When training the bert model, the weights are saved well, but the entire model is not saved.
After model.fit,
save model as model.save_weights('bert_xxx.h5') and load_weights works fine,
but since only weights are saved, the model frame must be loaded separately.
So I want to save the entire model at once.
However, the following error occurs.
The tensorflow version was 2.4, and the bert code used https://qiita.com/namakemono/items/4c779c9898028fc36ff3
Why is only the weights saved and not the entire model?
And how can I save the whole model??
Related
I have been training several models using 10-fold CV and added the ModelCheckpoint callback which saves the model with the lowest validation loss to an HDF5 file. However, for a while I would then call model.save(filepath) right after training.
I only came to the realization that the last call would probably save the model trained on the very last epoch and that the saved checkpoint is not being used at all. Is my assumption correct? If so, is it normal that the best models from the checkpoint files score lower than the ones saved with model.save()?
I am using a custom keras model in Databricks environment.
For a custom keras model, model.save(model.h5) does not work, because custom model is not serializable. Instead it is recommended to use model.save_weights(path) as an alternate.
model.save_weights(pathDirectory) works. This yields 3 files checkpoint,.data-00000-of-00001,.index in the pathDirectory
For loading weights, Following mechanism is working fine.
model = Model()
model.load_weights(path)
But I want to train my model on pretrained weights I just saved. Like I saved model weights, and continue training on these saved weights afterwards.
So, when I load model weights and apply training loop, I get this error, TypeError: 'CheckpointLoadStatus' object is not callable
After much research, I have found a workaround,
we can also save model using
model.save("model.hpy5") and read it the saved model in databricks.
model.h5 not work for customized models, but it works for standard models.
I have a saved trained model file in Keras which was saved using model.save(). Is it possible to load only the weights of the model from that file? Because I am getting an error when I try to load the model as a whole.
Using Tensorflow (tf.contrib.slim in particular) we are required to calibrate a few parameters to produce the graphs that we want at tensorboard.
Saving a summary interval is more clear for us what it does. It saves the value (or an average of them?) of a particular point in the graph at the interval provided.
Now checkpoints for saving the model itself why should be required at the training process? Does the model changes?.. Not sure how this works
You save the model to checkpoints because the Variables in the model, including neural network weights and biases and the global_step counter, keep changing during the training process. The structure of the model doesn't change. The saved checkpoints allow you to load the trained model for serving and to resume training later.
I want to train my own Word2Vec model for my text corpus. I can get the code from TensorFlow's tutorial. What I don't know is how to save this model to use for CNN text classification later? Should I use pickle to save it and then read it later?
No pickling is not the way of saving the model in case of tensorflow.
Tensorflow provides with tensorflow serving for saving the models as proto bufs(for exporting the model). The way to save model would be to save the tensorflow session as:
saver.save(sess, 'my_test_model',global_step=1000)
Heres the link for complete answer:
Tensorflow: how to save/restore a model?
You can use pickle to save it to disk. Then when you are creating the CNN model, load the saved word embedding table and use it to initialize the TensorFlow variable that holds the word embeddings for your CNN classifier.