Why my CNN IS NOT LEARNING.Couldn't able to use CNNs

[msarathrahul]

import torch
from torch import nn
from torch.utils.data import DataLoader,Dataset
import torchvision
from torchvision.transforms import ToTensor
from torchvision import datasets,models
train_data = datasets.FashionMNIST(root = 'data',train = True,transform=ToTensor(),download=True)
test_data = datasets.FashionMNIST(root = 'data',train = False,transform=ToTensor(),download=True)
train_batches = DataLoader(dataset=train_data,batch_size=64,shuffle=True)
test_batches = DataLoader(dataset=test_data,batch_size=64)
class ClassificationModel(nn.Module):

def __init__(self):
    
    super().__init__()
    
    self.conv_b1 = nn.Sequential(nn.Conv2d(in_channels=1,out_channels=10,
                                          kernel_size=3,stride=1,padding=1),
                                nn.ReLU(),
                                nn.Conv2d(in_channels=10,out_channels=10,
                                         kernel_size=3,stride=1,padding=1),
                                nn.ReLU(),
                                nn.MaxPool2d(kernel_size=2))
    self.conv_b2 = nn.Sequential(nn.Conv2d(in_channels=10,out_channels=10,
                                          kernel_size=3,stride=1,padding=1),
                                nn.ReLU(),
                                nn.Conv2d(in_channels=10,out_channels=10,
                                         kernel_size=3,stride=1,padding=1),
                                nn.ReLU(),
                                nn.MaxPool2d(kernel_size=2))
    
    self.classifier = nn.Sequential(nn.Flatten(),
                                   nn.Linear(in_features=10*7*7,out_features=10))
    
    
def forward(self,X):
    X = self.conv_b1(X)
    X = self.conv_b2(X)
    X = self.classifier(X)
    return X

torch.manual_seed(42)
model = ClassificationModel()
lossfn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(params=model.parameters(),lr = 0.1)
epochs = 3

for epoch in range(epochs):

train_loss = 0
for num,batch in enumerate(train_batches,start=1):
    
    model.train()
    
    Images,labels = batch
    
    logits = model(Images)
    
    loss = lossfn(logits,labels)
    
    train_loss += loss.item()
    
    optimizer.zero_grad()
    
    loss.backward()
    
    optimizer.step()
    
print(train_loss/num)

THE TRAIN LOSS IS AROUND 2.3, FOR ALL THE EPOCHS.
PLEASE HELP!!

[IIsameerII]

I faced a similar issue. changing the lr (learning rate) from 0.1 to 0.01 might solve the issue.

[msarathrahul]

Thanks, IIsameerII. I figured it after posting the question!!
Thanks for taking time and going though my question.