Loss per batch is not decreasing, need help!

Hello. I am very new to deep learning and recently I am going through tutorials on making neural network.

I have created my own custom set, which contains 10000 images of faces for training and roughly 3000 for validating, to classify age of people (79 classes, or ages). I use pretrain network from Resnet50 and freeze the CNN part and replace with my custom fully connected layers.

However, when training starts, the loss per batch (where I print it out everytime calculating the loss using cross entropy) is not decreasing. My GPU is kind of sucks so I use CPU for training. I just run for 10 batches where the loss per batch is always at 3.6-3.8, with no sign of lowering.

Hope someone can point out what is the exact underlying problems. Thank you.

train loss for 1 batch=  4.3613080978393555
train loss for 1 batch=  4.377458095550537
train loss for 1 batch=  4.360567092895508
train loss for 1 batch=  4.366171836853027
train loss for 1 batch=  4.372839450836182
train loss for 1 batch=  4.366669178009033
train loss for 1 batch=  4.3775248527526855
train loss for 1 batch=  4.3778791427612305
train loss for 1 batch=  4.360372066497803
train loss for 1 batch=  4.367254257202148
train loss for 1 batch=  4.349947929382324
train loss for 1 batch=  4.358979225158691
train loss for 1 batch=  4.36530065536499
train loss for 1 batch=  4.340318202972412
train loss for 1 batch=  4.3592658042907715
train loss for 1 batch=  4.3571014404296875
train loss for 1 batch=  4.34581995010376
train loss for 1 batch=  4.36361026763916
train loss for 1 batch=  4.362382888793945
import torch
import torchvision.transforms as transforms
import torchvision.models as models
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import dataset, DataLoader
from CustomDataset import FaceDataset
import time

def num_correct(outputs, labels):
    return correct_num

# age= index+2

model = models.resnet50(pretrained=True)
for paras in model.parameters():
    paras.requires_grad = False

fc_input = model.fc.in_features  # 2048
new_fc_layers = nn.Sequential(nn.Linear(fc_input, 1024),
                              nn.Linear(1024, 512),
                              nn.Linear(512, age_categories),
model.fc = new_fc_layers

dataset = FaceDataset(csvFile='ImageInfo.csv', ImageFolder='aglined faces',
                          transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))

train_set, valid_set = torch.utils.data.random_split(dataset, [10000, 3322])

optimizer=optim.SGD(model.parameters(), lr=learning_rate)

for epoch in range(iteration):
    train_loss = 0
    valid_loss = 0

    for batch in train_loader:
        images, labels=batch


        loss=loss_function(output, labels)
        print('train loss for 1 batch= ',loss.item())


    print('total train loss for 1 epoch= ',train_loss)

    with torch.no_grad():
        for batch in valid_loader:

            number_Correct = 0

            images, labels = batch
            images = images.to(device)
            labels = labels.to(device)

            output = model(images)

            loss = loss_function(output, labels)
            print('valid loss for 1 batch= ',loss.item())

            print('number correct for 1 batch= ',number_Correct)

    print('total valid loss for 1 epoch= ', valid_loss)


on my first glance, I am not sure why you need this line.
Can you try removing this line or at least change the paras.requires_grad to True back, once you enter your first epoch?

Because I use the pretrain network, where the CNN layers will be frozen so the trained parameters would not change. I then replace the fully connected layer with my own custom layers.
All the process is refer to the official pytorch guide, when want to do transfer learning.

Can you check whether
model.fc.parameters is on requires_grad = True.

Because at the end you are given into the optimizer the parameters you want to train.
You are exchanging model.fc with your own version of it and you want to train the parameters of your customized model.fc.

To check the parameters you give into the optimizer would be the first thing to check.
If this still fails we can think of other possible mistakes.

Thanks for your suggestion. I have tried to print out required_grad in each parameters in the model. It showed that CNN layers are false and fully connected layers are true… So, that is not the problem of this issue…

Hi can you try your code with the line:

optimizer=optim.SGD(model.fc.parameters(), lr=learning_rate)
The code seems totally legit and either this is the problem or one has to check the dataloader further.
There is also the probability of this

model.fc = new_fc_layers

assignment needs to be done differently.
For replacement check this out:
How to Replace a layer or Module in a pretrained network? - PyTorch Forums

Thank you so much! I look into the tutorials and yea that’s the exact point that I had missed! I only need to optimize the custom layers that I created indeed.

Thank you so much! I will try it later to see if it works.