Cannot convert a symbolic Tensor (simple_rnn/strided_slice:0) to a numpy array. 报错 （解决方法）_call initializer instance with the dtype argument

1.报错 问题：（来自《Python深度学习》P164-165）

在运行以下代码的时候，报错：Cannot convert a symbolic Tensor (simple_rnn/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported.

# 只返回最后一个时间步的输出
 
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, SimpleRNN
 
model = Sequential()
model.add(Embedding(10000, 32))
model.add(SimpleRNN(32))
model.summary()

2. 解决方法：

查了一下，当时用的numpy版本：

疑问：会不会是 numpy版本 太高了导致的？

于是，正好有个低版本的虚拟环境，其numpy版本为：

重新运行了上述代码，发现有Warning，但无伤大雅，可以运行： 

WARNING:tensorflow:From D:\QLDownload\AnacondaDon\envs\python37\lib\site-packages\tensorflow\python\keras\initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From D:\QLDownload\AnacondaDon\envs\python37\lib\site-packages\tensorflow\python\ops\init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding (Embedding)        (None, None, 32)          320000    
_________________________________________________________________
simple_rnn (SimpleRNN)       (None, 32)                2080      
=================================================================
Total params: 322,080
Trainable params: 322,080
Non-trainable params: 0
_________________________________________________________________

 BTW，虚拟环境的其他配置为：

scipy==1.3.0
Keras==2.2.4
numpy==1.16.4
networkx==2.1
tensorflow_gpu==1.14.0
scikit_learn==0.21.2
tensorflow==1.14.0