Configure the neural network to use the 'Spiral' dataset with 'Tanh' activation, 'L2' regularization at a rate of 0.003. Enable 'X1', 'X2', 'X1^2', and 'X2^2' features. Set up 3 hidden layers with 5, 4, and 3 neurons 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. Set the first hidden layer to 5 neurons. Set the second hidden layer to 4 neurons. Set the third hidden layer to 3 neurons. 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 has 3 hidden layers. The first hidden layer has 5 neurons. The second hidden layer has 4 neurons. The third hidden layer has 3 neurons. The network runs for at least 1000 epochs. State the final 'Test loss' and 'Training loss' values.