#ReduceLRonPlateau : 개선이 없으면 Learningrate 반으로 줄인다.
import numpy as np
from sklearn.datasets import fetch_california_housing, fetch_covtype
from sklearn.model_selection import train_test_split as tts
#1. 데이터
datasets = fetch_covtype()
x = datasets.data
y = datasets.target
x_train, x_test, y_train, y_test = tts(x,y,
train_size=0.8,
shuffle=True,
random_state=337,
)
#2. 모델
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
model = Sequential()
model.add(Dense(64, input_dim=8))
model.add(Dense(32))
model.add(Dense(32))
model.add(Dense(32))
model.add(Dense(1))
#3. 컴파일, 훈련
from tensorflow.keras.optimizers import Adam
learning_rate = 0.01
optimizer = Adam(learning_rate=learning_rate,)
model.compile(loss='mse', optimizer=optimizer)
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
es = EarlyStopping(monitor = 'val_loss',
patience = 20, mode = 'min',
verbose = 1)
rlr = ReduceLROnPlateau(monitor = 'val_loss',
patience = 2,
mode='auto',
verbose = 1,
factor=0.5)
model.fit(x_train, y_train,
epochs = 200,
batch_size = 32,
validation_split=0.2,
verbose=1,
callbacks=[es, rlr])
#4. 평가, 예측
results = model.evaluate(x_test, y_test)
print("loss :", results)
# loss : 0.650985836982727