可以分别使用model.evaluate和mean_absolute_error计算出不同的MAE值。model.evaluate返回的是模型在测试集上的平均误差，而mean_absolute_error是直接计算预测值与真实值之间的平均绝对误差。示例如下：

from keras.models import Sequential
from keras.layers import Dense
from sklearn.metrics import mean_absolute_error
import numpy as np

# 生成训练集和测试集
x_train = np.random.rand(1000, 10)
y_train = np.random.rand(1000, 1)
x_test = np.random.rand(100, 10)
y_test = np.random.rand(100, 1)

# 构建模型
model = Sequential()
model.add(Dense(64, input_dim=10, activation='relu'))
model.add(Dense(1, activation='linear'))
model.compile(optimizer='adam', loss='mse', metrics=['mae'])

# 训练模型
history = model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))

# 使用model.evaluate计算MAE
scores1 = model.evaluate(x_test, y_test, verbose=0)
print('MAE using model.evaluate:', scores1[1])

# 使用mean_absolute_error计算MAE
y_pred = model.predict(x_test)
scores2 = mean_absolute_error(y_test, y_pred)
print('MAE using mean_absolute_error:', scores2)