For use on https://austingwalters.com/hyperparameter-tuning-for-sentence-classification/

save_model_to_disk.py

if save_model_flag:
  
  # Add optimization method, loss function and optimization value
  model.compile(loss='categorical_crossentropy',
                optimizer='adam', metrics=['accuracy'])

  # "Fit the model" (train model), using training data (80% of datset)
  model.fit(x_train, y_train, batch_size=batch_size,
            epochs=epochs, validation_data=(x_test, y_test))

  # Save model to json file
  model_json = model.to_json()
  with open(model_name + ".json", "w") as json_file:
      json_file.write(model_json)

  # serialize weights to HDF5                                                               
  model.save_weights(model_name + ".h5")

else:

    # load json and create model                                                              
    json_file = open(model_name + '.json', 'r')
    loaded_model_json = json_file.read()
    json_file.close()
    model = model_from_json(loaded_model_json)

    # load weights into new model                                                             
    model.load_weights(model_name + ".h5")

    # evaluate loaded model on test data                                                      
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam', metrics=['accuracy'])
    
# Evaluate the trained model, using the test data (20% of the dataset)
score = model.evaluate(x_test, y_test, batch_size=batch_size)