def test(self):
trace('loading model ...')
src_vocab = Vocabulary.load(self.model + '.srcvocab')
trg_vocab = Vocabulary.load(self.model + '.trgvocab')
encdec = EncoderDecoder.load_spec(self.model + '.spec')
serializers.load_hdf5(self.model + '.weights', encdec)
trace('generating translation ...')
generated = 0
with open(self.target, 'w') as fp:
for src_batch in gens.batch(gens.word_list(self.source), self.minibatch):
src_batch = fill_batch(src_batch)
K = len(src_batch)
trace('sample %8d - %8d ...' % (generated + 1, generated + K))
hyp_batch = self.forward(src_batch, None, src_vocab, trg_vocab, encdec, False, self.generation_limit)
source_cuont = 0
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
print("src : " + "".join(src_batch[source_cuont]).replace("</s>", ""))
print('hyp : ' +''.join(hyp))
print(' '.join(hyp), file=fp)
source_cuont = source_cuont + 1
generated += K
trace('finished.')
def test(self):
trace('loading model ...')
src_vocab = Vocabulary.load(self.model + '.srcvocab')
trg_vocab = Vocabulary.load(self.model + '.trgvocab')
encdec = EncoderDecoder.load_spec(self.model + '.spec')
serializers.load_hdf5(self.model + '.weights', encdec)
trace('generating translation ...')
generated = 0
with open(self.target, 'w') as fp:
for src_batch in gens.batch(gens.word_list(self.source),
self.minibatch):
src_batch = fill_batch(src_batch)
K = len(src_batch)
trace('sample %8d - %8d ...' % (generated + 1, generated + K))
hyp_batch = self.forward(src_batch, None, src_vocab, trg_vocab,
encdec, False, self.generation_limit)
source_cuont = 0
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
print("src : " +
"".join(src_batch[source_cuont]).replace("</s>", ""))
print('hyp : ' + ''.join(hyp))
print(' '.join(hyp), file=fp)
source_cuont = source_cuont + 1
generated += K
trace('finished.')
def test(args):
trace('loading model ...')
word_vocab = Vocabulary.load(args.model + '.words')
phrase_vocab = Vocabulary.load(args.model + '.phrases')
semiterminal_vocab = Vocabulary.load(args.model + '.semiterminals')
parser = Parser.load_spec(args.model + '.spec')
if args.use_gpu:
parser.to_gpu()
serializers.load_hdf5(args.model + '.weights', parser)
embed_cache = {}
parser.reset()
trace('generating parse trees ...')
with open(args.source) as fp:
for l in fp:
word_list = to_vram_words(convert_word_list(l.split(), word_vocab))
tree = combine_xbar(
restore_labels(
parser.forward(word_list, None, args.unary_limit,
embed_cache), phrase_vocab,
semiterminal_vocab))
print('( ' + tree_to_string(tree) + ' )')
trace('finished.')
def load(filename):
self = AttentionalTranslationModel()
with ModelFile(filename) as fp:
self.__src_vocab = Vocabulary.load(fp.get_file_pointer())
self.__trg_vocab = Vocabulary.load(fp.get_file_pointer())
self.__n_embed = int(fp.read())
self.__n_hidden = int(fp.read())
self.__make_model()
wrapper.begin_model_access(self.__model)
fp.read_embed(self.__model.w_xi)
fp.read_linear(self.__model.w_ia)
fp.read_linear(self.__model.w_aa)
fp.read_linear(self.__model.w_ib)
fp.read_linear(self.__model.w_bb)
fp.read_linear(self.__model.w_aw)
fp.read_linear(self.__model.w_bw)
fp.read_linear(self.__model.w_pw)
fp.read_linear(self.__model.w_we)
fp.read_linear(self.__model.w_ap)
fp.read_linear(self.__model.w_bp)
fp.read_embed(self.__model.w_yp)
fp.read_linear(self.__model.w_pp)
fp.read_linear(self.__model.w_cp)
fp.read_linear(self.__model.w_dp)
fp.read_linear(self.__model.w_py)
wrapper.end_model_access(self.__model)
return self
def test(args):
trace('loading model ...')
word_vocab = Vocabulary.load(args.model + '.words')
phrase_vocab = Vocabulary.load(args.model + '.phrases')
semiterminal_vocab = Vocabulary.load(args.model + '.semiterminals')
parser = Parser.load_spec(args.model + '.spec')
if args.use_gpu:
parser.to_gpu()
serializers.load_hdf5(args.model + '.weights', parser)
embed_cache = {}
parser.reset()
trace('generating parse trees ...')
with open(args.source) as fp:
for l in fp:
word_list = to_vram_words(convert_word_list(l.split(), word_vocab))
tree = combine_xbar(
restore_labels(
parser.forward(word_list, None, args.unary_limit, embed_cache),
phrase_vocab,
semiterminal_vocab))
print('( ' + tree_to_string(tree) + ' )')
trace('finished.')
def test(args):
trace('loading model ...')
src_vocab = Vocabulary.load(args.model + '.srcvocab')
trg_vocab = Vocabulary.load(args.model + '.trgvocab')
attmt = AttentionMT.load_spec(args.model + '.spec')
if args.use_gpu:
attmt.to_gpu()
serializers.load_hdf5(args.model + '.weights', attmt)
trace('generating translation ...')
generated = 0
with open(args.target, 'w') as fp:
for src_batch in gens.batch(gens.word_list(args.source), args.minibatch):
src_batch = fill_batch(src_batch)
K = len(src_batch)
trace('sample %8d - %8d ...' % (generated + 1, generated + K))
hyp_batch = forward(src_batch, None, src_vocab, trg_vocab, attmt, False, args.generation_limit)
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
print(' '.join(hyp), file=fp)
generated += K
trace('finished.')
def load(filename):
self = AttentionalTranslationModel()
with ModelFile(filename) as fp:
self.__src_vocab = Vocabulary.load(fp.get_file_pointer())
self.__trg_vocab = Vocabulary.load(fp.get_file_pointer())
self.__n_embed = int(fp.read())
self.__n_hidden = int(fp.read())
self.__make_model()
wrapper.begin_model_access(self.__model)
fp.read_embed(self.__model.w_xi)
fp.read_linear(self.__model.w_ia)
fp.read_linear(self.__model.w_aa)
fp.read_linear(self.__model.w_ib)
fp.read_linear(self.__model.w_bb)
fp.read_linear(self.__model.w_aw)
fp.read_linear(self.__model.w_bw)
fp.read_linear(self.__model.w_pw)
fp.read_linear(self.__model.w_we)
fp.read_linear(self.__model.w_ap)
fp.read_linear(self.__model.w_bp)
fp.read_embed(self.__model.w_yp)
fp.read_linear(self.__model.w_pp)
fp.read_linear(self.__model.w_cp)
fp.read_linear(self.__model.w_dp)
fp.read_linear(self.__model.w_py)
wrapper.end_model_access(self.__model)
return self
def __predict_sentence(self, src_batch):
dialogue = EncoderDecoderModelForwardSlack(self.parameter)
src_vocab = Vocabulary.load(self.model_name + '.srcvocab')
trg_vocab = Vocabulary.load(self.model_name + '.trgvocab')
model = EncoderDecoder.load_spec(self.model_name + '.spec')
serializers.load_hdf5(dialogue.model + '.weights', model)
hyp_batch = dialogue.forward(src_batch, None, src_vocab, trg_vocab, model, False, self.generation_limit)
return hyp_batch
def __predict_sentence(self, src_batch):
dialogue = EncoderDecoderModelForwardSlack(self.parameter)
src_vocab = Vocabulary.load(self.model_name + '.srcvocab')
trg_vocab = Vocabulary.load(self.model_name + '.trgvocab')
model = EncoderDecoder.load_spec(self.model_name + '.spec')
serializers.load_hdf5(dialogue.model + '.weights', model)
hyp_batch = dialogue.forward(src_batch, None, src_vocab, trg_vocab,
model, False, self.generation_limit)
return hyp_batch
def train(args):
if os.path.exists("./model/vocab.bin"):
src_vocab = Vocabulary.load("./model/vocab.bin")
else:
src_vocab = Vocabulary.new(gens.word_list(args.source), args.n_vocab)
src_vocab.save('./model/vocab.bin')
if os.path.exists("./model/tag.bin"):
trg_tag = Vocabulary.load("./model/tag.bin")
else:
trg_tag = Vocabulary.new(gens.word_list(args.target), args.n_tag)
trg_tag.save('./model/tag.bin')
print("vocab_len:{}".format(src_vocab.__len__))
print("tag_len:{}".format(trg_tag.__len__))
encdec = BiEncDecLSTM(args.n_vocab, args.layer, args.embed, args.hidden,
args.n_tag)
optimizer = optimizers.Adam()
optimizer.setup(encdec)
for e_i in range(args.epoch):
tt_list = [[src_vocab.stoi(char) for char in char_arr]
for char_arr in gens.word_list(args.source_tr)]
tag_list = [
trg_tag.stoi(tag[0]) for tag in gens.word_list(args.target_tr)
]
print("{}:{}".format(len(tt_list), len(tag_list)))
assert len(tt_list) == len(tag_list)
ind_arr = [ri for ri in range(len(tt_list))]
random.shuffle(ind_arr)
tt_now = (tt_list[ri] for ri in ind_arr)
tag_now = (tag_list[ri] for ri in ind_arr)
tt_gen = gens.batch(tt_now, args.batchsize)
tag_gen = gens.batch(tag_now, args.batchsize)
for tt, tag in zip(tt_gen, tag_gen):
y_ws = encdec(tt)
teac_arr = [src_vocab.itos(t) for t in tt[0]]
pred_arr = [trg_tag.itos(y_each.data.argmax(0)) for y_each in y_ws]
print("teach:{}:{}:{}".format(teac_arr, trg_tag.itos(tag[0]),
pred_arr[0]))
tag = xp.array(tag, dtype=xp.int32)
loss = F.softmax_cross_entropy(y_ws, tag)
encdec.cleargrads()
loss.backward()
optimizer.update()
# loss.backward()
# optimizer.target.cleargrads()
# loss.backward()
# loss.unchain_backward()
# optimizer.update()
serializers.save_npz('./model/attn_tag_model_{}.npz'.format(e_i),
encdec)
def __init__(self, args):
trace('loading model ...')
self.args = args
self.src_vocab = Vocabulary.load(args.model + '.srcvocab')
self.trg_vocab = Vocabulary.load(args.model + '.trgvocab')
self.encdec = EncoderDecoder.load_spec(args.model + '.spec')
if args.use_gpu:
self.encdec.to_gpu()
serializers.load_hdf5(args.model + '.weights', self.encdec)
trace('generating translation ...')
def __predict_sentence(self, src_batch):
"""
predict sentence
:param src_batch: get the source sentence
:return:
"""
dialogue = EncoderDecoderModelAttention(self.parameter)
src_vocab = Vocabulary.load(self.model_name + '.srcvocab')
trg_vocab = Vocabulary.load(self.model_name + '.trgvocab')
model = AttentionDialogue.load_spec(self.model_name + '.spec', self.XP)
serializers.load_hdf5(self.model_name + '.weights', model)
hyp_batch = dialogue.forward_implement(src_batch, None, src_vocab, trg_vocab, model, False, self.generation_limit)
return hyp_batch
def __predict_sentence(self, src_batch):
"""
predict sentence
:param src_batch: get the source sentence
:return:
"""
dialogue = EncoderDecoderModelAttention(self.parameter)
src_vocab = Vocabulary.load(self.model_name + '.srcvocab')
trg_vocab = Vocabulary.load(self.model_name + '.trgvocab')
model = AttentionDialogue.load_spec(self.model_name + '.spec', self.XP)
serializers.load_hdf5(self.model_name + '.weights', model)
hyp_batch = dialogue.forward_implement(src_batch, None, src_vocab,
trg_vocab, model, False,
self.generation_limit)
return hyp_batch
def load(self, filename):
with ModelFile(filename) as fp:
self.src_vocab = Vocabulary.load(fp.get_file_pointer())
self.trg_vocab = Vocabulary.load(fp.get_file_pointer())
self.n_embed = int(fp.read())
self.n_hidden = int(fp.read())
self.make_model()
wrapper.begin_model_access(self.model)
fp.read_embed(self.model.weight_xi)
fp.read_linear(self.model.weight_ip)
fp.read_linear(self.model.weight_pp)
fp.read_linear(self.model.weight_pq)
fp.read_linear(self.model.weight_qj)
fp.read_linear(self.model.weight_jy)
fp.read_embed(self.model.weight_yq)
fp.read_linear(self.model.weight_qq)
wrapper.end_model_access(self.model)
return self
def load(self, filename):
with ModelFile(filename) as fp:
self.src_vocab = Vocabulary.load(fp.get_file_pointer())
self.trg_vocab = Vocabulary.load(fp.get_file_pointer())
self.n_embed = int(fp.read())
self.n_hidden = int(fp.read())
self.make_model()
wrapper.begin_model_access(self.model)
fp.read_embed(self.model.weight_xi)
fp.read_linear(self.model.weight_ip)
fp.read_linear(self.model.weight_pp)
fp.read_linear(self.model.weight_pq)
fp.read_linear(self.model.weight_qj)
fp.read_linear(self.model.weight_jy)
fp.read_embed(self.model.weight_yq)
fp.read_linear(self.model.weight_qq)
wrapper.end_model_access(self.model)
return self
def test(args, epoch):
model_name = "./model/attn_tag_model_{}.npz".format(epoch)
encdec = BiEncDecLSTM(args.n_vocab, args.layer, args.embed, args.hidden,
args.n_tag)
serializers.load_npz(model_name, encdec)
src_vocab = Vocabulary.load("./model/vocab.bin")
trg_tag = Vocabulary.load("./model/tag.bin")
tt_now = ([src_vocab.stoi(char) for char in char_arr]
for char_arr in gens.word_list(args.source_te))
tag_now = (trg_tag.stoi(tag[0]) for tag in gens.word_list(args.target_te))
tt_gen = gens.batch(tt_now, args.batchsize)
tag_gen = gens.batch(tag_now, args.batchsize)
correct_num = 0
wrong_num = 0
fw = codecs.open("./output/result_attn_tw{}.csv".format(epoch),
"w",
encoding="utf-8")
fw.write("台詞,教師キャラ,予測キャラ,予測値,›単語\n")
for tt, tag in zip(tt_gen, tag_gen):
y, att_w = encdec.callAndAtt(tt)
max_y = [
max(
F.softmax(F.reshape(y_each.data,
(1, len(y_each.data)))).data[0])
for y_each in y
]
y = [y_each.data.argmax(0) for y_each in y]
for tt_e, y_e, tag_e, max_y_e, att_w_e in zip(tt, y, tag, max_y,
att_w):
txt = ",".join([src_vocab.itos(id) for id in tt_e])
tag_e = trg_tag.itos(tag_e)
y_e = trg_tag.itos(y_e)
att_ind = att_w_e.data.argmax()
most_word = src_vocab.itos(tt_e[att_ind])
fw.write("{}:{}:{}:{}:{}\n".format(txt, tag_e, y_e, max_y_e,
most_word))
correct_num += len([1 for y_e, tag_e in zip(y, tag) if y_e == tag_e])
wrong_num += len([1 for y_e, tag_e in zip(y, tag) if y_e != tag_e])
print("epoch:{}".format(epoch))
print(" correct:{}".format(correct_num))
print(" wrong:{}".format(wrong_num))
fw.write("correct{}\n".format(correct_num))
fw.write("wrong:{}\n".format(wrong_num))
fw.close()
def load(filename):
self = EncoderDecoderModel()
with ModelFile(filename) as fp:
self.__src_vocab = Vocabulary.load(fp.get_file_pointer())
self.__trg_vocab = Vocabulary.load(fp.get_file_pointer())
self.__n_embed = int(fp.read())
self.__n_hidden = int(fp.read())
self.__make_model()
wrapper.begin_model_access(self.__model)
fp.read_embed(self.__model.w_xi)
fp.read_linear(self.__model.w_ip)
fp.read_linear(self.__model.w_pp)
fp.read_linear(self.__model.w_pq)
fp.read_linear(self.__model.w_qj)
fp.read_linear(self.__model.w_jy)
fp.read_embed(self.__model.w_yq)
fp.read_linear(self.__model.w_qq)
wrapper.end_model_access(self.__model)
return self
def test(self):
"""
Test method
You have to parepare the train model
"""
trace('loading model ...')
prefix = self.model
model_path = APP_ROOT + "/model/" + prefix
src_vocab = Vocabulary.load(model_path + '.srcvocab')
trg_vocab = Vocabulary.load(model_path + '.trgvocab')
self.attention_dialogue = AttentionDialogue.load_spec(
model_path + '.spec', self.XP)
serializers.load_hdf5(model_path + '.weights', self.attention_dialogue)
trace('generating translation ...')
generated = 0
with open(self.test_target, 'w') as fp:
for src_batch in gens.batch(gens.word_list(self.source),
self.minibatch):
src_batch = fill_batch(src_batch)
K = len(src_batch)
trace('sample %8d - %8d ...' % (generated + 1, generated + K))
hyp_batch = self.forward_implement(src_batch, None, src_vocab,
trg_vocab,
self.attention_dialogue,
False,
self.generation_limit)
source_cuont = 0
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
print("src : " +
"".join(src_batch[source_cuont]).replace("</s>", ""))
print('hyp : ' + ''.join(hyp))
print(' '.join(hyp), file=fp)
source_cuont = source_cuont + 1
generated += K
trace('finished.')
def load(filename):
self = EncoderDecoderModel()
with ModelFile(filename) as fp:
self.__src_vocab = Vocabulary.load(fp.get_file_pointer())
self.__trg_vocab = Vocabulary.load(fp.get_file_pointer())
self.__n_embed = int(fp.read())
self.__n_hidden = int(fp.read())
self.__make_model()
wrapper.begin_model_access(self.__model)
fp.read_embed(self.__model.w_xi)
fp.read_linear(self.__model.w_ip)
fp.read_linear(self.__model.w_pp)
fp.read_linear(self.__model.w_pq)
fp.read_linear(self.__model.w_qj)
fp.read_linear(self.__model.w_jy)
fp.read_embed(self.__model.w_yq)
fp.read_linear(self.__model.w_qq)
wrapper.end_model_access(self.__model)
return self
def test(args):
trace('loading model ...')
word_vocab = Vocabulary.load(args.model + '.words')
phrase_vocab = Vocabulary.load(args.model + '.phrases')
semi_vocab = Vocabulary.load(args.model + '.semiterminals')
parser = Parser.load_spec(args.model + '.spec')
if USE_GPU:
parser.to_gpu()
serializers.load_hdf5(args.model + '.weights', parser)
trace('generating parse trees ...')
with open(args.source) as fp:
for l in fp:
word_list = convert_word_list(l.split(), word_vocab)
tree = restore_labels(
parser.forward(word_list, None, args.unary_limit),
phrase_vocab, semi_vocab)
print('( ' + tree_to_string(tree) + ' )')
trace('finished.')
def test(args):
trace('loading model ...')
src_vocab = Vocabulary.load(args.model + '.srcvocab')
trg_vocab = Vocabulary.load(args.model + '.trgvocab')
encdec = EncoderDecoder.load_spec(args.model + '.spec')
if args.use_gpu:
encdec.to_gpu()
serializers.load_hdf5(args.model + '.weights', encdec)
trace('generating translation ...')
generated = 0
temp = gens.to_words(args.target)
# temp.append("</s>")
src_batch = []
src_batch.append(temp)
# src_batch = [['私は', '太郎', 'です', '(´', 'ー', '｀*)', 'ｳﾝｳﾝ', '</s>']]
src_batch = fill_batch(src_batch)
print("src_batch:", src_batch)
K = len(src_batch)
trace('sample %8d - %8d ...' % (generated + 1, generated + K))
print("question:")
for srp in src_batch:
srp.append('</s>')
srp = srp[:srp.index('</s>')]
print(''.join(srp))
hyp_batch = forward(src_batch, None, src_vocab, trg_vocab, encdec, False,
args.generation_limit)
print("answser:")
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
print(''.join(hyp))
print("----------------")
generated += K
trace('finished.')
def load(filename):
self = TransSegmentationModel()
with ModelFile(filename) as fp:
self.__vocab = Vocabulary.load(fp.get_file_pointer())
self.__n_context = int(fp.read())
self.__n_hidden = int(fp.read())
self.__make_model()
wrapper.begin_model_access(self.__model)
fp.read_embed(self.__model.w_xh)
fp.read_linear(self.__model.w_hy)
wrapper.end_model_access(self.__model)
return self
def load(filename):
self = SegmentationModel()
with ModelFile(filename) as fp:
self.__vocab = Vocabulary.load(fp.get_file_pointer())
self.__n_context = int(fp.read())
self.__n_hidden = int(fp.read())
self.__make_model()
wrapper.begin_model_access(self.__model)
fp.read_embed(self.__model.w_xh)
fp.read_linear(self.__model.w_hy)
wrapper.end_model_access(self.__model)
return self
def setCateg(self, args):
categ_name = "./{}/categ_{}.bin".format(args.dataname, args.dataname)
if os.path.exists(categ_name):
categ_vocab = Vocabulary.load(categ_name)
else:
set_cat = set()
[[set_cat.add(word) for word in word_arr] for word_arr in gens.word_list(args.category)]
n_categ = len(set_cat) + 3
print("n_categ:{}".format(n_categ))
categ_vocab = Vocabulary.new(gens.word_list(args.category), n_categ)
categ_vocab.save(categ_name)
self.categ_vocab = categ_vocab
return categ_vocab
def test(args):
trace('loading model ...')
word_vocab = Vocabulary.load(args.model + '.words')
phrase_vocab = Vocabulary.load(args.model + '.phrases')
semi_vocab = Vocabulary.load(args.model + '.semiterminals')
parser = Parser.load_spec(args.model + '.spec')
if USE_GPU:
parser.to_gpu()
serializers.load_hdf5(args.model + '.weights', parser)
trace('generating parse trees ...')
with open(args.source) as fp:
for l in fp:
word_list = convert_word_list(l.split(), word_vocab)
tree = restore_labels(
parser.forward(word_list, None, args.unary_limit),
phrase_vocab,
semi_vocab
)
print('( ' + tree_to_string(tree) + ' )')
trace('finished.')
def test(self):
"""
Test method
You have to parepare the train model
"""
trace("loading model ...")
prefix = self.model
model_path = APP_ROOT + "/model/" + prefix
src_vocab = Vocabulary.load(model_path + ".srcvocab")
trg_vocab = Vocabulary.load(model_path + ".trgvocab")
self.attention_dialogue = AttentionDialogue.load_spec(model_path + ".spec", self.XP)
serializers.load_hdf5(model_path + ".weights", self.attention_dialogue)
trace("generating translation ...")
generated = 0
with open(self.test_target, "w") as fp:
for src_batch in gens.batch(gens.word_list(self.source), self.minibatch):
src_batch = fill_batch(src_batch)
K = len(src_batch)
trace("sample %8d - %8d ..." % (generated + 1, generated + K))
hyp_batch = self.forward_implement(
src_batch, None, src_vocab, trg_vocab, self.attention_dialogue, False, self.generation_limit
)
source_cuont = 0
for hyp in hyp_batch:
hyp.append("</s>")
hyp = hyp[: hyp.index("</s>")]
print("src : " + "".join(src_batch[source_cuont]).replace("</s>", ""))
print("hyp : " + "".join(hyp))
print(" ".join(hyp), file=fp)
source_cuont = source_cuont + 1
generated += K
trace("finished.")
def setVocab(self, args):
vocab_name = "./{}/vocab_{}.bin".format(args.dataname, args.dataname)
if os.path.exists(vocab_name):
src_vocab = Vocabulary.load(vocab_name)
else:
set_vocab = set()
[[set_vocab.add(word) for word in word_arr] for word_arr in gens.word_list(args.source)]
n_vocab = len(set_vocab) + 3
print("n_vocab:{}".format(n_vocab))
print("arg_vocab:{}".format(args.n_vocab))
src_vocab = Vocabulary.new(gens.word_list(args.source), args.n_vocab)
src_vocab.save(vocab_name)
self.vocab = src_vocab
return src_vocab
def load(filename):
self = RNNSegmentationModel()
with ModelFile(filename) as fp:
self.__vocab = Vocabulary.load(fp.get_file_pointer())
self.__n_embed = int(fp.read())
self.__n_hidden = int(fp.read())
self.__make_model()
wrapper.begin_model_access(self.__model)
fp.read_embed(self.__model.w_xe)
fp.read_linear(self.__model.w_ea)
fp.read_linear(self.__model.w_aa)
fp.read_linear(self.__model.w_eb)
fp.read_linear(self.__model.w_bb)
fp.read_linear(self.__model.w_ay1)
fp.read_linear(self.__model.w_by1)
fp.read_linear(self.__model.w_ay2)
fp.read_linear(self.__model.w_by2)
wrapper.end_model_access(self.__model)
return self
def train(args):
trace('making vocabularies ...')
src_vocab = Vocabulary.new(gens.input_word_list(), args.vocab)
trg_vocab = Vocabulary.new(gens.output_word_list(), args.vocab)
trace('making model ...')
encdec = EncoderDecoder(args.vocab, args.embed, args.hidden)
if args.load_model != "":
print("model load %s ... " % (args.load_model))
src_vocab = Vocabulary.load(args.load_model + '.srcvocab')
trg_vocab = Vocabulary.load(args.load_model + '.trgvocab')
encdec = EncoderDecoder.load_spec(args.load_model + '.spec')
serializers.load_hdf5(args.load_model + '.weights', encdec)
if args.use_gpu:
encdec.to_gpu()
for epoch in range(args.epoch):
trace('epoch %d/%d: ' % (epoch + 1, args.epoch))
trained = 0
gen1 = gens.input_word_list()
gen2 = gens.output_word_list()
gen3 = gens.batch(
gens.sorted_parallel(gen1, gen2, 100 * args.minibatch),
args.minibatch)
opt = optimizers.AdaGrad(lr=0.01)
opt.setup(encdec)
opt.add_hook(optimizer.GradientClipping(5))
for src_batch, trg_batch in gen3:
src_batch = fill_batch(src_batch)
trg_batch = fill_batch(trg_batch)
K = len(src_batch)
hyp_batch, loss = forward(src_batch, trg_batch, src_vocab,
trg_vocab, encdec, True, 0)
loss.backward()
opt.update()
for k in range(K):
trace('epoch %3d/%3d, sample %8d' %
(epoch + 1, args.epoch, trained + k + 1))
trace(
' src = ' +
' '.join([x if x != '</s>' else '*'
for x in src_batch[k]]))
trace(
' trg = ' +
' '.join([x if x != '</s>' else '*'
for x in trg_batch[k]]))
trace(
' hyp = ' +
' '.join([x if x != '</s>' else '*'
for x in hyp_batch[k]]))
trained += K
if epoch % args.model_save_timing == 0:
trace('saving model ...')
prefix = args.model + '.%03.d' % (epoch + 1)
src_vocab.save(prefix + '.srcvocab')
trg_vocab.save(prefix + '.trgvocab')
encdec.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', encdec)
trace('finished.')
from util.vocabulary import Vocabulary
from util import generators as gens
from util.controller import Controller
from util.wrapper import wrapper
from util.const import *
if __name__ == '__main__':
args = parse_args()
trace('initializing ...')
wrapper = wrapper(args.gpu_id)
wrapper.init()
trace('loading vocab ...')
# src_vocab = Vocabulary.load(args.src_vocab)
# trg_vocab = Vocabulary.load(args.trg_vocab)
src_vocab = Vocabulary.load(VOCAB_SRC)
trg_vocab = Vocabulary.load(VOCAB_TRG)
controller = Controller(args.folder_name)
if args.mode == 'train':
controller.train_model(BasicEncoderDecoderModel, src_vocab, trg_vocab,
args)
elif args.mode == 'dev':
controller.dev_model(BasicEncoderDecoderModel, src_vocab, trg_vocab,
args)
elif args.mode == 'test':
controller.test_model(BasicEncoderDecoderModel, src_vocab, trg_vocab,
args)
