def save(self, dir=None):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
for attr in vars(self):
prop = getattr(self, attr)
if isinstance(prop, chainer.Chain) or isinstance(prop, chainer.optimizer.GradientMethod):
serializers.save_hdf5(dir + "/%s_%s.hdf5" % (self.name, attr), prop)
print "model saved."
python类save_hdf5()的实例源码
def save(self, dir=None):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
for attr in vars(self):
prop = getattr(self, attr)
if isinstance(prop, chainer.Chain) or isinstance(prop, chainer.optimizer.GradientMethod):
serializers.save_hdf5(dir + "/%s_%s.hdf5" % (self.name, attr), prop)
print "model saved."
def train(args):
trace('loading corpus ...')
with open(args.source) as fp:
trees = [make_tree(l) for l in fp]
trace('extracting leaf nodes ...')
word_lists = [extract_words(t) for t in trees]
lower_lists = [[w.lower() for w in words] for words in word_lists]
trace('extracting gold operations ...')
op_lists = [make_operations(t) for t in trees]
trace('making vocabulary ...')
word_vocab = Vocabulary.new(lower_lists, args.vocab)
phrase_set = set()
semiterminal_set = set()
for tree in trees:
phrase_set |= set(extract_phrase_labels(tree))
semiterminal_set |= set(extract_semiterminals(tree))
phrase_vocab = Vocabulary.new([list(phrase_set)], len(phrase_set), add_special_tokens=False)
semiterminal_vocab = Vocabulary.new([list(semiterminal_set)], len(semiterminal_set), add_special_tokens=False)
trace('converting data ...')
word_lists = [convert_word_list(x, word_vocab) for x in word_lists]
op_lists = [convert_op_list(x, phrase_vocab, semiterminal_vocab) for x in op_lists]
trace('start training ...')
parser = Parser(
args.vocab, args.embed, args.queue, args.stack, args.srstate,
len(phrase_set), len(semiterminal_set),
)
if args.use_gpu:
parser.to_gpu()
opt = optimizers.AdaGrad(lr = 0.005)
opt.setup(parser)
opt.add_hook(optimizer.GradientClipping(5))
for epoch in range(args.epoch):
n = 0
for samples in batch(zip(word_lists, op_lists), args.minibatch):
parser.zerograds()
loss = XP.fzeros(())
for word_list, op_list in zip(*samples):
trace('epoch %3d, sample %6d:' % (epoch + 1, n + 1))
loss += parser.forward(word_list, op_list, 0)
n += 1
loss.backward()
opt.update()
trace('saving model ...')
prefix = args.model + '.%03.d' % (epoch + 1)
word_vocab.save(prefix + '.words')
phrase_vocab.save(prefix + '.phrases')
semiterminal_vocab.save(prefix + '.semiterminals')
parser.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', parser)
trace('finished.')
def train(args):
trace('loading corpus ...')
with open(args.source) as fp:
trees = [make_tree(l) for l in fp]
trace('extracting leaf nodes ...')
word_lists = [extract_words(t) for t in trees]
lower_lists = [[w.lower() for w in words] for words in word_lists]
trace('extracting gold operations ...')
op_lists = [make_operations(t) for t in trees]
trace('making vocabulary ...')
word_vocab = Vocabulary.new(lower_lists, args.vocab)
phrase_set = set()
semiterminal_set = set()
for tree in trees:
phrase_set |= set(extract_phrase_labels(tree))
semiterminal_set |= set(extract_semiterminals(tree))
phrase_vocab = Vocabulary.new([list(phrase_set)], len(phrase_set), add_special_tokens=False)
semiterminal_vocab = Vocabulary.new([list(semiterminal_set)], len(semiterminal_set), add_special_tokens=False)
trace('converting data ...')
word_lists = [convert_word_list(x, word_vocab) for x in word_lists]
op_lists = [convert_op_list(x, phrase_vocab, semiterminal_vocab) for x in op_lists]
trace('start training ...')
parser = Parser(
args.vocab, args.embed, args.queue, args.stack, args.srstate,
len(phrase_set), len(semiterminal_set),
)
if args.use_gpu:
parser.to_gpu()
opt = optimizers.AdaGrad(lr = 0.005)
opt.setup(parser)
opt.add_hook(optimizer.GradientClipping(5))
for epoch in range(args.epoch):
n = 0
for samples in batch(zip(word_lists, op_lists), args.minibatch):
parser.zerograds()
loss = XP.fzeros(())
for word_list, op_list in zip(*samples):
trace('epoch %3d, sample %6d:' % (epoch + 1, n + 1))
loss += parser.forward(word_list, op_list, 0)
n += 1
loss.backward()
opt.update()
trace('saving model ...')
prefix = args.model + '.%03.d' % (epoch + 1)
word_vocab.save(prefix + '.words')
phrase_vocab.save(prefix + '.phrases')
semiterminal_vocab.save(prefix + '.semiterminals')
parser.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', parser)
trace('finished.')
def train(args):
trace('loading corpus ...')
with open(args.source) as fp:
trees = [make_tree(l) for l in fp]
trace('extracting leaf nodes ...')
word_lists = [extract_words(t) for t in trees]
trace('extracting gold operations ...')
op_lists = [make_operations(t) for t in trees]
trace('making vocabulary ...')
word_vocab = Vocabulary.new(word_lists, args.vocab)
phrase_set = set()
semi_set = set()
for tree in trees:
phrase_set |= set(extract_phrase_labels(tree))
semi_set |= set(extract_semi_labels(tree))
phrase_vocab = Vocabulary.new([list(phrase_set)], len(phrase_set), add_special_tokens=False)
semi_vocab = Vocabulary.new([list(semi_set)], len(semi_set), add_special_tokens=False)
trace('converting data ...')
word_lists = [convert_word_list(x, word_vocab) for x in word_lists]
op_lists = [convert_op_list(x, phrase_vocab, semi_vocab) for x in op_lists]
trace('start training ...')
parser = Parser(
args.vocab, args.embed, args.queue, args.stack,
len(phrase_set), len(semi_set),
)
if USE_GPU:
parser.to_gpu()
opt = optimizers.AdaGrad(lr = 0.005)
opt.setup(parser)
opt.add_hook(optimizer.GradientClipping(5))
for epoch in range(args.epoch):
n = 0
for samples in batch(zip(word_lists, op_lists), args.minibatch):
parser.zerograds()
loss = my_zeros((), np.float32)
for word_list, op_list in zip(*samples):
trace('epoch %3d, sample %6d:' % (epoch + 1, n + 1))
loss += parser.forward(word_list, op_list, 0)
n += 1
loss.backward()
opt.update()
trace('saving model ...')
prefix = args.model + '.%03.d' % (epoch + 1)
word_vocab.save(prefix + '.words')
phrase_vocab.save(prefix + '.phrases')
semi_vocab.save(prefix + '.semiterminals')
parser.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', parser)
trace('finished.')
def train(args):
trace('loading corpus ...')
with open(args.source) as fp:
trees = [make_tree(l) for l in fp]
trace('extracting leaf nodes ...')
word_lists = [extract_words(t) for t in trees]
lower_lists = [[w.lower() for w in words] for words in word_lists]
trace('extracting gold operations ...')
op_lists = [make_operations(t) for t in trees]
trace('making vocabulary ...')
word_vocab = Vocabulary.new(lower_lists, args.vocab)
phrase_set = set()
semiterminal_set = set()
for tree in trees:
phrase_set |= set(extract_phrase_labels(tree))
semiterminal_set |= set(extract_semiterminals(tree))
phrase_vocab = Vocabulary.new([list(phrase_set)], len(phrase_set), add_special_tokens=False)
semiterminal_vocab = Vocabulary.new([list(semiterminal_set)], len(semiterminal_set), add_special_tokens=False)
trace('converting data ...')
word_lists = [convert_word_list(x, word_vocab) for x in word_lists]
op_lists = [convert_op_list(x, phrase_vocab, semiterminal_vocab) for x in op_lists]
trace('start training ...')
parser = Parser(
args.vocab, args.queue, args.stack, args.srstate,
len(phrase_set), len(semiterminal_set),
)
if args.use_gpu:
parser.to_gpu()
opt = optimizers.AdaGrad(lr = 0.005)
opt.setup(parser)
opt.add_hook(optimizer.GradientClipping(5))
for epoch in range(args.epoch):
n = 0
for samples in batch(zip(word_lists, op_lists), args.minibatch):
parser.zerograds()
loss = XP.fzeros(())
for word_list, op_list in zip(*samples):
trace('epoch %3d, sample %6d:' % (epoch + 1, n + 1))
loss += parser.forward(word_list, op_list, 0)
n += 1
loss.backward()
opt.update()
trace('saving model ...')
prefix = args.model + '.%03.d' % (epoch + 1)
word_vocab.save(prefix + '.words')
phrase_vocab.save(prefix + '.phrases')
semiterminal_vocab.save(prefix + '.semiterminals')
parser.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', parser)
trace('finished.')
def train(args):
trace('loading corpus ...')
with open(args.source) as fp:
trees = [make_tree(l) for l in fp]
trace('extracting leaf nodes ...')
word_lists = [extract_words(t) for t in trees]
lower_lists = [[w.lower() for w in words] for words in word_lists]
trace('extracting gold operations ...')
op_lists = [make_operations(t) for t in trees]
trace('making vocabulary ...')
word_vocab = Vocabulary.new(lower_lists, args.vocab)
phrase_set = set()
semiterminal_set = set()
for tree in trees:
phrase_set |= set(extract_phrase_labels(tree))
semiterminal_set |= set(extract_semiterminals(tree))
phrase_vocab = Vocabulary.new([list(phrase_set)], len(phrase_set), add_special_tokens=False)
semiterminal_vocab = Vocabulary.new([list(semiterminal_set)], len(semiterminal_set), add_special_tokens=False)
trace('converting data ...')
word_lists = [convert_word_list(x, word_vocab) for x in word_lists]
op_lists = [convert_op_list(x, phrase_vocab, semiterminal_vocab) for x in op_lists]
trace('start training ...')
parser = Parser(
args.vocab, args.embed, args.char_embed, args.queue,
args.stack, args.srstate, len(phrase_set), len(semiterminal_set),
)
if args.use_gpu:
parser.to_gpu()
opt = optimizers.AdaGrad(lr = 0.005)
opt.setup(parser)
opt.add_hook(optimizer.GradientClipping(5))
for epoch in range(args.epoch):
n = 0
for samples in batch(zip(word_lists, op_lists), args.minibatch):
parser.zerograds()
loss = XP.fzeros(())
for word_list, op_list in zip(*samples):
trace('epoch %3d, sample %6d:' % (epoch + 1, n + 1))
loss += parser.forward(word_list, op_list, 0)
n += 1
loss.backward()
opt.update()
trace('saving model ...')
prefix = args.model + '.%03.d' % (epoch + 1)
word_vocab.save(prefix + '.words')
phrase_vocab.save(prefix + '.phrases')
semiterminal_vocab.save(prefix + '.semiterminals')
parser.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', parser)
trace('finished.')
def train(args):
trace('loading corpus ...')
with open(args.source) as fp:
trees = [make_tree(l) for l in fp]
trace('extracting leaf nodes ...')
word_lists = [extract_words(t) for t in trees]
lower_lists = [[w.lower() for w in words] for words in word_lists]
trace('extracting gold operations ...')
op_lists = [make_operations(t) for t in trees]
trace('making vocabulary ...')
word_vocab = Vocabulary.new(lower_lists, args.vocab)
phrase_set = set()
semiterminal_set = set()
for tree in trees:
phrase_set |= set(extract_phrase_labels(tree))
semiterminal_set |= set(extract_semiterminals(tree))
phrase_vocab = Vocabulary.new([list(phrase_set)], len(phrase_set), add_special_tokens=False)
semiterminal_vocab = Vocabulary.new([list(semiterminal_set)], len(semiterminal_set), add_special_tokens=False)
trace('converting data ...')
word_lists = [convert_word_list(x, word_vocab) for x in word_lists]
op_lists = [convert_op_list(x, phrase_vocab, semiterminal_vocab) for x in op_lists]
trace('start training ...')
parser = Parser(
args.vocab, args.queue, args.stack, args.srstate,
len(phrase_set), len(semiterminal_set),
)
if args.use_gpu:
parser.to_gpu()
opt = optimizers.AdaGrad(lr = 0.005)
opt.setup(parser)
opt.add_hook(optimizer.GradientClipping(5))
for epoch in range(args.epoch):
n = 0
for samples in batch(zip(word_lists, op_lists), args.minibatch):
parser.zerograds()
loss = XP.fzeros(())
for word_list, op_list in zip(*samples):
trace('epoch %3d, sample %6d:' % (epoch + 1, n + 1))
loss += parser.forward(word_list, op_list, 0)
n += 1
loss.backward()
opt.update()
trace('saving model ...')
prefix = args.model + '.%03.d' % (epoch + 1)
word_vocab.save(prefix + '.words')
phrase_vocab.save(prefix + '.phrases')
semiterminal_vocab.save(prefix + '.semiterminals')
parser.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', parser)
trace('finished.')
def train(args):
trace('loading corpus ...')
with open(args.source) as fp:
trees = [make_tree(l) for l in fp]
trace('extracting leaf nodes ...')
word_lists = [extract_words(t) for t in trees]
lower_lists = [[w.lower() for w in words] for words in word_lists]
trace('extracting gold operations ...')
op_lists = [make_operations(t) for t in trees]
trace('making vocabulary ...')
word_vocab = Vocabulary.new(lower_lists, args.vocab)
phrase_set = set()
semiterminal_set = set()
for tree in trees:
phrase_set |= set(extract_phrase_labels(tree))
semiterminal_set |= set(extract_semiterminals(tree))
phrase_vocab = Vocabulary.new([list(phrase_set)], len(phrase_set), add_special_tokens=False)
semiterminal_vocab = Vocabulary.new([list(semiterminal_set)], len(semiterminal_set), add_special_tokens=False)
trace('converting data ...')
word_lists = [convert_word_list(x, word_vocab) for x in word_lists]
op_lists = [convert_op_list(x, phrase_vocab, semiterminal_vocab) for x in op_lists]
trace('start training ...')
parser = Parser(
args.vocab, args.embed, args.queue, args.stack, args.srstate,
len(phrase_set), len(semiterminal_set),
)
if args.use_gpu:
parser.to_gpu()
opt = optimizers.AdaGrad(lr = 0.005)
opt.setup(parser)
opt.add_hook(optimizer.GradientClipping(5))
for epoch in range(args.epoch):
n = 0
for samples in batch(zip(word_lists, op_lists), args.minibatch):
parser.zerograds()
loss = XP.fzeros(())
for word_list, op_list in zip(*samples):
trace('epoch %3d, sample %6d:' % (epoch + 1, n + 1))
loss += parser.forward(word_list, op_list, 0)
n += 1
loss.backward()
opt.update()
trace('saving model ...')
prefix = args.model + '.%03.d' % (epoch + 1)
word_vocab.save(prefix + '.words')
phrase_vocab.save(prefix + '.phrases')
semiterminal_vocab.save(prefix + '.semiterminals')
parser.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', parser)
trace('finished.')
def train(args):
trace('loading corpus ...')
with open(args.source) as fp:
trees = [make_tree(l) for l in fp]
trace('extracting leaf nodes ...')
word_lists = [extract_words(t) for t in trees]
trace('extracting gold operations ...')
op_lists = [make_operations(t) for t in trees]
trace('making vocabulary ...')
word_vocab = Vocabulary.new(word_lists, args.vocab)
phrase_set = set()
semi_set = set()
for tree in trees:
phrase_set |= set(extract_phrase_labels(tree))
semi_set |= set(extract_semi_labels(tree))
phrase_vocab = Vocabulary.new([list(phrase_set)], len(phrase_set), add_special_tokens=False)
semi_vocab = Vocabulary.new([list(semi_set)], len(semi_set), add_special_tokens=False)
trace('converting data ...')
word_lists = [convert_word_list(x, word_vocab) for x in word_lists]
op_lists = [convert_op_list(x, phrase_vocab, semi_vocab) for x in op_lists]
trace('start training ...')
parser = Parser(
args.vocab, args.embed, args.queue, args.stack,
len(phrase_set), len(semi_set),
)
if USE_GPU:
parser.to_gpu()
opt = optimizers.AdaGrad(lr = 0.005)
opt.setup(parser)
opt.add_hook(optimizer.GradientClipping(5))
for epoch in range(args.epoch):
n = 0
for samples in batch(zip(word_lists, op_lists), args.minibatch):
parser.zerograds()
loss = my_zeros((), np.float32)
for word_list, op_list in zip(*samples):
trace('epoch %3d, sample %6d:' % (epoch + 1, n + 1))
loss += parser.forward(word_list, op_list, 0)
n += 1
loss.backward()
opt.update()
trace('saving model ...')
prefix = args.model + '.%03.d' % (epoch + 1)
word_vocab.save(prefix + '.words')
phrase_vocab.save(prefix + '.phrases')
semi_vocab.save(prefix + '.semiterminals')
parser.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', parser)
trace('finished.')
def train(args):
trace('loading corpus ...')
with open(args.source) as fp:
trees = [make_tree(l) for l in fp]
trace('extracting leaf nodes ...')
word_lists = [extract_words(t) for t in trees]
trace('extracting gold operations ...')
op_lists = [make_operations(t) for t in trees]
trace('making vocabulary ...')
word_vocab = Vocabulary.new(word_lists, args.vocab)
phrase_set = set()
semi_set = set()
for tree in trees:
phrase_set |= set(extract_phrase_labels(tree))
semi_set |= set(extract_semi_labels(tree))
phrase_vocab = Vocabulary.new([list(phrase_set)], len(phrase_set), add_special_tokens=False)
semi_vocab = Vocabulary.new([list(semi_set)], len(semi_set), add_special_tokens=False)
trace('converting data ...')
word_lists = [convert_word_list(x, word_vocab) for x in word_lists]
op_lists = [convert_op_list(x, phrase_vocab, semi_vocab) for x in op_lists]
trace('start training ...')
parser = Parser(
args.vocab, args.embed, args.queue, args.stack,
len(phrase_set), len(semi_set),
)
if USE_GPU:
parser.to_gpu()
opt = optimizers.AdaGrad(lr = 0.005)
opt.setup(parser)
opt.add_hook(optimizer.GradientClipping(5))
for epoch in range(args.epoch):
n = 0
for samples in batch(zip(word_lists, op_lists), args.minibatch):
parser.zerograds()
loss = my_zeros((), np.float32)
for word_list, op_list in zip(*samples):
trace('epoch %3d, sample %6d:' % (epoch + 1, n + 1))
loss += parser.forward(word_list, op_list, 0)
n += 1
loss.backward()
opt.update()
trace('saving model ...')
prefix = args.model + '.%03.d' % (epoch + 1)
word_vocab.save(prefix + '.words')
phrase_vocab.save(prefix + '.phrases')
semi_vocab.save(prefix + '.semiterminals')
parser.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', parser)
trace('finished.')
def train(args):
trace('loading corpus ...')
with open(args.source) as fp:
trees = [make_tree(l) for l in fp]
trace('extracting leaf nodes ...')
word_lists = [extract_words(t) for t in trees]
lower_lists = [[w.lower() for w in words] for words in word_lists]
trace('extracting gold operations ...')
op_lists = [make_operations(t) for t in trees]
trace('making vocabulary ...')
word_vocab = Vocabulary.new(lower_lists, args.vocab)
phrase_set = set()
semiterminal_set = set()
for tree in trees:
phrase_set |= set(extract_phrase_labels(tree))
semiterminal_set |= set(extract_semiterminals(tree))
phrase_vocab = Vocabulary.new([list(phrase_set)], len(phrase_set), add_special_tokens=False)
semiterminal_vocab = Vocabulary.new([list(semiterminal_set)], len(semiterminal_set), add_special_tokens=False)
trace('converting data ...')
word_lists = [convert_word_list(x, word_vocab) for x in word_lists]
op_lists = [convert_op_list(x, phrase_vocab, semiterminal_vocab) for x in op_lists]
trace('start training ...')
parser = Parser(
args.vocab, args.queue, args.stack,
len(phrase_set), len(semiterminal_set),
)
if args.use_gpu:
parser.to_gpu()
opt = optimizers.AdaGrad(lr = 0.005)
opt.setup(parser)
opt.add_hook(optimizer.GradientClipping(5))
for epoch in range(args.epoch):
n = 0
for samples in batch(zip(word_lists, op_lists), args.minibatch):
parser.zerograds()
loss = XP.fzeros(())
for word_list, op_list in zip(*samples):
trace('epoch %3d, sample %6d:' % (epoch + 1, n + 1))
loss += parser.forward(word_list, op_list, 0)
n += 1
loss.backward()
opt.update()
trace('saving model ...')
prefix = args.model + '.%03.d' % (epoch + 1)
word_vocab.save(prefix + '.words')
phrase_vocab.save(prefix + '.phrases')
semiterminal_vocab.save(prefix + '.semiterminals')
parser.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', parser)
trace('finished.')
def train(args):
trace('loading corpus ...')
with open(args.source) as fp:
trees = [make_tree(l) for l in fp]
trace('extracting leaf nodes ...')
word_lists = [extract_words(t) for t in trees]
lower_lists = [[w.lower() for w in words] for words in word_lists]
trace('extracting gold operations ...')
op_lists = [make_operations(t) for t in trees]
trace('making vocabulary ...')
word_vocab = Vocabulary.new(lower_lists, args.vocab)
phrase_set = set()
semiterminal_set = set()
for tree in trees:
phrase_set |= set(extract_phrase_labels(tree))
semiterminal_set |= set(extract_semiterminals(tree))
phrase_vocab = Vocabulary.new([list(phrase_set)], len(phrase_set), add_special_tokens=False)
semiterminal_vocab = Vocabulary.new([list(semiterminal_set)], len(semiterminal_set), add_special_tokens=False)
trace('converting data ...')
word_lists = [convert_word_list(x, word_vocab) for x in word_lists]
op_lists = [convert_op_list(x, phrase_vocab, semiterminal_vocab) for x in op_lists]
trace('start training ...')
parser = Parser(
args.vocab, args.queue, args.stack,
len(phrase_set), len(semiterminal_set),
)
if args.use_gpu:
parser.to_gpu()
opt = optimizers.AdaGrad(lr = 0.005)
opt.setup(parser)
opt.add_hook(optimizer.GradientClipping(5))
for epoch in range(args.epoch):
n = 0
for samples in batch(zip(word_lists, op_lists), args.minibatch):
parser.zerograds()
loss = XP.fzeros(())
for word_list, op_list in zip(*samples):
trace('epoch %3d, sample %6d:' % (epoch + 1, n + 1))
loss += parser.forward(word_list, op_list, 0)
n += 1
loss.backward()
opt.update()
trace('saving model ...')
prefix = args.model + '.%03.d' % (epoch + 1)
word_vocab.save(prefix + '.words')
phrase_vocab.save(prefix + '.phrases')
semiterminal_vocab.save(prefix + '.semiterminals')
parser.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', parser)
trace('finished.')
def train(epoch_num):
image_groups, sentence_groups = make_groups(train_image_ids, train_sentences)
test_image_groups, test_sentence_groups = make_groups(test_image_ids, test_sentences, train=False)
for epoch in range(epoch_num):
batches = random_batches(image_groups, sentence_groups)
sum_loss = 0
sum_acc = 0
sum_size = 0
batch_num = len(batches)
for i, (image_id_batch, sentence_batch) in enumerate(batches):
loss, acc, size = forward(caption_net, images[image_id_batch], sentence_batch)
caption_net.cleargrads()
loss.backward()
loss.unchain_backward()
optimizer.update()
sentence_length = sentence_batch.shape[1]
sum_loss += float(loss.data) * size
sum_acc += acc * size
sum_size += size
if (i + 1) % 500 == 0:
print '{} / {} loss: {} accuracy: {}'.format(i + 1, batch_num, sum_loss / sum_size, sum_acc / sum_size)
print 'epoch: {} done'.format(epoch + 1)
print 'train loss: {} accuracy: {}'.format(sum_loss / sum_size, sum_acc / sum_size)
sum_loss = 0
sum_acc = 0
sum_size = 0
for image_ids, sentences in zip(test_image_groups, test_sentence_groups):
if len(sentences) == 0:
continue
size = len(sentences)
for i in range(0, size, batch_size):
image_id_batch = image_ids[i:i + batch_size]
sentence_batch = sentences[i:i + batch_size]
loss, acc, size = forward(caption_net, images[image_id_batch], sentence_batch, train=False)
sentence_length = sentence_batch.shape[1]
sum_loss += float(loss.data) * size
sum_acc += acc * size
sum_size += size
print 'test loss: {} accuracy: {}'.format(sum_loss / sum_size, sum_acc / sum_size)
serializers.save_hdf5(args.output + '_{0:04d}.model'.format(epoch), caption_net)
serializers.save_hdf5(args.output + '_{0:04d}.state'.format(epoch), optimizer)
