How to learn multiple binary classifiers?

Hi, I have an input of shape 14 x 10 x 128 x 128, where 14 is batch_size, 10 is the sequence_length and each item in the sequence is of shape 128 x 128. I want to learn to map this input to output of shape 14 x 10 x 128, i.e., for each item in the sequence I want to learn 128-binary classifiers.

Does the following model make sense? So, first I reshape my input to 140 x 128 x 128 and then pass it through the model and reshape the output back to 14 x 10 x 128 .

classifier = nn.Sequential(
    nn.Conv1d(128, 128, 1),
    nn.ReLU(),
    nn.BatchNorm1d(128),
    nn.Conv1d(128, 128, 1),
    nn.ReLU(),
    nn.BatchNorm1d(128),
    nn.Conv1d(128, 1, 1)
)

Thank you.

The model architecture could work and the conv layers could be replaced with inear layers (but the current architecture might be easier to apply the batchnorm layers so you might want to keep it).
The last reshaping might not be necessary as you are treating each time step as a separate sample in the model so could also do the same in the loss calculation, but again it also doesn’t seem to be wrong.

Thank you so much for your valuable feedback. Much appreciated!

Could you please take a quick look at the loss I’m calculating for the above model to see if I’m doing it correctly: Multi-label Classifier Loss