Configure the neural network 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. Create 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. Select the 'Spiral' dataset. Set 'Activation' to 'Tanh'. Set 'Regularization' to 'L2'. Set 'Regularization rate' to '0.003'. Ensure only 'X1', 'X2', 'X1^2', and 'X2^2' features are selected. Add hidden layers until there are 3 hidden layers. For the first hidden layer, adjust the neuron count to 5. For the second hidden layer, adjust the neuron count to 4. For the third hidden layer, adjust the neuron count to 3. Run the network until at least 1000 epochs are completed. Record the final 'Test loss' and 'Training loss' values.
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.