Explore how different activation functions affect the performance of a neural network on a classification problem. Specifically, configure the network to solve the 'Exclusive OR' dataset with 2 hidden layers, each having 4 neurons. Compare the 'Tanh' and 'ReLU' activation functions. For both, describe the 'Test loss' and 'Training loss' behavior, and visually describe the output plot after approximately 100 epochs, noting any differences in how the network learns to separate the data.
Reset the network using the 'Reset the network' button (ID 12). Change the 'Problem type' to 'Classification' (ID 69). Select the 'Exclusive OR' dataset. Add hidden layers until there are 2 hidden layers. For each of the 2 hidden layers, adjust the number of neurons to 4. Set the 'Activation' to 'Tanh' (ID 40). 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 100 epochs. Pause the simulation. Describe the behavior of the loss values and the visual output for 'Tanh'. Reset the network using the 'Reset the network' button (ID 12). Ensure 'Problem type' is 'Classification' and 'Exclusive OR' dataset is selected. Ensure the network architecture is 2 hidden layers with 4 neurons each. Set the 'Activation' to 'ReLU' (ID 40). 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 100 epochs. Pause the simulation. Describe the behavior of the loss values and the visual output for 'ReLU'. Compare and contrast the learning behavior and visual outputs of 'Tanh' and 'ReLU' for this 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 initial loss values, their convergence behavior, and the visual appearance of the decision boundary (e.g., smooth curves, successful separation). For 'ReLU', the agent describes the initial loss values, their convergence behavior, and the visual appearance of the decision boundary (e.g., linear segments, successful separation). The agent explicitly compares the loss convergence speed and final values between 'Tanh' and 'ReLU'. The agent explicitly compares the visual characteristics of the decision boundaries generated by 'Tanh' and 'ReLU' (e.g., smoothness, linearity, effectiveness in separating the XOR pattern).