Model loss increases after learning

username: OkunElya
cooked: <p>So I’m trying to learn model to drive a robot<br>
there is rgb image input<br>
and 4 outputs (turn,throttle,m1,m2)<br>
batch size is always 1,if this is problem pls help to solve (<br>
here is some loss information<br>
I think normal loss for this model is 0.05-0.1<br>
<strong>lr=0.025</strong><br>
<strong>{</strong><br>
<strong>Epoch:0 , loass:7737375380554.846</strong><br>
<strong>Epoch:1 , loass:7737643846993.172</strong><br>
at ep 7 loss was 2.5<br>
<strong>}</strong><br>
Lr is to high lets try smaller one!<br>
<strong>lr=0.001</strong><br>
<strong>{</strong><br>
<strong>Epoch:0 , loass:72.43417893535843</strong><br>
<strong>Epoch:1 , loass:72.60916486998268</strong><br>
<strong>Epoch:2 , loass:72.69703222041555</strong><br>
<strong>Epoch:3 , loass:72.75141251493582</strong><br>
<strong>Epoch:4 , loass:72.79511734658347</strong><br>
<strong>Epoch:5 , loass:7905.47881773466</strong><br>
<strong>Epoch:6 , loass:7910.287118291918</strong><br>
<strong>}</strong><br>
hm,still to big.<br>
lr=0.0001<br>
{<br>
Epoch:1 , loass:0.04062871445124512<br>
Epoch:2 , loass:0.08202989919366599<br>
Epoch:3 , loass:0.11867608327466399<br>
Epoch:4 , loass:0.15182475683913288<br>
Epoch:5 , loass:0.1840306407139207<br>
Epoch:6 , loass:0.21269866782967778<br>
Epoch:7 , loass:0.2390143628541438<br>
Epoch:8 , loass:0.26148748367000685<br>
Epoch:9 , loass:0.28192748126230427<br>
Epoch:10 , loass:0.30146832446190663<br>
}<br>
mayby it’s better but error is still increading instead of decreasing<br>
lr=0.00001<br>
{<br>
Epoch:1 , loass:0.056156842192071454<br>
Epoch:2 , loass:0.10663441620528054<br>
Epoch:3 , loass:0.15288402274336577<br>
Epoch:4 , loass:0.19622855389989446<br>
Epoch:5 , loass:0.23698536783349802<br>
Epoch:6 , loass:0.2753670450382031<br>
Epoch:7 , loass:0.31140105827773595<br>
Epoch:8 , loass:0.34510847057268446<br>
Epoch:9 , loass:0.3766837950037029<br>
Epoch:10 , loass:0.40588362538347367<br>
}<br>
i dont know why it’s getting wroster</p>
<p>Here is model train code,yes i now that corect loss comute is make a validate after every epoh,but its to slow.</p>
<pre><code class="lang-auto">model = CNN()
model.train()
model.to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
#optimizer=optim.SGD(model.parameters(),lr=learning_rate,momentum=0,nesterov=False)
loss_list = []
ep_loss=[]
sr_loss=0
loss = 0
for epoch in range(num_epochs):
    i = 0
    for data in train_loader:
        i += 1

        in_images, out_images = data
        in_images=in_images.to(device)
        out_images=out_images.to(device)

        optimizer.zero_grad()

        outputs = model(in_images) 
       
        loss = criterion(outputs, out_images[0])

        loss.backward()
        
        optimizer.step()
        if i % 20==0 :
            print("Loss = ", round(float(loss.cpu().detach().numpy()), 4), i,end="\r")
        sr_loss+=float(loss.cpu().detach().numpy())
    loss_list.append(sr_loss/len(train_X))
    #test(val_X,model,[val_Y1,val_Y2,val_Y3,val_Y4])
    print(f'Epoch:{epoch} , loass:{sr_loss/len(train_X)}')

    torch.save(model.state_dict(), "model_pytorch_1.plk")

    time.sleep(5)
</code></pre>
<p>Here is model</p>
<pre><code class="lang-auto">class CNN(nn.Module):
    def __init__(self, in_channels=3, num_classes=4):
        super(CNN, self).__init__()

        self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=16, kernel_size=(5, 5), stride=(2, 2), padding=(1, 1))
        #self.pool = nn.MaxPool2d(kernel_size=(24, ), stride=(2, 2))
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(5, 5), stride=(2, 2), padding=(1, 1))
        self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(5, 5), stride=(2, 2), padding=(1, 1))
        self.conv4 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(5, 5), stride=(2, 2), padding=(1, 1))
        self.drop = nn.Dropout(p=0.2)
    
        self.fc1 = nn.Linear(6912, 1024)
       # self.rnn1=nn.LSTM(input_size=1024,hidden_size=1024,num_layers=1,batch_first=False)
        self.fc2 = nn.Linear(1024, num_classes)
        
        # self.fc1 = nn.Linear(128*1*1, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        #x = self.pool(x)
        x = self.conv2(x)
        # x = self.pool(x)
        x = self.conv3(x)
        # x = self.pool(x)
        x = self.conv4(x)
        #x = self.pool(x)
        x = self.drop(x)
        x = torch.flatten(x)
        x = self.fc1(x)
        #x=self.rnn1(x)
        x = self.fc2(x)
        return x

</code></pre>
<p>Please help make this model learn correctly and if you need dataset and code message here i will send it in your email<br>
P.S(sorry for mistakes my eng is bad)</p>

username: ayalaa2
cooked: <p>I think the issue is this line: <code>sr_loss/len(train_X)</code>. I’m assuming that <code>len(train_X)</code> is a constant and <code>sr_loss</code> is defined to be 0 at the beginning of training.</p>
<p>But you don’t ever zero it out. It’s always being added onto. So it’s just getting larger and larger as time goes on, <em>regardless</em> of how training is going.</p>

username: OkunElya
cooked: <p>Thanks! i will fix it right now</p>

username: OkunElya
cooked: <p>Epoch:1 , loass:0.04277728414539726<br>
Epoch:2 , loass:0.0406925378030401<br>
Epoch:3 , loass:0.03725993632761486<br>
Epoch:4 , loass:0.03740865272509567<br>
Epoch:5 , loass:0.029603138416451756<br>
Epoch:6 , loass:0.02794443646247365<br>
Epoch:7 , loass:0.028427238596042553</p>