keras basics3

# 1. 데이터 전처리
from keras.models import Sequential
from keras.layers import Dense
import numpy as np

x = np.linspace(1, 10, 10)
y = x

# 2. 모델 구성
model = Sequential()
model.add(Dense(10, input_dim=1, activation='linear'))
model.add(Dense(10, activation='linear'))
model.add(Dense(8, activation='linear'))
model.add(Dense(1))
 
# 3. 모델 훈련
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
model.fit(x, y, epochs=100, verbose=0)
 
# 4. 모델 평가 예측
loss_met = model.evaluate(x, y, batch_size=1)
print(loss_met)
 
predict = model.predict(x)
print('y', y, ' predict: \n', predict)

# RMSE 구하기
from sklearn.metrics import mean_squared_error
def RMSE(y_test, y_predict):
    return np.sqrt(mean_squared_error(y_test, y_predict))
print('RMSE : ', RMSE(y, predict))

# R2 구하기
from sklearn.metrics import r2_score
r2_predict = r2_score(y, predict)
print('R2 : ', r2_predict)

# 그래프 그리기
import matplotlib.pyplot as plt
plt.plot(x, predict, 'b', x, y, 'k.')
plt.legend(['predict', 'y'])

import matplotlib.pyplot as plt
plt.plot(x, predict, 'b', x, y, 'k.')
plt.legend(['predict', 'y'])