What if the sample size is not divisible by batch_size in Keras model

user_6070667 0
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What if we specify batch size as 15 and the sample size of 1000 which is not divisible by 15 in Keras model training?.should it still able to train?

also I have looked in to this answer but it's not helping question

please can anybody explain this Thank you.

keras tensorflow machine-learning
3 Answers

I found the answer for this. If this is the case, it will take it will take the remaining 10 samples to the last step of the epoch.

Eg: 15x66+10=1000 that means it will take 66 batches of size 15 and for the final steps it takes only 10.

Anyways this will only work with input_shape, if we use batch_input_shape it will give us an error because we are specifying the batch shape in the graph level.

user_60706670

This is no problem for your training and validation data. The generator will take care of this. Hence you can simply use:

STEPS = train_generator.n // train_generator.batch_size
VALID_STEPS = validation_generator.n // train_generator.batch_size

history = model.fit_generator(
    train_generator,
    steps_per_epoch=STEPS,
    epochs=100,
    validation_data=validation_generator,
    validation_steps=VALID_STEPS)

However, for your testset make sure that the batch size fits the data, otherwise you run a risk of your predictions not matching your true labels when comparing both (please check this article which highlights this https://medium.com/difference-engine-ai/keras-a-thing-you-should-know-about-keras-if-you-plan-to-train-a-deep-learning-model-on-a-large-fdd63ce66bd2). You can ensure that the batch size fits your data by using a loop for example:

for i in range(1,160):
    if len(test_data) % i == 0:
        div = i
batch_size = div
user_63415100

in tf1.14 you should not care about this issue. It seems that in tf1.15, you need to worry about this.

user_78932160
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