numpy - 未实现错误 : Cannot convert a symbolic Tensor to a numpy array

Question

下面的代码去年还可以使用，但 keras/tensorflow/numpy 中的更新破坏了它。它现在输出下面的异常。有谁知道如何让它再次工作？
我正在使用:

Tensorflow 2.4.1

Keras 2.4.3

Numpy 1.20.1

Python 3.9.1

import numpy as np
from keras.layers import LSTM, Embedding, Input, Bidirectional

dim = 30
max_seq_length = 40
vecs = np.random.rand(45,dim)

input_layer = Input(shape=(max_seq_length,))
embedding_layer = Embedding(len(vecs), dim, weights=[vecs], input_length=max_seq_length, trainable=False, name="layerA")(input_layer)
lstm_nobi = LSTM(max_seq_length, return_sequences=True, activation="linear", name="layerB")
lstm = Bidirectional(lstm_nobi, name="layerC")(embedding_layer)

上面脚本的完整输出: https://pastebin.com/DsQNWVwz
缩短输出:

2021-02-10 17:51:13.037468: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-02-10 17:51:13.037899: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-02-10 17:51:13.038418: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
Traceback (most recent call last):
  File "/run/media/volker/DATA/configruns/load/./test.py", line 13, in <module>
    lstm = Bidirectional(lstm_nobi, name="layerC")(embedding_layer)
  ... omitted, see pastebin ...
  File "/usr/lib/python3.9/site-packages/tensorflow/python/framework/ops.py", line 852, in __array__
    raise NotImplementedError(
NotImplementedError: Cannot convert a symbolic Tensor (layerC/forward_layerB/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

Answer 1

解决方案:使用 Python 3.8，因为 Tensorflow 不支持 Python 3.9。