When you're using tf.keras in TensorFlow 2.0, training your models with `fit` is one option, but you can also write a custom training loop. It takes about 10 lines.pic.twitter.com/Tn3NA4pZaf
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If your model creates additional losses during its forward pass (i.e. if you called `self.add_loss` in a custom layer), collect them in one line like this.pic.twitter.com/tuvBdibMAP
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The advantage of using `fit` is that it packs a lot of functionality in a convenient interface: a progress bar, easy reporting of metrics such as precision & recall, easy management of class weights for imbalanced classification, callbacks for model checkpointing and more...pic.twitter.com/pPwETGqZvw
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Is it possible to use `tf.keras.utils.Progbar()` with this approach now? I had issues where the ETA was compounded every epoch; if epoch 1 took 10 secs, epoch 2 would report 20
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How do I use callbacks (lr scheduler) with a custom training loop?
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