I have time series data with sample dim of 1024x1 which one-dimensional input with 1024 length, I am trying to apply conv1d by specifying the number of input channels as 1 and the output channels as 5. while running I got an error, I think i miss understood something, I will be grateful for any help.
This is my layer architecture :  self.feature_extractor = nn.Sequential(
#1024x1 —> 5x1024
nn.Conv1d(1, 5,3),
#5x1024----> 5x512
nn.MaxPool1d(2),
nn.ReLU(),
#5x512---->10x512
nn.Conv1d(5, 10, kernel_size=3, padding=1),
#10x512—>10x256
nn.MaxPool1d(2),
#10x512—>10x256
nn.Conv1d(10, 20, kernel_size=3,padding=1),
#20256—>20128
nn.MaxPool1d(2),
nn.Dropout2d(),
)
====
And here is the Error:
Expected 3-dimensional input for 3-dimensional weight [5, 1, 3], but got input of size [1024, 1] instead