def train(self, epoch):
trace('making vocabularies ...')
self.trg_vocab = Vocabulary.new(gens.word_list(self.target), self.vocab)
trace('making model ...')
trace('epoch %d/%d: ' % (epoch + 1, self.epoch))
opt = optimizers.AdaGrad(lr=0.01)
opt.setup(self.encdec)
opt.add_hook(optimizer.GradientClipping(5))
gen1 = gens.word_list(self.target)
gen = gens.batch(gen1, self.minibatch)
for trg_batch in gen:
self.batch_size = len(trg_batch)
self.trg_batch = fill_batch(trg_batch)
if len(trg_batch) != self.minibatch:
break
self.encdec.clear(self.batch_size)
self.__forward_img()
self.encdec.reset(self.batch_size)
loss, hyp_batch = self.__forward_word(self.trg_batch, self.encdec, True, 0)
loss.backward()
opt.update()
K = len(self.trg_batch) - 2
self.print_out(K, hyp_batch, epoch)
def train(self, epoch):
trace('making vocabularies ...')
self.trg_vocab = Vocabulary.new(gens.word_list(self.target),
self.vocab)
trace('making model ...')
trace('epoch %d/%d: ' % (epoch + 1, self.epoch))
opt = optimizers.AdaGrad(lr=0.01)
opt.setup(self.encdec)
opt.add_hook(optimizer.GradientClipping(5))
gen1 = gens.word_list(self.target)
gen = gens.batch(gen1, self.minibatch)
for trg_batch in gen:
self.batch_size = len(trg_batch)
self.trg_batch = fill_batch(trg_batch)
if len(trg_batch) != self.minibatch:
break
self.encdec.clear(self.batch_size)
self.__forward_img()
self.encdec.reset(self.batch_size)
loss, hyp_batch = self.__forward_word(self.trg_batch, self.encdec,
True, 0)
loss.backward()
opt.update()
K = len(self.trg_batch) - 2
self.print_out(K, hyp_batch, epoch)
def __init__(self):
self.parameter_dict = {}
train_path = APP_ROOT + "/Data/"
self.parameter_dict["id2image"] = train_path + "index2img_exclude.txt"
self.parameter_dict["id2caption"] = train_path + "index2caption.txt"
self.parameter_dict["target"] = train_path + "index2caption.txt"
self.parameter_dict["vocab"] = 5000
self.parameter_dict["embed"] = 300
self.parameter_dict["hidden"] = 500
self.parameter_dict["epoch"] = 20
self.parameter_dict["minibatch"] = 64
self.parameter_dict["generation_limit"] = 256
self.parameter_dict["use_gpu"] = False
self.parameter_dict["gpu_id"] = -1
self.parameter_dict["choose_model"] = "Alex_Model"
if self.parameter_dict["choose_model"] == "Alex_Model":
self.insize = 224
if self.parameter_dict["choose_model"] == "AlexBn_Model":
self.insize = 227
mean_image = pickle.load(open("mean.npy", 'rb'))
cropwidth = 256 - self.insize
self.start = cropwidth // 2
self.stop = self.start + self.insize
self.mean_image = mean_image[:, self.start:self.stop,
self.start:self.stop].copy()
self.x_batch = np.ndarray(
(self.parameter_dict["minibatch"], 3, self.insize, self.insize),
dtype=np.float32)
self.y_batch = np.ndarray((self.parameter_dict["minibatch"]),
dtype=np.int32)
self.trg_vocab = Vocabulary.new(
gens.word_list(self.parameter_dict["target"]),
self.parameter_dict["vocab"])
self.read_data = Read_Data(self.parameter_dict["id2image"],
"Data/val2014_resize",
self.parameter_dict["id2caption"])
self.read_data.load_image_list()
self.read_data.load_caption_list()
def __init__(self, use_gpu, gpu_id):
self.parameter_dict = {}
train_path = APP_ROOT + "/../../Chainer_Image_Caption_Neural_Network/Code/Data/"
self.resize_image_path = APP_ROOT + "/../../Chainer_Image_Caption_Neural_Network/Code/"
self.parameter_dict["id2image"] = train_path + "index2img_exclude.txt"
self.parameter_dict["id2caption"] = train_path + "index2caption.txt"
self.parameter_dict["target"] = train_path + "index2caption.txt"
self.parameter_dict["vocab"] = 5000
self.parameter_dict["embed"] = 300
self.parameter_dict["hidden"] = 200
self.parameter_dict["epoch"] = 20
self.parameter_dict["minibatch"] = 110
self.parameter_dict["generation_limit"] = 256
self.parameter_dict["use_gpu"] = use_gpu
self.parameter_dict["gpu_id"] = gpu_id
self.parameter_dict["choose_model"] = "Alex_Model"
if self.parameter_dict["choose_model"] == "Alex_Model":
self.insize = 224
if self.parameter_dict["choose_model"] == "AlexBn_Model":
self.insize = 227
mean_image = pickle.load(open("mean.npy", 'rb'))
cropwidth = 256 - self.insize
self.start = cropwidth // 2
self.stop = self.start + self.insize
self.mean_image = mean_image[:, self.start:self.stop, self.start:self.stop].copy()
self.x_batch = np.ndarray((self.parameter_dict["minibatch"], 3,
self.insize, self.insize), dtype=np.float32)
self.y_batch = np.ndarray((self.parameter_dict["minibatch"]),
dtype=np.int32)
self.trg_vocab = Vocabulary.new(gens.word_list(self.parameter_dict["target"]), self.parameter_dict["vocab"])
self.read_data = Read_Data(self.parameter_dict["id2image"],
"Data/val2014_resize",
self.parameter_dict["id2caption"])
self.read_data.load_image_list()
self.read_data.load_caption_list()
def train(self):
trace('making vocabularies ...')
trg_vocab = Vocabulary.new(gens.word_list(self.target), self.vocab)
trace('making model ...')
for epoch in range(self.epoch):
trace('epoch %d/%d: ' % (epoch + 1, self.epoch))
trained = 0
opt = optimizers.AdaGrad(lr=0.01)
opt.setup(self.encdec)
opt.add_hook(optimizer.GradientClipping(5))
gen1 = gens.word_list(self.target)
gen = gens.batch(gen1, self.minibatch)
random_number = random.randint(0, self.minibatch - 1)
for trg_batch in gen:
self.trg_batch = fill_batch(trg_batch)
if len(self.trg_batch) != self.minibatch:
break
hyp_batch, loss = self.forward(trg_vocab, self.use_gpu,
self.gpu_id)
loss.backward()
opt.update()
K = len(self.trg_batch)
if trained == 0:
self.print_out(random_number, epoch, trained, hyp_batch)
trained += K
trace('saving model ...')
prefix = self.model
trg_vocab.save(prefix + '.trgvocab')
self.encdec.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', self.encdec)
trace('finished.')
def train(self):
trace('making vocabularies ...')
trg_vocab = Vocabulary.new(gens.word_list(self.target), self.vocab)
trace('making model ...')
for epoch in range(self.epoch):
trace('epoch %d/%d: ' % (epoch + 1, self.epoch))
trained = 0
opt = optimizers.AdaGrad(lr=0.01)
opt.setup(self.encdec)
opt.add_hook(optimizer.GradientClipping(5))
gen1 = gens.word_list(self.target)
gen = gens.batch(gen1, self.minibatch)
random_number = random.randint(0, self.minibatch - 1)
for trg_batch in gen:
self.trg_batch = fill_batch(trg_batch)
if len(self.trg_batch) != self.minibatch:
break
hyp_batch, loss = self.forward(trg_vocab, self.use_gpu, self.gpu_id)
loss.backward()
opt.update()
K = len(self.trg_batch)
if trained == 0:
self.print_out(random_number, epoch, trained, hyp_batch)
trained += K
trace('saving model ...')
prefix = self.model
trg_vocab.save(prefix + '.trgvocab')
self.encdec.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', self.encdec)
trace('finished.')
