Linear nn output problem

Hi everyone, i build simple linear nn but output of this network is not same as desired result.
I got this result from my network
this is my loss graph
this is the desired result

my codes are:

class Module(nn.Module):
        def __init__(self, D_in, H1, H2, D_out):
                        self.linear1 = nn.Linear(D_in, H1)
                        self.linear2 = nn.Linear(H1, H2)
                        self.linear3 = nn.Linear(H2, D_out)
        def forward(self, x):
                        x = F.relu(self.linear1(x))  
                        x = F.relu(self.linear2(x))
                        x = self.linear3(x)
                        return x

train_dataset = TensorDataset(train_x, train_y)
train_generator = DataLoader(train_dataset, batch_size=32,shuffle=False)
valid_dataset = TensorDataset(val_x, val_y)
valid_generator = DataLoader(valid_dataset, batch_size=32)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
for e in range(epochs):
        running_loss = 0.0
        running_corrects = 0.0
        val_running_loss = 0.0
        val_running_corrects = 0.0
        for inputs,out in train_generator:
                new_output = torch.squeeze(output)
                print("input size: ",inputs.size())
                loss = criterion(new_output,out)
                print("output size: ",output.size())
                print("out size: ",out.size())

             with torch.no_grad():
                for val_inputs, val_labels in valid_generator:
                        #val_inputs = val_inputs.view(val_inputs.shape[0], -1)
                        val_outputs = model(val_inputs)
                        val_loss = criterion(val_outputs, val_labels)
                        val_preds,_ = torch.max(val_outputs, 1)

If you can help me, I will be very thankful

Guys, if anyone can help me, i will be very appreciate to him/her

What kind of input data are you using?
Based on the loss curve it looks like your model is still training and the loss goes down.
Have you seen a plateau after a while?

the loss curve is normal. the result curve is abnormal,
I think that the distribution of the trian and vaild datasets is different.

I have dataset which is .csv file and I read them into numpy array

Hi, if we said that dataset is 100x then train set is 85x and valid set is 15x so actually they are not so different

Thanks for the information. I’m still unsure, why you’ve stopped the training when the loss was still going down. Did you try to train it a bit longer and check the results?

Of course, the number of the train and the valid set is different.
My idea is whether the distribution of the two is the same.
u can eandomly split the dataset into train and vaild set, then train your model, and observe the results.

Yes, i trained them like 2k epochs and more but result did not change

I thought validation set size should be smaller than training set? If I am wrong, can you explain me briefly?


yeah, the train set size should bigger than the validation set size.
My means is that u can randomly split the dataset into train and valid set according to a ratio, e.g., 8:2.
Then, train your model and oberve the results.