day2-01-regressi

import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

train_x=np.arange(-20,20,0.25)
train_y=np.sqrt((2*train_x**2)+1)

model = Sequential()

model.add(Dense(8,input_dim=1,activation='relu'))  # input hidden ke input
model.add(Dense(4, activation = 'relu'))   # dim layer
model.add(Dense(1, activation = 'linear')) # ouput layer

model.compile (loss = 'mean_squared_error', optimizer = 'adam')
model.fit(train_x,train_y,batch_size=20, epochs=10000,verbose=2)

print(model.summary())
model.save("model.h5")

# test kasus
x=np.array([26])
predict= model.predict(x)
print("f(26) =", predict )