Sorry for the confusion in the last post and thank you for being patient with me.

But what are the  batch_Y  (targets)? Are they known stride lengths?

These are known stride lengths (outputs) in the range 1.2514 to 1.5568 that in batches of 10 in a tensor.

I don’t really understand what you mean. Do you mean that you have a batch size of 200, and for each sample in the batch you get exactly the same value for the single, scalar output?

For the test batch, I meant I have separate data that I compare my NN with. It has 200 tensor(1, 1040) inputs with their respective outputs.
The issue I have is when I feed an input tensor into the neural network, it feeds out a tensor with the same value single float value for each input. I want the output to be specific with each input. The training and testing code is as follows with the NN class as above
    val_size = int(len(input_tensor) * test_partition)
    train_inputs = input_tensor[:-val_size]
    train_outputs = output_tensor[:-val_size]
    test_inputs = input_tensor[-val_size:]
    test_outputs = output_tensor[-val_size:]

        for epoch in range(EPOCHS):
        for i in tqdm(range(0, (len(train_inputs)), BATCH_SIZE)):
            batch_X = train_inputs[i:i+BATCH_SIZE]
            batch_Y = train_outputs[i:i+BATCH_SIZE]

            net.zero_grad()
            outputs = net(batch_X)
            loss = loss_function(outputs, batch_Y)
            loss.backward()

            if epoch > learning_rate_cutoff:  # this acts as a decaying learning rate for our system
                learning_rate = 0.0001
            optimizer.step()
    print(f"\nEpoch: {epoch}. Loss: {loss}")
    
    print("testing")
    correct = 0
    total = 0
    with torch.no_grad():
        for i in tqdm(range(len(test_inputs))):
            predicted_class = net(test_inputs[i].view(-1, 1040))
            real_class = test_outputs[i]
            print(predicted_class, real_class)

            if predicted_class == real_class:
                correct += 1
            total += 1
    print("\nAccuracy: ", round(correct / total, 3))

EPOCHS is 700
BATCH_SIZE is 10
test_partition is 0.1 with len(input_tensor) being 2000