在这个简单的XOR问题中连接keras中的输入和输出

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

# 定义模型
model = Sequential()
model.add(Dense(2, input_dim=2, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

from keras.models import Model
from keras.layers import Input, Dense

# 定义模型
inputs = Input(shape=(2,))
x = Dense(2, activation='relu')(inputs)
outputs = Dense(1, activation='sigmoid')(x)
model = Model(inputs=inputs, outputs=outputs)

# 编译模型
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, epochs=100, batch_size=1)

# 评估模型
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])