One Full Turn of the Loop

A toy regression network, small enough to train by hand, takes two inputs, has two ReLU hidden neurons, and has one output neuron with no activation:

$h_1 = \text{ReLU}(w_{11}x_1 + w_{12}x_2 + b_1), \qquad h_2 = \text{ReLU}(w_{21}x_1 + w_{22}x_2 + b_2)$

$\hat y = v_1 h_1 + v_2 h_2 + c$

Its nine parameters currently are:

neuron	first incoming weight	second incoming weight	bias
hidden 1 (fed by $x_1, x_2$ )	$w_{11} = 0.5$	$w_{12} = 0.25$	$b_1 = 0$
hidden 2 (fed by $x_1, x_2$ )	$w_{21} = -0.5$	$w_{22} = 0.75$	$b_2 = 0.5$
output (fed by $h_1, h_2$ )	$v_1 = 1$	$v_2 = -0.5$	$c = 0.25$

The training example is $x_1 = 1$ , $x_2 = 2$ with target $y = 2$ . The loss is the squared error on this one example, $L = (\hat y - y)^2$ .

Run one complete training step on this example: forward, loss, backward, update. The update is plain gradient descent on every weight and bias,

$p \leftarrow p - \eta\, \frac{\partial L}{\partial p}, \qquad \eta = 0.02$

Then run the forward pass again on the same example, using the updated parameters.

What is the loss now?

Round to 3 decimal places.