Hi, thanks for the response.
I tried adding batch normalization as such:
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        
        self.conv1 = nn.Conv1d(2, 2, kernel_size=(10,1), stride=1)
        self.conv1_bn = nn.BatchNorm2d(2)
        
        self.fc1 = nn.Linear(68, 64)
        self.fc1_bn = nn.BatchNorm1d(64)
        self.fc2 = nn.Linear(64, 16)
        self.fc2_bn = nn.BatchNorm1d(16)
        self.fc3 = nn.Linear(16, 1)
        
        self.dropout = nn.Dropout(0.25)
        
        
    def forward(self, x):
        
        x = self.conv1_bn(F.relu(self.conv1(x)))
#         print("shape after conv1:", x.shape)
        x = x.reshape(-1, 68)
        x = self.dropout(x)
        x = self.fc1_bn(F.relu(self.fc1(x)))
        x = self.dropout(x)
        x = self.fc2_bn(F.relu(self.fc2(x)))
        x = self.dropout(x)
        x = self.fc3(x)
        
        return x
    
net = Net()
print(net)

However I still ended up with similar results:

Any other ideas?