问题
If I have 1000 observations in my dataset with 15 features and 1 label, how is the data in input neurons fed for forward pass and back propagation? Is it fed row wise for 1000 observations (one at a time) and weights are updated with each observation fed or full data is given in terms of input matrix and then with number of epochs, the network learns corresponding weight values? Also if it is fed one at time, what is epochs in that case? Thanks
回答1:
Assuming that the data is formatted into rows (1000 instances with 16 features each, with the last one being the label), you would feed in the first 15 features row by row and use the last "feature"/label as the target. This is called online learning. Online learning requires you to feed the data in one example at a time and conduct the back propagation and the weight update for every example. As you can imagine this can get quite intensive due to the backpropagation and update for every instance of your data.
The other option that you mentioned is feeding in the entire data into the network. This performs poorly in practice as the convergence is extremely slow.
In practice, mini-batches are used. This involves sending a small subset of the dataset through and then doing the back propagation and weight update. This provides the benefit of relatively frequent weight updates to speed up learning but is less intensive than the online learning. For more information on mini-batches see this University of Toronto Lecture by Geoffrey Hinton
Finally, an epoch is always 1 run through all of your data. It doesn't matter if you feed it in one at a time or all at once.
I hope this clarified your questions.
来源：https://stackoverflow.com/questions/45201220/how-is-input-dataset-fed-into-neural-network