CODEWITHSUNDEEP
tensorflowkeras
Josh
Josh

Reputation: 12791

Why does Keras accept a batch size option for model.evaluate?

Why does the evaluate function of the Keras API in Tensorflow accept a batch_size? To my knowledge, this parameter should only be relevant for managing how many samples we use per iteration during training. What influence does this choice have during model evaluation?

Upvotes: 2

Views: 463

Answers (1)

user11530462
user11530462

Reputation:

Batch size is mainly used in Sequence-based predictions or in Time series predictions.

Below are the cases where you have to use batch size while prediction.

In Time Series use cases it may be desirable to use a large batch size when training the network and a batch size of 1 when making predictions in order to predict the next step in the sequence.

For Stateful RNN it is required to provide a fixed batch size during prediction/evaluation where the output state of the current batch is used as the initial state for the next batch. They keep information from one batch to another batch.

If your model doesn't fall into these kinds of category technically you don't need to provide batch size as input during evaluating. Even if you provide batch size, it's how much data you are feeding at a time for GPU.

Upvotes: 1

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