Is there a way to restore the best model to a normal keras model?

[ghost]

After evaluating the best model, is there a way to get it back to a normal keras model? I tried importing the files from Restore() but that h5 file is not a complete one, so it failed.

[ErikVini]

My guess (I'm not related to the development of Talos) is that you can recover the Keras model from the Talos one doing this:

First, you get the index of the best model (given by Talos) using:

t = ta.Scan(...)
best_idx = t.best_model(metric='loss', asc=True) # Change the configurations accordingly to your case

Then transform it back to a Keras model using:

from talos.utils.best_model import activate_model
Talos_Model = activate_model(t, best_idx)

This way Talos_Model will be the Keras model corresponding to the best one trained by Talos and you can do whatever you want with it (I use Talos_Model.predict(X) to make my predictions).

You can also change the best_idx to any model that Talos has trained, in case you want to check the other trained models.

[ghost]

Thank you! I get how I would retrieve the best model, however when I try to do so I get the following error:

best_idx = t.best_model(metric='mean_absolute_error', asc=True)

Talos_Model = activate_model(t, best_idx)

Traceback (most recent call last):
  File "/home/emiel/.local/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3296, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-13-2e176e3c01dd>", line 3, in <module>
    Talos_Model = activate_model(t, best_idx)
  File "/home/emiel/.local/lib/python3.7/site-packages/talos/utils/best_model.py", line 20, in activate_model
    model = model_from_json(self.saved_models[model_id])
TypeError: list indices must be integers or slices, not Sequential

Did you encounter this as well? Let me know if you need more information!

[ErikVini]

The error you get means that best_idx is actually the model, not the index of it. My mistake, sorry. So you can just do

best_model = t.best_model(metric='mean_absolute_error', asc=True)
best_model.predict(X)

If you want to check other models, you can do the following:

# Make a report on the scan
r = ta.Reporting(t)
# Show the models
display(r.data)

This will output a Pandas DataFrame with all the models and the metrics, so you can choose one of them and use the previous code:

Talos_Model = activate_model(t, best_idx)

And predict normally. An advantage of r.data being a Pandas DataFrame is that you can do operations with it, for example:

r.data.sort_values('loss')

This will sort the results table by loss, in ascending order, very useful if you want to compare models.

Edit: I forgot to mention another cool tip: you can get the index of the model that satisfies a condition like so:

column = 'loss'
display(r.data.loc[r.data[column] == np.min(r.data[column])])

In this case it will print the model with the lowest loss. You can change the np.min to np.max to get the opposite. To get the index only, simply do:

r.data.loc[r.data[column] == np.min(r.data[column])].index.values[0]