plot_weights hook: plot unlimited weights; access weights directly from Executor.model not external files

Now, I have managed to plot the convolutional weights by the following kurfile:

hooks:
    - plot_weights:
        weight_file: cifar.best.valid.w
        weight_keywords1: ["convolution.0", "kernel"]
        weight_keywords2: ["convolution.1", "kernel"]

In my plot_weights_hook.py, I get weight_keywords1, weight_keywords2 into hooks through __init__():

def __init__(self, weight_file, weight_keywords1, weight_keywords2, *args, **kwargs):
		""" Creates a new plotting hook, get plot filenames and matplotlib ready.
		"""

my question:

if I want to plot more convolutional weights, say weight_keywords3, weight_keywords4, weight_keywords5, do I have to change the source code, by adding them into __init__ like above?

Can **kwargs somehow help me avoid changing source every time I want to plot more weights? If so, how?

Thanks!

[ajsyp]

You can do it two ways. First, yes, you could use **kwargs to do it, but then you need to be careful to make sure that you pass through the correct pieces of kwargs to the base class constructor (there are lots of ways you can envision this, but all are harder to maintain or are fragile). A better way to do it would be to simply add another layer of indirection:

hooks:
  - plot_weights
      weight_file: cifar.best.valid.w
      with_weights:
        - ['convolution.0', 'kernel']
        - ['convolution.1', 'kernel']
        - ['convolution.2', 'kernel']
        - ...

And then your constructor signature looks like this: def init(self, weight_file, with_weights, *args, **kwargs)

[ajsyp]

That’s it: kur.model.Model.save(). But it only calls keras_backend._save_keras() IF the Keras backend is selected. If the PyTorch backend is in use, then it will use pytorch_backend.save() instead. That’s the reason for using inheritance (base/derived classes), and the whole point of designing an API.