shakeel608:

My question is what is the inputSize in LSTM


The mentioned inputSize in your shape information would correspond to the “feature” dimension.
Since your CNN output is 4-dimensional, you would have to decide which dimensions are corresponding to the temporal dimensions and which to the features.
Assuming you would like to use C_out and Fequency as the features, you could use:
x = torch.randn(8, 1, 10, 10)
x = x.view(x.size(0), -1, x.size(3)) # [batch_size, features=channels*height, seq_len=width]
x = x.permute(2, 0, 1) # [seq_len, batch_size, features]

and pass it to the RNN.
PS: Please don’t tag certain people, as this might discourage others to post a solution