Yes I did, things didn’t changed significantly even at some sets of hyper parameter it became worse.
Sure, I’m going to try to overfit my model with just a few example.
would u please take a look at my training section to see if I am feeding my NN correctly?
learning_rate = 1e-5
num_epochs = 360
model = Autoencoder().cuda()
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate,
weight_decay=1e-5)
#training loop
loss = 0
epoch_num = 0
error = []
for epoch in range(num_epochs):
for data in train_loader:
img = data
img = Variable(img).cuda()
output = model (img)
loss = criterion (output,img)

optimizer.zero_grad()
loss.backward()
optimizer.step()
error.append(loss.item())


if epoch%10 == 9:
  epoch_num += epoch_num
  plt.plot(error)


  print ('\r Train Epoch : {}/{} \tLoss : {:.4f}'.format (epoch+1,num_epochs,loss/32))

model_save_name = ‘Autoencoder 09 Jan 2020 2nd with new normalzation 2nd trial.pt’
path = F"/content/drive/My Drive/{model_save_name}"
torch.save(model.state_dict(), path)
plt.plot(error)
I have to recall you thay my input size is a tensor of (65536,1,94) shape, which 65536 is number of signals 1 is number of channel and 94 is length of each signal.
Kind Regards,