Keras Model.save() Raise Notimplementederror

I have tried the keras nmt code in the following link:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_atte

Solution 1:

I had a similar problem with the current tf version (1.11). I used the tf.keras API to define my model and trained it without problems. When I wanted to save my model using tensorflow.keras.models.save_model or model.save()(which just calls save_model) I got the following exception:

NotImplementedError: __deepcopy__() is only available when eager execution is enabled.

So I called tf.enable_eager_execution(), but because of the usage of a Lambda Layer in my architecture, I ended up with another NotImplementedError of "compute_output_shape".. If your architecture does not contain a Lambda Layer the enabling of eager_execution could fix your problem in tf 1.11.

My final "way to go" was to use model.save_weights('model_weights.h5') because I did not need to save the model architecture just the trained weights. Btw.: in my case it was also possible to switch from tensorflow.keras.* imports to keras.* and use just "plain" keras with tf backend (model.save() works here - of course).

Solution 2:

Consider using tf.keras.models.save_model() and load_model(), it may work.

Solution 3:

model.save('model.h5py') may solve the problem. The key is to save as h5py file.