# Is it acceptable to have a slightly lower validation loss than training loss

I have a dataset which I split as 80% training and %20 validation sets. (38140 images for training, 9520 for validation) Model that I train is a deeper (~45 layers) convolutional neural network.

I got the below results in the first epochs of training:

Epoch 1: train loss: 1041.52 - validation loss: 1045.89
Epoch 2: train loss: 750.78  - validation loss: 749.95
Epoch 3: train loss: 425.88  - validation loss: 423.35
Epoch 4: train loss: 320.29  - validation loss: 319.35
Epoch 5: train loss: 305.41  - validation loss: 305.07


As can be seen, after first epoch the validation error is slightly lower than training loss. Is it something that I worry or Is it an indicator of good convergence and generalization?