Chapter 8 examples, why do they all turn label into one_hot_labels about grokking-deep-learning HOT 3 OPEN

It converts an int into an array of 0's and one 1, so number "2" will be converted to [0,0,1,0,0,0,0,0,0,0].

The final prediction is of same type an array of size 10, so for each digit you get its own prediction.

Hopefully this helps.

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@marshallxxx 3 weeks later I still feel uncomfortable with that code , particularly this part,

for i in range(len(images)):
    layer_0 = images[i:i+1] # why not change to layer_0 = images[i]
    layer_1 = relu(np.dot(layer_0,weights_0_1))
    layer_2 = np.dot(layer_1,weights_1_2)
    error += np.sum((labels[i:i+1] - layer_2) ** 2)  # then we use labels[i]
    ...

If we change to layer_0 = images[i], i.e. from 2-D array to 1-D array, then we need less matrix transposition operations and implementation seems more easy to understand.

So what is your opinion of using layer_0 = images[i:i+1] there ?

for i in range(len(images)):
    layer_0 = images[i]
    layer_1 = relu(np.dot(layer_0,weights_0_1))
    layer_2 = np.dot(layer_1,weights_1_2)
    error += np.sum((labels[i] - layer_2) ** 2)
    ...

Thanks

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