Can someone explain batch-size and timestep for a regression model using RNN?

Question

I am working on a regression model, which has 50 datapoints per hour. I am having a hard time deciding on the difference between batch size and time-step. From my understanding, batch size is used to decide how many datapoints do we want to consider before making a prediction. The larger the value, the longer it takes for the model to converge. If that is the case I am clear on definition of batch size. So if my model isn't taking very long, can I just use the maximum? Would that maximum be the test datasize?

How about timesteps then? For a model where you measure let's say temperature every minute till 30 hours, what would the timestep be? I would appreciate it if someone who knows about regression using RNN could answer my doubts.

Answer 1

Given :

import numpy as np

x = np.array([[[1], [0], [1]]])

print(x.shape)

Output:

(1, 3, 1)

This is for m samples s timesteps with e measurements per timesteps.

(m, s, e)

In any case the number of data points is the size of the array so:

m * s * e

The number of data point per sample:

s * e

If you meaure temperature every second for an hour on one sample.

(1, 3600, 1)

If you measure let say temperature and humidity.

(1, 3600, 2)

Let say you do that simultaneously for 2 samples (in place A and B).

(2, 3600, 2)

Batch size is just not related at all.

For each epoch it just means how many samples you want to run at once. For 100 samples batch size 50 you have two weight update per epoch for instance.