How to save Scikit-Learn-Keras Model into a Persistence File (pickle/hd5/json/yaml)

Question

I have the following code, using Keras Scikit-Learn Wrapper:

from keras.models import Sequential
from sklearn import datasets
from keras.layers import Dense
         


        

Answer 1

The accepted answer is too complicated. You can fully save and restore every aspect of your model in a .h5 file. Straight from the Keras FAQ:

You can use model.save(filepath) to save a Keras model into a single HDF5 file which will contain:

• the architecture of the model, allowing to re-create the model
• the weights of the model
• the training configuration (loss, optimizer)
• the state of the optimizer, allowing to resume training exactly where you left off.

You can then use keras.models.load_model(filepath) to reinstantiate your model. load_model will also take care of compiling the model using the saved training configuration (unless the model was never compiled in the first place).

And the corresponding code:

from keras.models import load_model

model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'
del model  # deletes the existing model

# returns a compiled model identical to the previous one
model = load_model('my_model.h5')