I'm trying to understand how different activation functions affect a neural network's ability to solve the 'Exclusive OR' classification problem. Could you configure a network with two hidden layers, each having four neurons, and compare the 'Tanh' and 'ReLU' activation functions? For each, describe how the 'Test loss' and 'Training loss' behave and visually describe the output plot, noting how well the network learns to separate the data. Finally, summarize the key differences you observe between 'Tanh' and 'ReLU' for this specific problem.
Ensure the network is reset. Set the 'Problem type' to 'Classification' and select the 'Exclusive OR' dataset. Configure the network to have 2 hidden layers, each with 4 neurons. Set the 'Activation' to 'Tanh'. Run the simulation and observe the 'Test loss', 'Training loss', and the visual output plot until the network shows stable learning or clear separation. Pause the simulation and note observations for 'Tanh' regarding loss behavior and visual output. Reset the network, ensuring the architecture and dataset remain the same. Set the 'Activation' to 'ReLU'. Run the simulation and observe the 'Test loss', 'Training loss', and the visual output plot until the network shows stable learning or clear separation. Pause the simulation and note observations for 'ReLU' regarding loss behavior and visual output. Compare and contrast the learning behavior, loss convergence, and visual characteristics of the decision boundaries between 'Tanh' and 'ReLU' for the 'Exclusive OR' dataset.
The agent successfully configures the network for 'Exclusive OR' classification with 2 hidden layers and 4 neurons each for both 'Tanh' and 'ReLU' tests. For 'Tanh', the agent describes the observed behavior of 'Test loss' and 'Training loss' (e.g., initial values, convergence, stability) and the visual appearance of the decision boundary (e.g., smooth curves, successful separation). For 'ReLU', the agent describes the observed behavior of 'Test loss' and 'Training loss' (e.g., initial values, convergence, stability) and the visual appearance of the decision boundary (e.g., linear segments, successful separation). The agent explicitly compares the loss convergence speed and final loss values between 'Tanh' and 'ReLU', noting which converged faster or achieved lower loss. The agent explicitly compares the visual characteristics of the decision boundaries generated by 'Tanh' and 'ReLU' (e.g., smoothness vs. linearity, effectiveness in separating the XOR pattern).