In this section, we'll carefully look at the model we built. First, we'll verify the overall accuracy of our model, then we'll see where the model goes wrong. Finally, we'll visualize the weights associated with several neurons to see what they're looking for:
plt.figure(figsize=(6, 6)) plt.plot(train_acc,'bo') plt.plot(test_acc,'rx')
Make sure that you've trained your model as we did in the previous section, if not, you might want to stop here and do that first. Because we evaluated our model accuracy every 10 training epochs and saved the result, it's now easy to explore how our model has evolved.
Using Matplotlib, we can plot both the training accuracy (the blue dots) and testing accuracy (the red dots) on the same figure:
Again, if you don't have Matplotlib, that's okay. You can just look at the array values themselves. Note that the training accuracy (blue in color) is usually a little better than the testing accuracy (red in color). This isn't surprising,...
