More on SageTex here
- Playing around with something to indicate the linear fit to the data. Trying P value
- Updated UI code so that the P value will display on the next build
- Hopefully we try the world map code today?
- More TF Dev Book.
- I can now draw a TF embedding! It’s based on this tensorflow.org embedding tutorial
- Learning more about multiple inputs to embedding and had to get the keras.utils.plot_model working, which failed with this error: ImportError: Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work. So I pip installed both, and had the same problem.
- Stack overflow to the rescue: “dot.exe” not found in path. Pydot on Python (Windows 7)
- You have to install the graphviz executable first
- Then you have to add the path to the executables. In my case, that’s C:\Program Files (x86)\Graphviz2.38\bin
- It still didn’t work in the IDE, but that’s because I need to restart it. Verified that it did work in a new cmd window.
- Had problems running the distribution samples. Upgraded tf to version 2.1. No problems and better performance
- Finished chapter 2
- Struggled with picture placement. Moving on.
- Finished first pass. I need to add more ABM text, but I’m down to 10 pages plus references!
- Here’s a good use case for the functional API: models with multiple inputs and outputs. The functional API makes it easy to manipulate a large number of intertwined datastreams. Let’s consider the following model. We seek to predict how many retweets and likes a news headline will receive on Twitter. The main input to the model will be the headline itself, as a sequence of words, but to spice things up, our model will also have an auxiliary input, receiving extra data such as the time of day when the headline was posted, etc. The model will also be supervised via two loss functions. Using the main loss function earlier in a model is a good regularization mechanism for deep models.