Configure the neural network playground to use the 'Spiral' dataset with 'Tanh' activation and 'L2' regularization at a rate of 0.003. Enable 'X1', 'X2', 'X1^2', and 'X2^2' features. Build a network with 3 hidden layers, setting their neuron counts to 5, 4, and 3 respectively. Run the network and report the final test and training loss after 1000 epochs.
Click the 'Reset the network' button (id: 12). Select the 'Spiral' dataset (visible as a circular data pattern). Set 'Activation' to 'Tanh' (id: 40). Set 'Regularization' to 'L2' (id: 48). Set 'Regularization rate' to '0.003' (id: 55). Ensure only 'X1', 'X2', 'X1^2', and 'X2^2' features are selected in the 'Features' section. Click the 'add' button next to 'Hidden layer' twice to create 3 hidden layers. Adjust the neuron count of the first hidden layer to 5. Adjust the neuron count of the second hidden layer to 4. Adjust the neuron count of the third hidden layer to 3. Click the 'Run/Pause' button (id: 14) to start the network. Monitor the 'Epoch' counter and wait until it reaches at least 1000. Record the final 'Test loss' and 'Training loss' values displayed.
The 'Reset the network' button is clicked. The 'Spiral' dataset is selected. The 'Activation' function is 'Tanh'. The 'Regularization' is 'L2' and 'Regularization rate' is '0.003'. Only 'X1', 'X2', 'X1^2', and 'X2^2' features are enabled. The network displays '3 Hidden layers'. The first hidden layer shows 5 neurons. The second hidden layer shows 4 neurons. The third hidden layer shows 3 neurons. The network runs for at least 1000 epochs. State the final 'Test loss' and 'Training loss' values observed after 1000 epochs.