Where the parameters actually are

A layer from width $a$ to width $b$ holds $ab$ weights and $b$ biases.

Weights. One matrix per layer, of shape (input width $\times$ output width):

$\text{total weights} = 6{,}100$

Biases. One per output unit, so $50 + 20 + 5 = 75$.

Total. $6{,}100 + 75 = \mathbf{6{,}175}$ parameters.

First-layer share.

$\frac{5{,}000 + 50}{6{,}175} = \frac{5{,}050}{6{,}175} = 0.8178 = 81.78\%$

What it implies. Four fifths of the network is the single matrix that meets the input. Two consequences follow directly:

Widening the input is expensive and widening the tail is cheap. Going from $100$ to $200$ inputs adds $5{,}000$ parameters — as many as the whole first layer already had. Going from $5$ outputs to $10$ adds $105$.

Parameter count is a poor proxy for depth. This network could be made twice as deep by inserting another $20 \to 20$ layer, adding $420$ parameters — under $7\%$ — while changing the function class substantially. Counting parameters tells you about memory; it tells you very little about capacity in the sense that matters for I.1.T2.

Biases are $1.2\%$ of the total here, which is why they are routinely omitted from back-of-envelope counts. That is a rounding decision, not a claim that they do nothing.