def validate(self, sess, x_val):
# Calculate BLEU on validation data
hypotheses_val = []
references_val = []
for batch_i, (input_batch, output_batch, sent_lengths) in enumerate(
utils.get_batches(x_val, self.batch_size)):
pred_sentences, self._validate_logits = sess.run(
[self.validate_sent, self.validate_logits],
feed_dict={self.input_data: input_batch,
self.source_sentence_length: sent_lengths,
self.keep_prob: 1.0,
})
for pred, actual in zip(pred_sentences, output_batch):
hypotheses_val.append(
word_tokenize(
" ".join([self.idx_word[i] for i in pred if i not in [self.pad, -1, self.eos]])))
references_val.append(
[word_tokenize(" ".join([self.idx_word[i] for i in actual if i not in [self.pad, -1, self.eos]]))])
self.val_pred = ([" ".join(sent) for sent in hypotheses_val])
self.val_ref = ([" ".join(sent[0]) for sent in references_val])
bleu_scores = utils.calculate_bleu_scores(references_val, hypotheses_val)
self.epoch_bleu_score_val['1'].append(bleu_scores[0])
self.epoch_bleu_score_val['2'].append(bleu_scores[1])
self.epoch_bleu_score_val['3'].append(bleu_scores[2])
self.epoch_bleu_score_val['4'].append(bleu_scores[3])
def predict(self, checkpoint, x_test):
pred_logits = []
hypotheses_test = []
references_test = []
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, checkpoint)
for batch_i, (input_batch, output_batch, sent_lengths) in enumerate(
utils.get_batches(x_test, self.batch_size)):
result = sess.run(self.validate_sent, feed_dict={self.input_data: input_batch,
self.source_sentence_length: sent_lengths,
self.keep_prob: 1.0,
})
pred_logits.extend(result)
for pred, actual in zip(result, output_batch):
hypotheses_test.append(
word_tokenize(" ".join(
[self.idx_word[i] for i in pred if i not in [self.pad, -1, self.eos]])))
references_test.append([word_tokenize(
" ".join([self.idx_word[i] for i in actual if i not in [self.pad, -1, self.eos]]))])
bleu_scores = utils.calculate_bleu_scores(references_test, hypotheses_test)
print('BLEU 1 to 4 : {}'.format(' | '.join(map(str, bleu_scores))))
return pred_logits
def validate(self, sess, x_val, y_val, true_val):
# Calculate BLEU on validation data
hypotheses_val = []
references_val = []
for batch_i, (input_batch, output_batch, source_sent_lengths, tar_sent_lengths) in enumerate(
utils.get_batches_xy(x_val, y_val, self.batch_size)):
answer_logits = sess.run(self.inference_logits,
feed_dict={self.input_data: input_batch,
self.source_sentence_length: source_sent_lengths,
self.keep_prob: 1.0,
self.word_dropout_keep_prob: 1.0,
self.z_temperature: self.z_temp})
for k, pred in enumerate(answer_logits):
hypotheses_val.append(
word_tokenize(
" ".join([self.decoder_idx_word[i] for i in pred if i not in [self.pad, -1, self.eos]])))
references_val.append([word_tokenize(true_val[batch_i * self.batch_size + k])])
self.val_pred = ([" ".join(sent) for sent in hypotheses_val])
self.val_ref = ([" ".join(sent[0]) for sent in references_val])
bleu_scores = utils.calculate_bleu_scores(references_val, hypotheses_val)
self.epoch_bleu_score_val['1'].append(bleu_scores[0])
self.epoch_bleu_score_val['2'].append(bleu_scores[1])
self.epoch_bleu_score_val['3'].append(bleu_scores[2])
self.epoch_bleu_score_val['4'].append(bleu_scores[3])
