from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dense, Dropout, Input, Conv1D, MaxPooling2D, Flatten, LSTM
import numpy as np
from sklearn.metrics import r2_score
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from tensorflow.keras.callbacks import EarlyStopping
import time
#1. 데이터
datasets= load_diabetes()
x = datasets.data
y = datasets.target
print(x.shape) #(442, 10)
x_train, x_test, y_train, y_test = train_test_split(x, y,
train_size=0.8,
random_state=221,
)
scaler=MinMaxScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)
# print(np.min(x_test), np.max(x_test))
print(x_train.shape, x_test.shape) #(16512, 8) (4128, 8)
x_train = x_train.reshape(-1, 5, 2)
x_test = x_test.reshape(-1, 5, 2)
#2. 모델 구성
input1 = Input(shape=(5, 2))
Conv1 = Conv1D(20, 2)(input1)
Conv2 = Conv1D(20, 2)(Conv1)
Flat1 = Flatten()(Conv2)
dense1 = Dense(10, activation='relu')(Flat1)
dense2 = Dense(10, activation='relu')(dense1)
dense3 = Dense(10, activation='relu')(dense2)
output1 = Dense(1)(dense3)
model=Model(inputs=input1, outputs=output1)
#3. 컴파일, 훈련
start_time=time.time()
model.compile(loss='mse', optimizer='adam')
es = EarlyStopping(monitor = 'loss',
patience=20,
restore_best_weights=True,
verbose=1)
hist = model.fit(x_train, y_train,
epochs =1000,
batch_size=64,
validation_split = 0.2,
callbacks=[es])
end_time=time.time()
#4. 평가, 예측
result = model.evaluate(x_test, y_test)
print('result :', result)
y_pred=model.predict(x_test)
r2 = r2_score(y_test, y_pred)
print('r2 :', r2)
# result : 3722.1572265625
# r2 : 0.38330340623672166