[[my back prop SGD from scratch 2022-Aug]] 13:35 yea so last time I had noticed , hey on a random initialization why was the y_prob 0.5 ? I had literally just initialized a new network and got this first example, ipdb> p x, y (array([10.31816265, -8.80044688]), 1) while running the ipdb debug mode, and inside of train_network() , ran --> 186 y_prob = feed_forward(x, model.layers, verbose=False) and got ipdb> p y_prob 0....

[[my back prop SGD from scratch 2022-Aug]] 16:38 why no learning going on hmm look at this network import network as n import dataset import plot X, Y = dataset.build_dataset_inside_outside_circle(0.5) model = n.initialize_model({"learning_rate": 0.01}) ( loss_vec, model, artifacts, X_validation, Y_validation, Y_prob ) = n.train_network(X, Y, model) 17:02 wondering if I can inspect the gradient, to see if it is pointing where it should 23:02 so for a random weight initialized network, curious at least here the response should be nonlinear right?...

[[my back prop SGD from scratch 2022-Aug]] 12:38 so what happened last time? well let me look at the 2022-08-21.html notes I created. 13:20 darn so ok spent bunch of time figuring out why I couldnt view all the images in that html but basically combination of the html references images in log seq dir and also I have to copy them to my git repo area for this repo....

hmm blah blah test something #publish-this What So I had a particular need to load a #[[scikit learn sklearn]] model trained with 0.20.4 on Databricks 10.4 and ran into some fun details I was not initially able to use the Databricks 10.4 pinned version of scikit-learn , 0.24.x because of course there were #backwards-compatibility issues where trying to load the model gave me an error saying that ModuleNotFoundError: No module named 'sklearn....

will this work? when blog vimeo 2022-04-10 foo yea – – – hope so