Explore the 'Regression' problem type on the TensorFlow Neural Network Playground. Configure the network to have 3 hidden layers with 5 neurons each. Use the 'Gaussian' dataset with a 'Learning rate' of 0.03. Apply 'L2' regularization with a 'Regularization rate' of 0.01. Run the network and describe how the 'Test loss' and 'Training loss' behave, and visually describe the output plot after a few epochs, noting any differences from classification problems.
Reset the network using the 'Reset the network' button (ID 12). Change the 'Problem type' to 'Regression' (ID 69). Select the 'Gaussian' dataset. Set the 'Learning rate' to '0.03' (ID 25). Add hidden layers until there are 3 hidden layers (using the '*add* button' for hidden layers). For each of the 3 hidden layers, adjust the number of neurons to 5 (using the '*add* button' for neurons within each layer). Change 'Regularization' to 'L2' (ID 48). Set the 'Regularization rate' to '0.01' (ID 55). Click the 'Run/Pause' button (ID 14) to start the training. Observe the 'Test loss' and 'Training loss' values and the visual output plot for approximately 50-100 epochs. Describe the behavior of the loss values and the visual output, specifically noting how it differs from the classification problems previously explored.
The agent successfully resets the network. The 'Problem type' is set to 'Regression'. The 'Gaussian' dataset is selected. The 'Learning rate' is set to '0.03'. The network architecture is configured with 3 hidden layers, each containing 5 neurons. The 'Regularization' is set to 'L2'. The 'Regularization rate' is set to '0.01'. The agent runs the simulation. The agent describes the observed behavior of 'Test loss' and 'Training loss' (e.g., if they decrease, converge, or fluctuate). The agent visually describes the output plot, noting its appearance for a regression problem (e.g., continuous color gradients instead of distinct boundaries, indicating predicted values). The agent highlights differences in the output visualization and loss interpretation between regression and classification problems.