Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

• L stands for the Length of each sequence
"Wait, where is the number of features in it?"
Good point! Since 1D convolutions only move along the sequence, each feature is
considered an input channel. So, you can think of the shape as NFL or N(C/F)L if
you like.
"Really?! Yet another shape for input sequences?"
Unfortunately, yes. But I’ve built this small table to help you wrap your head
around the different shape conventions while working with sequences as inputs:
Shape
Use Case
Batch-first N, L, F Typical shape; RNNs with batch_first=True
RNN-friendly L, N, F Default for RNNs (batch_first=False)
Sequence-last N, F, L Default for 1D convolutions
Having (hopefully) cleared that up, let’s use permute to get our sequences in the
appropriate shape:
seqs = torch.as_tensor(points).float() # N, L, F
seqs_length_last = seqs.permute(0, 2, 1)
seqs_length_last.shape # N, F=C, L
Output
torch.Size([128, 2, 4])
Multiple Features or Channels
Our sequences of corners have two coordinates; that is, two features. These will
be considered (input) channels as far as the 1D convolution is concerned, so we
create the convolutional layer accordingly:
672 | Chapter 8: Sequences