I am using the KNIME Doc2Vec Learner node to build a Word Embedding. I know how Doc2Vec works. In KNIME I have the option to set the parameters
- Batch Size: The number of words to use for each batch.
- Number of Epochs: The number of epochs to train.
- Number of Training Iterations: The number of updates done for each batch.
From Neural Networks I know that:
- one epoch = one forward pass and one backward pass of *all* the training examples
- batch size = the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you'll need.
- number of iterations = number of passes, each pass using [batch size] number of examples. To be clear, one pass = one forward pass + one backward pass (we do not count the forward pass and backward pass as two different passes).
As far as I understand it makes little sense to set batch size *and* iterations, because one is determined by the other (given the data size, which is given by the circumstances). So why can I change both parameters?