https://discuss.pytorch.org/t/trouble-with-1d-cnn/21868

1d_conv_experiment.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class model(nn.Module):
    def __init__(self):
        super(model, self).__init__()
        self.conv1 = nn.Conv1d(9, 18, kernel_size=3) #9 input channels, 18 output channels
        self.conv2 = nn.Conv1d(18, 36, kernel_size=3) #18 input channels from previous Conv. layer, 36 out
        self.conv2_drop = nn.Dropout2d() #dropout
        self.fc1 = nn.Linear(1044, 72) #Fully-connected classifier layer
        self.fc2 = nn.Linear(72, 19) #Fully-connected classifier layer
    
    def forward(self, x):
        x = F.relu(F.max_pool1d(self.conv1(x), 2))
        print(x.shape)
        x = F.relu(F.max_pool1d(self.conv2_drop(self.conv2(x)),2))
        print(x.shape)

        #point A
        x = x.view(x.shape[0], -1)
        
        #point B
        x = self.fc1(x)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

if __name__ == '__main__':
    m = model()
    data = torch.randn(64, 9, 125)
    out = m(data)
    print(out.shape)