问Keras符号输入/输出不实现__len__错误

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TypeError                                 Traceback (most recent call last)
<ipython-input-352-765c5782fe72> in <module>()
      1 model=Model(inputs=input_layer,outputs=output)
----> 2 model.compile(optimizer='adam',loss=-RewardFn,metrics=['acc'])
      3 model.summary()

1 frames
/usr/local/lib/python3.7/dist-packages/keras/engine/keras_tensor.py in __len__(self)
    219 
    220   def __len__(self):
--> 221     raise TypeError('Keras symbolic inputs/outputs do not '
    222                     'implement `__len__`. You may be '
    223                     'trying to pass Keras symbolic inputs/outputs '

TypeError: Keras symbolic inputs/outputs do not implement `__len__`. You may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model. This error will also get raised if you try asserting a symbolic input/output directly.

!pip install keras-rl2
import pandas as pd
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from google.colab import files
import io
# %matplotlib inline
import seaborn as sns

sns.set(style='darkgrid')
uploaded=files.upload()
cols=['node1x','node2x','node3x','node4x','node1y','node2y','node3y','node4y','Rmin']
Dataset=pd.read_csv(io.StringIO(uploaded['DNNsamples.csv'].decode('utf-8')),names=cols,header=None)

Dataset.head(20)

from sklearn.model_selection import train_test_split
X_train,X_test=train_test_split(Dataset,test_size=0.2,random_state=42)

from tensorflow.keras.layers import Input,Dense,Activation,Dropout,Flatten
from tensorflow.keras.models import Model
------

input_layer=Input(shape=(Dataset.shape[1],))
dense_layer1=Dense(21,activation='relu')(input_layer)
dense_layer2=Dense(21,activation='relu')(dense_layer1)
dense_layer3=Dense(21,activation='relu')(dense_layer2)
dense_layer4=Dense(21,activation='relu')(dense_layer3)
dense_layer5=Dense(21,activation='relu')(dense_layer4)
dense_layer6=Dense(21,activation='relu')(dense_layer5)
output=Dense(outputss,activation='sigmoid')(dense_layer6)
-----
RewardFn=Ravg+Constraint1+Constraint2+Constraint3+Constraint4+Constraint5
tf.shape(RewardFn)

model=Model(inputs=input_layer,outputs=output)
model.compile(loss=-RewardFn,optimizer='adam',metrics=['acc'])
model.summary()