I am new to Keras and just started working on some examples. I am dealing with the following problem: I have 4032 samples and use about 650 of them as for the fit or basically the training state and then use the rest for testing the model. The problem is that I keep getting the following error:
Exception: In a stateful network, you should only pass inputs with a number of samples that can be divided by the batch size.
I understand why I am getting this error, my question is, what if the size of my data is not divisible by batch_size? I used to work with Deeplearning4j LSTM and did not have to deal with this problem. Is there anyway to get around with this?
Thanks
The simplest solution is to use fit_generator instead of fit. I write a simple dataloader class that can be inherited to do more complex stuff. It would look something like this with get_next_batch_data redefined to whatever your data is including stuff like augmentation etc..
class BatchedLoader():
def __init__(self):
self.possible_indices = [0,1,2,...N] #(say N = 33)
self.cur_it = 0
self.cur_epoch = 0
def get_batch_indices(self):
batch_indices = self.possible_indices [cur_it : cur_it + batchsize]
# If len(batch_indices) < batchsize, the you've reached the end
# In that case, reset cur_it to 0 and increase cur_epoch and shuffle possible_indices if wanted
# And add remaining K = batchsize - len(batch_indices) to batch_indices
def get_next_batch_data(self):
# batch_indices = self.get_batch_indices()
# The data points corresponding to those indices will be your next batch data
