def evaluate_model(self, batch_size=1000):
"""
self: CaptioningSolver object
Prints unigram BLEU score averaged over 1000 training and val examples.
"""
BLEUscores = {}
for split in ['train', 'val']:
minibatch = sample_coco_minibatch(self.data,
split=split,
batch_size=batch_size)
gt_captions, features, urls = minibatch
gt_captions = decode_captions(gt_captions,
self.data['idx_to_word'])
sample_captions = self.model.sample(features)
sample_captions = decode_captions(sample_captions,
self.data['idx_to_word'])
total_score = 0.0
for gt_caption, sample_caption, url in zip(gt_captions,
sample_captions, urls):
total_score += self.BLEU_score(gt_caption, sample_caption)
BLEUscores[split] = total_score / len(sample_captions)
for split in BLEUscores:
print('Average BLEU score for %s: %f' % (split, BLEUscores[split]))
def _step(self):
"""
Make a single gradient update. This is called by train() and should not
be called manually.
"""
# Make a minibatch of training data
minibatch = sample_coco_minibatch(self.data,
batch_size=self.batch_size,
split='train')
captions, features, urls = minibatch
# Compute loss and gradient
loss, grads = self.model.loss(features, captions)
self.loss_history.append(loss)
# Perform a parameter update
for p, w in self.model.params.items():
dw = grads[p]
config = self.optim_configs[p]
next_w, next_config = self.update_rule(w, dw, config)
self.model.params[p] = next_w
self.optim_configs[p] = next_config