if __name__ == "__main__":
x_test = np.genfromtxt('X_test', delimiter=',')[1:]
x_test = preprocess(x_test)
x_test = preprocess(x_test)
model = Sequential()
model.add(Dense(1000, input_dim=x_train.shape[1], activation='relu'))
model.add(Dense(500, activation='relu'))
model.add(Dense(1))
model.add(BatchNormalization())
model.add(Activation('sigmoid'))
adam = optimizers.Adam(lr=5e-4)
model.checkpoint = ModelCheckpoint('best_29.h5', monitor = 'val_loss', verbose = 1, save_best_only = True, mode = 'min')
model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
# try:
# gradient_decent(x_train, y_train)
# except (KeyboardInterrupt):
# np.save('result/w_'+str(lr)+'_'+str(epoch)+'_'+str(batch)+'_l='+str(loss), w)
# np.save('result/b_'+str(lr)+'_'+str(epoch)+'_'+str(batch)+'_l='+str(loss), b)
# np.save('w',w)
# np.save('b',b)
# np.save('w_v',w_var)
# np.save('b_v',b_var)
