def test_beam(self):
src_voc = Vocabulary()
trg_voc = Vocabulary()
for tok in "</s> I am Philip You are a".split():
src_voc[tok]
for tok in "</s> 私 は フィリップ です 1 2 3".split():
trg_voc[tok]
model = EncDecNMT(Args("attn"), src_voc, trg_voc, optimizer=optimizers.SGD())
model_out = "/tmp/model-nmt.temp"
X, Y = src_voc, trg_voc
# Train with 1 example
src = np.array([[X["I"], X["am"], X["Philip"]]], dtype=np.int32)
trg = np.array([[Y["私"], Y["は"], Y["フィリップ"], Y["です"]]], dtype=np.int32)
model.train(src, trg)
# Save
serializer = ModelSerializer(model_out)
serializer.save(model)
# Load
model1 = EncDecNMT(InitArgs(model_out))
k = model.classify(src, beam=10)
def save_model(epoch):
out_file = args.model_out
if args.save_models:
out_file += "-" + str(epoch)
UF.trace("saving model to " + out_file + "...")
serializer = ModelSerializer(out_file)
serializer.save(model)
def test_NMT_2_read_write(self):
for model in ["encdec", "attn"]:
src_voc = Vocabulary()
trg_voc = Vocabulary()
for tok in "</s> I am Philip".split():
src_voc[tok]
for tok in "</s> 私 は フィリップ です".split():
trg_voc[tok]
model = EncDecNMT(Args(model), src_voc, trg_voc, optimizer=optimizers.SGD())
model_out = "/tmp/nmt/tmp"
X, Y = src_voc, trg_voc
# Train with 1 example
src = np.array([[X["I"], X["am"], X["Philip"]]], dtype=np.int32)
trg = np.array([[Y["私"], Y["は"], Y["フィリップ"], Y["です"]]], dtype=np.int32)
model.train(src, trg)
# Save
serializer = ModelSerializer(model_out)
serializer.save(model)
# Load
model1 = EncDecNMT(InitArgs(model_out))
# Check
self.assertModelEqual(model._model, model1._model)
def __init__(self, args, X=None, Y=None, optimizer=None, use_gpu=-1, collect_output=False, activation=F.tanh):
## We only instantitate the descendant of this class
assert hasattr(self, "_all_models"), "Shoudln't instantitate this class."
## Default configuration
self._opt = optimizer
self._xp, use_gpu = UF.setup_gpu(use_gpu)
self._collect_output = collect_output
self._gpu_id = use_gpu
self._train_state = self._train_state = { "loss": 150, "epoch": 0 }
## Loading Classifier
if args.init_model:
serializer = ModelSerializer(args.init_model)
serializer.load(self, self._all_models, xp=self._xp)
self._model.report(sys.stderr, verbosity=1)
else:
args.input = len(X)
args.output = len(Y)
self._model = UF.select_model(args.model, self._all_models)(X, Y, args, xp=self._xp)
## Use GPU or not?
if use_gpu >= 0:
self._model = self._model.to_gpu(use_gpu)
## Setup Optimizer
if optimizer is not None:
self._opt.setup(self._model)
if args.init_model:
serializer.load_optimizer(self._opt)
def test_read_write(self):
model = "/tmp/chainer-test/text/model.temp"
X, Y = self.src_voc, self.trg_voc
# Train with 1 example
inp = np.array([[X["Philip"], X["I"]]], dtype=np.int32)
out = np.array([[Y["フィリップ"], Y["私"]]], dtype=np.int32)
self.model.train(inp, out)
# Save
serializer = ModelSerializer(model)
serializer.save(self.model)
# Load
model1 = ParallelTextClassifier(InitArgs(model))
# Check
self.assertModelEqual(self.model._model, model1._model)
def test_read_write(self):
model = "/tmp/rnn.temp"
serializer = ModelSerializer(model)
serializer._init_dir()
serializer._write_model(self.model)
model1 = serializer._read_model(self.Model)
self.assertModelEqual(self.model, model1)
