Why 3d input tensors in LSTM?

[chilango (Alex)]

@zhidali

“So if you divide a time series of length 10000 into chunks of length 50, your input tensor would be 50 (timesteps) by 200 (batch size) by 1 (features).”
“There are 200 batches in the dataset; each batch is 50x200x1.”
For my understanding, so if we train only one batch? because 50*200=10000, 10000 is the total number of the data

@zhidali Input needs to be a 3d tensor with dimensions (seq_len, batch_size, input_size), so: length of your input sequence, batch size, and number of features (if you only have the time series there is only 1 feature). If you train with a batch of size 1 the input tensor would be 50x1x1. I’m also learning but I think its accurate