from keras.layers import Dense, Dropout
from keras.layers import Embedding
from keras.layers import LSTM
from keras.models import Sequential
from keras.preprocessing import sequence
# TODO
dataset = Data(path="data/",
stem=False,
simply=True,
stop_word=False,
delete_class=['0', '000'],
codif='bagofwords',
max_features=None)
X_train, y_train, X_test, y_test = dataset.train_test_split(0.8)
x, y = dataset.get_non_coded()
# create the model
embedding_vecor_length = 2
model = Sequential()
model.add(
Embedding(n_features,
embedding_vecor_length,
input_length=max_review_length))
model.add(LSTM(2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
"""
from toy_simpleNN import ToySimpleNN
from preprocess import Data
hyperparam = {
'activation': 'tanh',
'regularization': 'l2',
'batch_size': 20,
'learning_rate': 1e-4,
'valid_rate': 0.1,
'optimizer': 'SGD',
'train_step': 1000
}
data_set = Data()
data_set.load_OUTCAR("C:/Users/Seungwoo Hwang/Desktop/toy-simpleNN/OUTCAR")
#data_set = Data("OUTCAR")
x_train, y_train, x_test, y_test = data_set.train_test_split()
model = ToySimpleNN(act=hyperparam['activation'],
train_step=hyperparam['train_step'])
model.set_optimizer(optimizer=hyperparam['optimizer'])
model.load_data(data_set)
model.train()
model.test()