keras.basic(예측)

import numpy as np
import keras

 

from keras.models import Sequential
from keras.layers import Dense

 

x=np.array([1,2,3])
y=np.array([3,5,9])

model =Sequential()
model.add(Dense(1,input_shape=(1,),activation='linear'))
# 1 : 중간층 없음
# input_shape=(1,) : 속성 갯수
# activation='linear') : 활성 함수

 

#학습률 지정안함
#가중치 초기화 안함
model.compile(loss='mse',optimizer='sgd')
# loss='mse' : 비용함수
# optimizer='sgd' : 옵티마이져
model.fit(x.reshape(-1,1),y,epochs=1000)
# x.reshape(-1,1),y : 훈련 data
# epochs=1000 : 반복 횟수(에포크)

 

# 예측
model.predict(x.reshape(-1,1))

array([[2.7419074],
       [5.682813 ],
       [8.623719 ]], dtype=float32)

 

import matplotlib.pyplot as plt
plt.scatter(x,y)
plt.plot(x,model.predict(x.reshape(-1,1)).ravel(),'r:')

ws = model.get_weights() # 계산 된 가중치 값을 가져온다.(w,b)
ws

[array([[2.9409058]], dtype=float32), array([-0.19899847], dtype=float32)]

w=ws[0][0,0]
b=ws[1][0]
w,b

(2.9409058, -0.19899847)

 

h=model.history.history
plt.plot(h['loss'])
plt.ylim([0,1])