So I’m trying to learn model to drive a robot
there is rgb image input
and 4 outputs (turn,throttle,m1,m2)
batch size is always 1,if this is problem pls help to solve (
here is some loss information
I think normal loss for this model is 0.05-0.1
lr=0.025
{
Epoch:0 , loass:7737375380554.846
Epoch:1 , loass:7737643846993.172
at ep 7 loss was 2.5
}
Lr is to high lets try smaller one!
lr=0.001
{
Epoch:0 , loass:72.43417893535843
Epoch:1 , loass:72.60916486998268
Epoch:2 , loass:72.69703222041555
Epoch:3 , loass:72.75141251493582
Epoch:4 , loass:72.79511734658347
Epoch:5 , loass:7905.47881773466
Epoch:6 , loass:7910.287118291918
}
hm,still to big.
lr=0.0001
{
Epoch:1 , loass:0.04062871445124512
Epoch:2 , loass:0.08202989919366599
Epoch:3 , loass:0.11867608327466399
Epoch:4 , loass:0.15182475683913288
Epoch:5 , loass:0.1840306407139207
Epoch:6 , loass:0.21269866782967778
Epoch:7 , loass:0.2390143628541438
Epoch:8 , loass:0.26148748367000685
Epoch:9 , loass:0.28192748126230427
Epoch:10 , loass:0.30146832446190663
}
mayby it’s better but error is still increading instead of decreasing
lr=0.00001
{
Epoch:1 , loass:0.056156842192071454
Epoch:2 , loass:0.10663441620528054
Epoch:3 , loass:0.15288402274336577
Epoch:4 , loass:0.19622855389989446
Epoch:5 , loass:0.23698536783349802
Epoch:6 , loass:0.2753670450382031
Epoch:7 , loass:0.31140105827773595
Epoch:8 , loass:0.34510847057268446
Epoch:9 , loass:0.3766837950037029
Epoch:10 , loass:0.40588362538347367
}
i dont know why it’s getting wroster
Here is model train code,yes i now that corect loss comute is make a validate after every epoh,but its to slow.
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)

Here is model
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


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