NotImplementedError: Cannot convert a symbolic Tensor to a numpy array


The code below used to work last year, but updates in keras/tensorflow/numpy broke it. It now outputs the exception below. Does anyone know how to make it work again?

I’m using:

  • 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)

Complete output of the script above:

Shortened output:

2021-02-10 17:51:13.037468: I tensorflow/compiler/jit/] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-02-10 17:51:13.037899: I tensorflow/core/platform/] 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/] 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/./", 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/", 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


Solution: Use Python 3.8, because Python 3.9 is not supported by Tensorflow.

Answered By – Volker Weißmann

This Answer collected from stackoverflow, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0

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