In addition to supporting a handful of built-in models, skflow allows you to supply a custom graph, typically capped off with one of the common models (like a fully connected layer). The example shows how this actually works with a convolutional neural net. There’s just one problem: weights are no longer accessible. I struggled through this in the first video last week.

But perseverance paid off! This week I studied the log file normally used for TensorBoard. Running strings on that file, I extracted the necessary incantation to access all of the weights. And I submitted a Pull Request to show other users how this is done.

classifier.get_tensor_value('conv_layer1/convolution/filters:0')

Here’s the triumph in action: