Flask and Keras model Error ''_thread._local' object has no attribute 'value''?

Question

I am using the following: python 3.6.4

Flask = 1.1.1,

Keras = 2.3.0,

TensorFlow = 1.14.0, I have a Flask server that gets pictures from the clients. using Keras model with a TensorFlow back-end I try to get a prediction from a pre-trained model.

I am using the following function to upload the model( as part of a class)

 model_path = self.conf["model_path"] // path in conf to model
 self.model = load_model(model_path)  // uploading the model
 self.model._make_predict_function()
 p_log.info("model had been upload successfully ")

and I use the following line for prediction:

cm_prediction = self.model.predict([face, reye, leye, fg])[0]

Until today I didn't have any problem, always got a prediction. now I get the following error:

Traceback (most recent call last):
  File "D:\code_project\path to project", line 75, in predict
    cm_prediction = self.model.predict([face, reye, leye, fg])[0]
  File "D:\code_project\path to project", line 1462, in predict
    callbacks=callbacks)
  File "D:\code_project\predictserver\venv\lib\site-packages\keras\engine\training_arrays.py", line 276, in predict_loop
    callbacks.model.stop_training = False
  File "D:\code_project\predictserver\venv\lib\site-packages\keras\engine\network.py", line 323, in __setattr__
    super(Network, self).__setattr__(name, value)
  File "D:\code_project\predictserver\venv\lib\site-packages\keras\engine\base_layer.py", line 1215, in __setattr__
    if not _DISABLE_TRACKING.value:
AttributeError: '_thread._local' object has no attribute 'value'

I have a simple Flask server running:

if __name__ == '__main__':
    pre = predictor()
    # app.run(debug=True)
    app.run(host='0.0.0.0', port=12345)

The model is always being uploaded.

If I am running the program without the Flask server, hence giving manually input, I get a prediction, but as soon as the server is on the error appears and I stop getting a predictions

I tried to look on the web for some similar problem but didnt found any, if someone knows what the problem and how to solve it, I will appreciate sharing it.

Answer 1

因此，经过漫长的夜晚，Keras在19年9月17日发布了新版本2.3.0。作为修订版本的一部分，我更新了所有库，其中包括Keras。自从我做到了，消息就出现了。

在我降级到Keras 2.2.5之后，问题消失了。

Answer 2

如果它仍然相关，我只需更改即可解决此问题

from keras.models import Sequential

from keras.layers import Dense, Dropout, LSTM

到

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM

因此，无需关闭多线程。