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I saw a sample of code (too big to paste here) where the author used model.train_on_batch(in, out) instead of model.fit(in, out). The official documentation of Keras says:
Single gradient update over one batch of samples.
But I don't get it. Is it the same as fit(), but instead of doing many feed-forward and backprop steps, it does it once? Or am I wrong?
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train_on_batch allows you to expressly update weights based on a collection of samples you provide, without regard to any fixed batch size. You would use this in cases when that is what you want: to train on an explicit collection of samples.
So in the end what train_on_batch returns is the loss, output one mse, and output two mse. That is why you get three values. Follow this answer to receive notifications. answered Mar 17, 2021 at 15:09.
You can do this by setting the validation_split argument on the fit() function to a percentage of the size of your training dataset. For example, a reasonable value might be 0.2 or 0.33 for 20% or 33% of your training data held back for validation.
Here we are training our network for 10 epochs along with the default batch size of 32. For small and less complex datasets it is recommended to use keras.
Yes, train_on_batch trains using a single batch only and once.
While fit trains many batches for many epochs. (Each batch causes an update in weights).
The idea of using train_on_batch is probably to do more things yourself between each batch.
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It is used when we want to understand and do some custom changes after each batch training.
A more precide use case is with the GANs. You have to update discriminator but during update the GAN network you have to keep the discriminator untrainable. so you first train the discriminator and then train the gan keeping discriminator untrainable. see this for more understanding: https://medium.com/datadriveninvestor/generative-adversarial-network-gan-using-keras-ce1c05cfdfd3
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