In the graphing code for the Complex Model there is

`np.argmax(tf.nn.softmax(model.predict(Xl)))`

isn’t that equivalent to

`np.argmax(model.predict(Xl))`

since softmax doesn’t reorder its input?

you are right, softmax produces the probabilities of `Xl`

belonging to each of the classes, in the order of classes. `np.argmax`

returns the most probable class numbers.

Yes, that’s correct. Because softmax is a monotonic function, *argmax* will give you the same answer either with the “logits” or the softmax outputs as the input to *argmax*. The point is that they almost always define TF models with linear outputs (logits) at the output layer and then use *from_logits = True* mode in the cost function (the appropriate version of cross entropy), so that the activation calculation is done internally by the cost function.

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