Sure, here are some additional information that could be added to the neural network architecture representation to make it more informative:

1. Activation functions: Indicate the activation function used for each layer. For example, you could use a different color or shape for nodes that use a sigmoid function compared to nodes that use a ReLU function.

2. Bias units: Indicate the presence of bias units in each layer. For example, you could use a different shape or color for bias units compared to regular nodes.

3. Weights: Indicate the weights associated with each connection between nodes. You could use line thickness or color to indicate the weight magnitude.

4. Output values: Show the output values for each node in the network. This could be indicated using the node size or color.

5. Loss function: Indicate the loss function used for training the network. For example, you could add a label or annotation to the figure indicating the loss function name.

6. Optimizer: Indicate the optimizer used for training the network. Similar to the loss function, you could add a label or annotation to the figure indicating the optimizer name.

7. Input and output data: Indicate the dimensions and type of the input and output data used by the network. For example, you could add annotations indicating the input shape (e.g., image dimensions) and output shape (e.g., number of classes).

To implement these additional features, you can modify the existing code or use a library such as TensorBoard to visualize the neural network architecture along with other useful information.
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