def main(opt):
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu
if opt.mode == "train":
st = time.time()
print('Loading data')
x_train, y_train, x_valid, y_valid, vocabulary_size = load_data(
"data", opt.debug)
num_training_data = x_train.shape[0]
sequence_length = x_train.shape[1]
print(num_training_data)
print('Vocab Size', vocabulary_size)
model = build_model(opt.embedding_dim, opt.hidden_size, opt.drop,
sequence_length, vocabulary_size)
print("Traning Model...")
history = model.fit(
x_train,
y_train,
batch_size=opt.batch_size,
epochs=opt.epochs,
verbose=1,
callbacks=[TestCallback((x_valid, y_valid), model=model)])
model.save(opt.saved_model)
print("Training cost time: ", time.time() - st)
elif opt.mode == "ensemble":
model1 = load_model(opt.saved_model1)
model1.name = 'model1'
for layer in model1.layers:
layer.name = layer.name + str("_1")
model2 = load_model(opt.saved_model2)
model2.name = 'model2'
for layer in model2.layers:
layer.name = layer.name + str("_2")
models = [model1, model2]
vocabulary = json.load(open(os.path.join("data", "vocab.json")))
predict_dict = predict_final_word_models(models, vocabulary, opt.input)
sub_file = make_submission(predict_dict, opt.student_id, opt.input)
if opt.score:
scoring(sub_file, os.path.join("data"), type="valid")
# x_train, y_train, x_valid, y_valid, vocabulary_size = load_data(
# "data", opt.debug)
# num_training_data = x_train.shape[0]
# sequence_length = x_train.shape[1]
# model_inputs = Input(shape=(sequence_length,), dtype='int32')
# model = ensemble(models, model_inputs)
# model.save(opt.model_to_be_saved)
else:
model = load_model(opt.saved_model)
vocabulary = json.load(open(os.path.join("data", "vocab.json")))
predict_dict = predict_final_word(model, vocabulary, opt.input)
sub_file = make_submission(predict_dict, opt.student_id, opt.input)
if opt.score:
scoring(sub_file, os.path.join("data"), type="valid")
def main(opt):
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu
if opt.mode == "train":
st = time.time()
print('Loading data')
x_train, y_train, x_valid, y_valid, vocabulary_size = load_data(
"data", opt.debug)
num_training_data = x_train.shape[0]
sequence_length = x_train.shape[1]
print(num_training_data)
print('Vocab Size', vocabulary_size)
model = build_model(opt.embedding_dim, opt.hidden_size, opt.drop,
sequence_length, vocabulary_size, opt.optimizer)
print("Traning Model...")
history = model.fit(
x_train,
y_train,
batch_size=opt.batch_size,
epochs=opt.epochs,
verbose=1,
callbacks=[TestCallback((x_valid, y_valid), model=model)])
model.save(opt.saved_model)
print("Training cost time: ", time.time() - st)
else:
model = load_model(opt.saved_model)
vocabulary = json.load(open(os.path.join("data", "vocab.json")))
predict_dict = predict_final_word(model, vocabulary, opt.input)
sub_file = make_submission(predict_dict, opt.student_id, opt.input)
if opt.score:
scoring(sub_file, os.path.join("data"), type="valid")
def main(opt):
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu
if opt.mode == "train":
st = time.time()
print('Loading data')
x_train, y_train, x_valid, y_valid, vocabulary_size = load_data(
"data", opt.debug)
num_training_data = x_train.shape[0]
sequence_length = x_train.shape[1]
print(num_training_data)
print('Vocab Size', vocabulary_size)
model = build_model(opt.embedding_dim, opt.hidden_size, opt.drop1,
opt.drop2, sequence_length, vocabulary_size)
print("Training Model...")
model.fit(x_train,
y_train,
batch_size=opt.batch_size,
epochs=opt.epochs,
verbose=2,
callbacks=[TestCallback((x_valid, y_valid), model=model)])
model.save(opt.saved_model)
print("Training cost time: ", time.time() - st)
else:
if opt.mode == "score_valid":
model = load_model(opt.saved_model)
vocabulary = json.load(open(os.path.join("data", "vocab.json")))
predict_dict = predict_final_word([model], vocabulary, opt.input)
sub_file = make_submission(predict_dict, opt.student_id, opt.input)
scoring(sub_file, os.path.join("data"), type="valid")
else:
model0 = load_model('models/model0.h5')
model1 = load_model('models/model1.h5')
model2 = load_model('models/model2.h5')
model3 = load_model('models/model3.h5')
model4 = load_model('models/model4.h5')
model5 = load_model('models/model5.h5')
model6 = load_model('models/model6.h5')
model7 = load_model('models/model7.h5')
model8 = load_model('models/model8.h5')
model9 = load_model('models/model9.h5')
model_list = [
model0, model1, model2, model3, model4, model5, model6, model7,
model8, model9
]
vocabulary = json.load(open(os.path.join("data", "vocab.json")))
predict_dict = predict_final_word(model_list, vocabulary,
opt.input)
sub_file = make_submission(predict_dict, opt.student_id, opt.input)
scoring(sub_file, os.path.join("data"), type="valid")
def main(opt):
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu
if opt.mode == "train":
st = time.time()
print('Loading data')
x_train, y_train, x_valid, y_valid, vocabulary_size = load_data(
"data", opt.debug)
num_training_data = x_train.shape[0]
sequence_length = x_train.shape[1]
print(num_training_data)
print('Vocab Size', vocabulary_size)
model = build_model(opt.embedding_dim, opt.hidden_size, opt.drop,
sequence_length, vocabulary_size)
print("Traning Model...")
history = model.fit(
x_train,
y_train,
batch_size=opt.batch_size,
epochs=opt.epochs,
verbose=1,
callbacks=[TestCallback((x_valid, y_valid), model=model)])
model.save(opt.saved_model)
# Save the model architecture
#with open('model_architecture.yaml', 'w') as f:
# f.write(model.to_json())
print("Training cost time: ", time.time() - st)
else:
# Model reconstruction from JSON file
#with open('model_architecture.yaml', 'r') as f:
# model = model_from_yaml(f.read())
model = load_model(
opt.saved_model,
custom_objects={'LayerNormalization': LayerNormalization})
vocabulary = json.load(open(os.path.join("data", "vocab.json")))
predict_dict = predict_final_word(model, vocabulary, opt.input)
sub_file = make_submission(predict_dict, opt.student_id, opt.input)
if opt.score:
scoring(sub_file, os.path.join("data"), type="valid")
def testScoringCase(self):
prediction_key = Prediction(contract_one=0.00,
contract_two=0.00,
liquidity=100,
resolved=False,
outcome='CONTRACT_ONE',
statement='Test',
end_time=datetime.datetime.now()).put()
user_key = Profile(balance=100,
user_ledger=[
LedgerRecords(
prediction_id=prediction_key.urlsafe(),
contract_one=10.00,
contract_two=0.00)
]).put()
trade_key = Trade(prediction_id=prediction_key,
user_id=user_key,
direction='BUY',
contract='CONTRACT_ONE',
quantity=10).put()
user = user_key.get()
audit = scoring()
self.assertEqual(10, audit[0]['earned'])
def main(opt):
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu
np.random.seed(opt.seed) # set a seed for reproduciaiblity
if opt.mode == "train":
st = time.time()
print('Loading data')
x_train, y_train, x_valid, y_valid, vocabulary_size = load_data(
"data", opt.debug)
num_training_data = x_train.shape[0]
sequence_length = x_train.shape[1]
print(num_training_data)
print('Vocab Size', vocabulary_size)
model = build_model(opt.model, opt.embedding_dim, opt.hidden_size,
opt.drop, opt.filter, sequence_length,
vocabulary_size)
adam = Adam()
model.compile(loss='sparse_categorical_crossentropy', optimizer=adam)
print("Traning Model...")
checkpoint = ModelCheckpoint(opt.saved_model,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min')
early = EarlyStopping(monitor="val_loss", mode="min", patience=5)
history = model.fit(x_train,
y_train,
batch_size=opt.batch_size,
epochs=100,
verbose=1,
validation_data=(x_valid, y_valid),
callbacks=[
TestCallback((x_valid, y_valid), model=model),
checkpoint, early
])
model.save(opt.saved_model)
print("Training cost time: ", time.time() - st)
elif opt.mode == "ensemble":
x_train, y_train, x_valid, y_valid, vocabulary_size = load_data(
"data", opt.debug)
num_training_data = x_train.shape[0]
sequence_length = x_train.shape[1]
print(num_training_data)
print('Vocab Size', vocabulary_size)
ENSEMBLE_DIR = "models/ensemble/"
model_files = []
for (dirpath, dirnames, filenames) in os.walk(ENSEMBLE_DIR):
model_files.extend(filenames)
break
models = []
model_count = 0
for filename in model_files:
model = load_model(ENSEMBLE_DIR + filename)
model.name = "model" + str(model_count)
model_count += 1
models.append(model)
build_save_ensemble_model(opt.saved_model, models, sequence_length)
else:
model = load_model(opt.saved_model)
vocabulary = json.load(open(os.path.join("data", "vocab.json")))
predict_dict = predict_final_word(model, vocabulary, opt.input)
sub_file = make_submission(predict_dict, opt.student_id, opt.input)
if opt.score:
scoring(sub_file, os.path.join("data"), type="valid")
def on_epoch_end(self, epoch, logs={}):
x, y = self.test_data
predict_dict = predict_final_word(self.model, self.vocabulary, self.filename)
sub_file = make_submission(predict_dict, opt.student_id, opt.input)
scoring(sub_file, os.path.join("data"), type="valid")
