Keras 符号输入/输出未实现 __len__ 错误

Question

我想构建一个 AI 来解决给定环境中的优化问题，但出现以下错误

---------------------------------------------------------------------------
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.

我发现了这个错误，据说是 tensorflow 的问题。 但我不知道如何解决它。 这是我的 model

!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()

在损失 function 中使用输入和 output 值会不会有问题？ 我使用谷歌 Colab。

Answer 1

您无需单独安装Keras package。 您可以从TensorFlow Keras 另外，请在导入输入时提供正确的别名，如下所示。 Input is submodule of tf.keras API, not part of tensorflow.keras.layers API.

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

请检查tensorflow和keras版本应符合此测试的构建配置。 让我们知道问题是否仍然存在。