# 0. 사용할 패키지 불러오기
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
# 1. 데이터 준비하기
x_train = np.loadtxt("./x_train.csv", delimiter=",")
y_train = np.loadtxt("./y_train.csv", delimiter=",")
x_test = np.loadtxt("./x_test.csv", delimiter=",")
# 2. 모델 구성하기
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# 3. 모델 학습과정 설정하기
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# 4. 모델 학습시키기
model.fit(x_train, y_train, epochs=100, batch_size=64)
# 5. 모델 결과 저장하기
y_pred = (model.predict(x_test) > 0.5).astype("int32")
np.savetxt('y_pred.csv', y_pred, fmt='%i')
