# Should weight distribution change more when fine-tuning transformers-based classifier?

I'm using pre-trained DistilBERT model from Huggingface with custom classification head, which is almost the same as in the reference implementation:

class PretrainedTransformer(nn.Module):
def __init__(
self, target_classes):
super().__init__()
base_model_output_shape=768
self.base_model = DistilBertModel.from_pretrained("distilbert-base-uncased")
self.classifier = nn.Sequential(
nn.Linear(base_model_output_shape, out_features=base_model_output_shape),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(base_model_output_shape, out_features=target_classes),
)

for layer in self.classifier:
if isinstance(layer, nn.Linear):
layer.weight.data.normal_(mean=0.0, std=0.02)
if layer.bias is not None:
layer.bias.data.zero_()

def forward(self, input_, y=None):
base_model_last_layer = base_output[:, 0]
cls = self.classifier(base_model_last_layer)
return cls


During training, I use linear LR warmup schedule with max LR=5-e5 and cross entropy loss. In general, the model is able to learn on my dataset and reach high precision/recall metrics.

My question is:

Should weights distributions and biases in classification layers change more during training? It seems like the weights almost do not change at all, even when I do not initialize them as in the code (to mean=0.0 and std=0.02). Is this an indication that something is wrong with my model or it's just because the layers I've added are redundant and model does not learn nothing new?

Take look at the image of weight from the tensorboard: