Our next step is to extract the features. We will be using the Inception V3 network for this. Because the total number of images is relatively large, I will be using a GPU instance. You can try using a CPU, but it will take a longer to complete.
We will start by instantiating the model in the GPU context:
nnet = mx.load_checkpoint("weights/inception-v3/InceptionV3-FE", 0, mx.FeedForward; context = mx.gpu());
We will continue by creating a variable that will store features for the whole dataset. From the previous sections, you should remember that Inception V3 returns 2048 neurons or attributes per image:
features = zeros(2048, length(images))
Now, we can iterate over images and populate the features array. Because running ...
