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With this we can make a function which will generate random training data for us on the fly.

Let’s start by training the generator. This consists of:

Generator

50 lines of code simple.

The discriminator is trying to learn to distinguish real data from “fake” generated data. The labels while training the discriminator need to represent that, i.e. one when our data comes from the real data set and zero when it is generated by our generator. We pass in those two batches in steps (1) and (2) above and then average the loss from the two batches. It’s important to note that when passing in the generated data we want to detach the gradients. We do this because we are not training the generator we are just focused on the discriminator. Once all of that is done we backpropagate the gradients in only the discriminator and we are done.

50 lines of real Python code!

itext-paulo (lowagie.com)[JDK1.1] – build 132

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Acrobat Distiller 10.0.1 (Windows)

Acrobat Distiller 10.0.1 (Windows)

Acrobat Distiller 10.0.1 (Windows)

application/pdf Effect of cooking and preservation on nutritional and phytochemical composition of the mushroom Amanita zambiana

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