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In Keras, to predict class of a datatest, the predict_classes() is used.
For example:
classes = model.predict_classes(X_test, batch_size=32) My question is, I know the usage of batch_size in training, but why does it need a batch_size for prediction? how does it work?
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The default batch size is 32, due to which predictions can be slow. You can specify any batch size you like, in fact it could be as high as 10,000.
Batch size controls the accuracy of the estimate of the error gradient when training neural networks. Batch, Stochastic, and Minibatch gradient descent are the three main flavors of the learning algorithm. There is a tension between batch size and the speed and stability of the learning process.
Number of samples per batch. If unspecified, batch_size will default to 32.
In practical terms, to determine the optimum batch size, we recommend trying smaller batch sizes first(usually 32 or 64), also keeping in mind that small batch sizes require small learning rates. The number of batch sizes should be a power of 2 to take full advantage of the GPUs processing.
Keras can predict multiple values at the same time, like if you input a vector of 100 elements, Keras can compute one prediction for each element, giving 100 outputs. This computation can also be done in batches, defined by the batch_size.
This is just in case you cannot fit all the data in the CPU/GPU RAM at the same time and batch processing is needed.
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The reason is the same , why you need batch size for training, because you cannot fit all data into one single batch
Similarly, if you have millions of data points to predict, it is obviously that you will not be able to pass at one go (single batch).
After all, training and prediction both have a forward pass on the batch data.
Hence, you need the batch size to control/limit the data point in a single batch and distribute it across multiple batches of prediction.
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