| Fontastic Nischal HP | @nischalhp | VP, Engineering, omni:us Raghotham S | @raghothams | Senior Data Scientist, Ericsson Research Strata Data Conference 2019, London Strata Data Conference 2019, London
Pass 3: Generate Image using PIL Steps Steps 1. Create 4 set of random texts 2. Generate 4k resolution image using the TTF for every random text 3. Take 10 random crop of size 256x256 px from the 4k image With this we have the ability to generate large number of training images
import pickle from ipywidgets import interact, interactive, fixed, interact_manual import ipywidgets as widgets import matplotlib.pyplot as plt with open('./fd727d3f-73f4-4ec6-8e89-3e15fd3801b0resnet50_grad_cam', 'rb') as f: data = pickle.load(f) def show_cam(epoch_slider, image_slider, layer_slider): plt.imshow(data[epoch_slider][image_slider][layer_slider])
not only reveal visual abstractions within a model, but they can reveal high-level misunderstandings in a model that can be exploited. For example, by looking at an activation atlas we will be able to see why a picture of a baseball can switch the classi cation of an image from “grey whale” to “great white shark”.