toy example of plotting a fully connected layer as presented in the blog post Learning when to skim and when to read

• Python 3
• sklearn
• numpy
• pandas
• bokeh

the script innards.py expects a pandas dataframe similar to the one found in metrics.pkl. which could have been created from something like:

def save_info(x_dev, y_dev, y_net, prob_net, layer, path_): '''  save test set info into a pandas dataframe and pickle it  x_dev: sentences (list of strings)  y_dev: sentiment label (list of ints) ex. 0 negative, 1 positive  y_net: network output (list of ints) ex. 0 negative, 1 positive  prob_net: networks probabilities/y  layer: fully connected layer list of a vector list per sentence  path_: path to save dataframe  ''' d = {'x_dev': x_dev, 'y_dev': y_dev, 'layer': layer, 'y_net': y_net, 'prob_net': prob_net} df = pd.DataFrame(data=d) df.to_pickle(path_)

you can also check the jupyter notebook where a version of an LSTM fully connected layer from SST is plotted. So far the two plots of the paragraph Exploring the innards are only implemented

Usising the script innards_finegrained.py one can plot in space more than two classes. An example dataset exists at metrics_f.pkl and the program's output at plot_finegrained.html.