Parameters & Cost log per iteration

Hello, at the moment Im doing course C1 week2 Lab03 Feature Scaling and Learning Rate.
I found super useful to run the gradient descent function and observe how values change for the Cost function and for the parameters w in each iteration. This historical information per each iteration I believe provide insightful information about if gradient descent is converging: if Cost is close to 0 and if parameters Ws values are not changing from previous iteration.

My question is: is possible to get above historical info from sklearn model SGDRegressor? How I can get from SGDRegressor Ws and Cost values per each interaction?
Many thanks

I do not think sklearn provides such an interface. (as far as I know…)

You can use SGD in Tensorflow, and get history from model.fit for visualization as you wrote.

Tensorflow provides more useful tool, Tensorboard.
You can easily pass logs to Tensorboard for visualization and analysis. Here is an example.

thanks Nobu I will check Tensorboard sound a good tool