Why Keras Dense layer is expanding number of tensors in each layer
The number of parameters is the number of weights connecting the layer to the previous one, so for the layer $i$ it depends on the number of neurons in the layer $i$ and the previous one $i-1$. 

The exact formula for a fully connected neural network is: $$n_i(n_{i-1}+1),$$ where $n_i$ is the number of neurons in the layer $i$, $n_{i+1}$ the number of neurons in the layer $i+1$, and the $+1$ term takes into account the bias.

So for dense_2477 you indeed get $$n_{\mathrm{params}}=8(16+1) = 136,$$ and 
for dense_2482, $$n_{\mathrm{params}}=16*(8+1) = 144,$$ as expected.