经过努力，我的问题是，
我有两个模型，两个模型都可以检测2-2类。正如我们所知，我们可以使用functionalapi合并两个模型。我试过了，但我没有得到想要的结果。
我的目标-：我想合并这些模型，更新后的模型应该有（1个输入，4个输出）。

inputs = tf.keras.Input(shape=(50,50,1))
y_1 = f1_Model(inputs)
y_2 = f2(inputs)
outputs = tf.concat([y_1, y_2], axis=0)
new_model = keras.Model(inputs, outputs)
new_model.summary()

Model: "functional_5"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_2 (InputLayer)            [(None, 50, 50, 1)]  0                                            
__________________________________________________________________________________________________
sequential (Sequential)         (None, 2)            203874      input_2[0][0]                    
__________________________________________________________________________________________________
sequential_1 (Sequential)       (None, 2)            203874      input_2[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_concat (TensorFlowO [(None, 2)]          0           sequential[1][0]                 
                                                                 sequential_1[1][0]               
==================================================================================================
Total params: 407,748
Trainable params: 407,748
Non-trainable params: 0
__________________________________________________________________________________________________

当我在其中传递图像时，它给出了错误的结果。我不知道我哪里出错了。

prediction = new_model.predict([prepare(img)]) 
prediction

# index_pred=np.argmax(prediction) (this should return from 0 to 3, but not happening)

array([[1., 0.],
       [1., 0.]], dtype=float32)

提前谢谢