I’m trying to implement this network from keras to pytorch, not sure if it’s workable. The target model in keras is as following:
input_7 (InputLayer)             (None, 200, 76)       0                                            
____________________________________________________________________________________________________
conv_1 (Convolution1D)           (None, 192, 10)       6850        input_7[0][0]                    
____________________________________________________________________________________________________
conv_2 (Convolution1D)           (None, 184, 10)       910         conv_1[0][0]                     
____________________________________________________________________________________________________
conv_3 (Convolution1D)           (None, 174, 20)       2220        conv_2[0][0]                     
____________________________________________________________________________________________________                   

But the PyTorch seems to have a different API for conv1d. I tried this:
nn.Sequential(
            nn.Conv1d(in_channels=200, out_channels=192, kernel_size=10),
            nn.ReLU(),
            nn.Conv1d(in_channels=192, out_channels=184, kernel_size=10),
            nn.ReLU(),
            nn.Conv1d(in_channels=184, out_channels=174, kernel_size=20),
            nn.ReLU(),
        )

which seems not working properly.
The kernel_size in keras Convolution1D doesn’t seem very simple to transfer to conv1d in PyTorch.
Appreciate any help.