TensorFlow keras model fit() parameters steps_per_epoch and epochs behavior on train set

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I'm using a tf.data dataset containing my training data consisting of (lets say) 100k images. I'm also using a tf.data dataset containing my validation set. Since an epoch of all 100k images takes quite long (in my case approximately one hour) before I get any feedback on performance on the validation set, I set the steps_per_epoch parameter in tf.keras.Model fit() to 10000. Using a batch size of 1 this results into having 10 validation scores when reaching 100k of images. In order to complete one epoch of 100k images of my entire training dataset, I set the epochs parameter to 10

However, I'm not sure if using steps_per_epoch and epochs this way has any other consequences. Is it correct to use these parameters in order to get more frequent feedback on performance? And also a more specific question, does it use all 100k images or does it use the same first 10k images of my training set at every 'epoch'? I already dug into the TensorFlow docs and read several different stack overflow questions, but I couldn't find anything conclusive to answer my own question. Hope you can help!

Tensorflow version I'm using is 2.2.0.

tensorflow-datasets tensorflow2.0 tensorflow python-3.x python
1 Answers

Is it correct to use these parameters in order to get more frequent feedback on performance?

Yes, it is correct to use these parameters. Here is the code that i used to fit the model.

model.fit(
train_data,
steps_per_epoch = train_samples//batch_size,
epochs = epochs,
validation_data = test_data,
verbose = 1,
validation_steps = test_samples//batch_size)

does it use all 100k images or does it use the same first 10k images of my training set at every 'epoch'?

It use all images in your training data.

For better understanding Epoch is the number times the learning algorithm will work through the entire training data set.

Where as steps_per_epoch is the total number of samples in your training data set divided by the batch size.

For example, if you have 100000 training samples and use a batch size of 100, one epoch will be equivalent to 1000 steps_per_epoch.

Note: We generally observe batch size to be the power of 2, this is because of the effective work of optimized matrix operation libraries.

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