load_save=False): answer = Input(shape=(answer_maxlen,), dtype='int32') embedded = Embedding(input_dim=vocab_len, output_dim=n_hidden, mask_zero=True)(answer) - # masked = Masking(mask_value=0.)(answer) # encoder rnn encode_rnn = GRU(n_hidden, return_sequences=True, dropout_U=0.2)(embedded) @@ -132,7 +132,7 @@ if __name__ == '__main__': i = 0 question_idx = np.zeros(shape=(batch_size, question_maxlen, len(qa.vocab))) answer_idx = np.zeros(shape=(batch_size, answer_maxlen)) - for s in questions: + for s in random.shuffle(questions): if test: ans = s['good'] else: @@ -154,7 +154,7 @@ if __name__ == '__main__': print('Generating model...') model = get_model(question_maxlen=question_maxlen, answer_maxlen=answer_maxlen, vocab_len=len(qa.vocab), - n_hidden=256, load_save=False) + n_hidden=256, load_save=True) print('Training model...') for iteration in range(1, nb_iteration + 1):