# Why do current models use multiple normalization layers?

In most current models, the normalization layer is applied after each convolution layer. Many models use the block $$\text{convolution} \rightarrow \text{batch normalization} \rightarrow \text{ReLU}$$ repeatedly. But why do we need multiple batch normalization layers? If we have a convolution layer that receives a normalized input, shouldn't it spit out a normalized output? Isn't it enough to place normalization layers only at the beginning of the model?