Tensorflow keras.models.load_model（）给出错误；ValueError：方程“的输入为0”；baik，baij->；“bakj”；，期望2“；

我的代码将批量矩阵乘法“tf.einsum（'baik，baij->bakj'，q，k）/np.sqrt（dv）”作为它的一部分。训练完模型后，我使用“model.save（'./model'）保存它，现在我想加载保存的模型。我用“model=keras.models.load_model（'./model'，compile=False，custom_objects={'f1'：f1}）”的方式进行了尝试。但它给出了下面的错误。为什么会发生这种情况

Traceback (most recent call last):
  File "test_pro.py", line 37, in <module>
    model = keras.models.load_model('./model', compile=False, custom_objects={'f1': f1})
  File "/home/dcs2016csc007/.local/lib/python3.8/site-packages/tensorflow/python/keras/saving/save.py", line 212, in load_model
    return saved_model_load.load(filepath, compile, options)
  File "/home/dcs2016csc007/.local/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 147, in load
    keras_loader.finalize_objects()
  File "/home/dcs2016csc007/.local/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 612, in finalize_objects
    self._reconstruct_all_models()
  File "/home/dcs2016csc007/.local/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 631, in _reconstruct_all_models
    self._reconstruct_model(model_id, model, layers)
  File "/home/dcs2016csc007/.local/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 677, in _reconstruct_model
    created_layers) = functional_lib.reconstruct_from_config(
  File "/home/dcs2016csc007/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/functional.py", line 1285, in reconstruct_from_config
    process_node(layer, node_data)
  File "/home/dcs2016csc007/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/functional.py", line 1233, in process_node
    output_tensors = layer(input_tensors, **kwargs)
  File "/home/dcs2016csc007/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 1012, in __call__
    outputs = call_fn(inputs, *args, **kwargs)
  File "/home/dcs2016csc007/.local/lib/python3.8/site-packages/tensorflow/python/keras/layers/core.py", line 1327, in _call_wrapper
    return self._call_wrapper(*args, **kwargs)
  File "/home/dcs2016csc007/.local/lib/python3.8/site-packages/tensorflow/python/keras/layers/core.py", line 1359, in _call_wrapper
    result = self.function(*args, **kwargs)
  File "/home/dcs2016csc007/.local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py", line 201, in wrapper
    return target(*args, **kwargs)
  File "/home/dcs2016csc007/.local/lib/python3.8/site-packages/tensorflow/python/ops/special_math_ops.py", line 751, in einsum
    return _einsum_v2(equation, *inputs, **kwargs)
  File "/home/dcs2016csc007/.local/lib/python3.8/site-packages/tensorflow/python/ops/special_math_ops.py", line 1174, in _einsum_v2
    _einsum_v2_parse_and_resolve_equation(equation, input_shapes))
  File "/home/dcs2016csc007/.local/lib/python3.8/site-packages/tensorflow/python/ops/special_math_ops.py", line 1254, in _einsum_v2_parse_and_resolve_equation
    raise ValueError('Got {} inputs for equation "{}", expecting {}'.format(
ValueError: Got 0 inputs for equation "baik,baij->bakj", expecting 2

def MultiHeadsAttModel(l=7*7, d=1024 , dv=64, dout=1024, nv = 16 ):

v1 = Input(shape = (l, d))
q1 = Input(shape = (l, d))
k1 = Input(shape = (l, d))

v2 = Dense(dv*nv, activation = "relu")(v1)
q2 = Dense(dv*nv, activation = "relu")(q1)
k2 = Dense(dv*nv, activation = "relu")(k1)

v = Reshape([l, nv, dv])(v2)
q = Reshape([l, nv, dv])(q2)
k = Reshape([l, nv, dv])(k2)
att = tf.einsum('baik,baij->bakj',q, k)/np.sqrt(dv) #batch matrix multiplication
att = Lambda(lambda x:  K.softmax(x) , output_shape=(l, nv, nv))(att)
out = tf.einsum('bajk,baik->baji',att, v)
out = Reshape([l, d])(out)
out = Add()([out, q1])

out = Dense(dout, activation = "relu")(out)

return  Model(inputs=[q1,k1,v1], outputs=out) 



   
def create_model(input_shape, output_classes):
     mobile = tf.keras.applications.mobilenet.MobileNet(weights='imagenet')
     x = mobile.layers[-6].input
    
     if True:
        x = Reshape([7*7,1024])(x)
        att = MultiHeadsAttModel(l=7*7, d=1024 , dv=64, dout=1024, nv = 16 )
        x = att([x,x,x])
        x = Reshape([7,7,1024])(x)   
        x = BatchNormalization()(x)