Week 2 - Exercise 2 - Mini Batch

Course Q&A Deep Learning Specialization Improving Deep Neural Networks: Hyperparameter tun
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Hey @Syed_Hamza_Tehseen,
For building this logic, 4 variables will come in handy to you:

  • m = Total number of samples, which you can use for defining the last mini-batch
  • mini_batch_size = Maximum number of samples in a mini-batch, which you can use to find the indices of the various mini-batches.
  • num_complete_minibatches = Denotes the total number of mini-batches, consisting of samples equal to mini_batch_size
  • k = Denotes the indices for the mini-batches

Note that all of these variables have already been defined for you. Now, let’s say that k = 2 and mini_batch_size = 32. Since k = 2, this means we are referring to the 3rd mini-batch (since indexing begins from 0 in Python). Now in this case, the first mini-batch will have samples indexed from 0 to 31. Similarly, the second mini-batch will have samples indexed from 32 to 63.

Now, the 3rd mini-batch, which we are concerned about has samples indexed from 64 to 95. If we contemplate k * mini_batch_size = 64 and (k * mini_batch_size) - 1 = 95. Voila, our indices :partying_face: Cherry on the cake, when you use slicing for indexing in Python, it doesn’t include the element corresponding to the closing index, i.e., if you use X[start, end], this won’t include the sample X[end]. Now, I leave it up to you to figure out the code for the last mini-batch.

Cheers,
Elemento

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