@Unity05  Thanks a lot. It works. Now the loss is not terrible! And the training phase is not bad,
Training phase prediction
[ 7.6779],
        [ 4.2894],
        [ 4.9621],
        [ 3.0036],
        [ 1.2166],
        [ 4.3545],
        [ 6.1130],
        [ 8.3136],
        [ 6.8330],
        [ 2.3176],
        [ 4.7833],
        [-0.7525],
        [ 3.9115],
        [ 3.6507]]

Training phase actual
[9, 6, 6, 2, 1, 2, 8, 9, 7, 3, 6, 0, 5, 5]

But at the time of testing I am getting 0 not any numbers!!
Test Accuracy of the model on the 10000 test images: %.2f 0.05
Prediction Number: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
Actual Number [9 0 5 4 9 4 9 9 6 4 8 6 3 3 7 8 8 9 6 6 7 2 8 1 6 6 7 9 2 0 1 8 9 4 3 2 6
 6 0 1 5 2 7 2 8 2 0 5 7 4 6 2 4 2 1 2 8 2 2 9 6 8 1 7 3 4 1 4 2 3 1 6 4 8
 7 0 2 5 5 1 4 4 5 1 1 3 7 1 9 0 7 4 2 0 7 0 2 8 5 3]

Any idea, why this is happening? Or I am doing something wrong at the time of testing?
#Loading Model
model = my_model.get_model()
model.load_state_dict(torch.load(os.path.join(CHECKPOINT, "baseline.h5")))

#loading test data
loaders = preprocess.data_loaders()

def test():
    # Test the model
    with torch.no_grad():
        correct = 0
        total = 0
        for images, labels in loaders['test']:
            test_output, last_layer = model(images)
            pred_y = torch.max(test_output, 1)[1].data.squeeze()
            accuracy = (pred_y == labels).sum().item() / float(labels.size(0))
            
    print('Test Accuracy of the model on the 10000 test images: %.2f', accuracy)
    
    
test()

This is the actual prediction from test images
        [ 7.0612],
        [ 4.3061],
        [ 6.4712],
        [ 0.7163],
        [ 3.1995],
        [ 2.7388],
        [ 3.8326],
        [ 3.6017],
        [ 7.4603],
        [ 0.7398],
        [ 8.2723],
        [ 1.7648]], grad_fn=<AddmmBackward>)

I think, I am doing something wrong in this line
pred_y = torch.max(test_output, 1)[1].data.squeeze()