Sure! One way to enrich the neural network architecture representation is to include additional information such as the activation functions, biases, and weights used in the network. Here's an example code that includes this information:

```
import matplotlib.pyplot as plt
```

Define the coordinates of the nodes

```
input_nodes = [(0, 0.5), (0, 1.5), (0, 2.5)]
hidden_nodes = [(1, 1), (1, 2)]
output_node = [(2, 1.5)]
```

Define the activation functions, biases, and weights

```
activation_functions = ['sigmoid', 'tanh', 'sigmoid']
biases = [0.1, -0.3, 0.2]
weights = [[0.5, -0.4], [0.3, 0.2], [-0.1, 0.2]]
```

Plot the nodes and labels

```
for node in input_nodes:
plt.scatter(node[0], node[1], s=300, color='blue')
for node in hidden_nodes:
plt.scatter(node[0], node[1], s=300, color='orange')
for node in output_node:
plt.scatter(node[0], node[1], s=300, color='green')
for i, node in enumerate(input_nodes):
plt.text(node[0]-0.15, node[1], f'Input {i+1}')
for i, node in enumerate(hidden_nodes):
plt.text(node[0]-0.15, node[1], f'Hidden {i+1}\n{activation_functions[i]}\nBias: {biases[i]}')
plt.text(output_node[0][0]-0.15, output_node[0][1], f'Output\n{activation_functions[-1]}')
```

Connect the nodes with lines and weights

```
for i, input_node in enumerate(input_nodes):
for j, hidden_node in enumerate(hidden_nodes):
plt.plot([input_node[0], hidden_node[0]], [input_node[1], hidden_node[1]], color='gray',
linewidth=2, alpha=abs(weights[i][j]))
for j, hidden_node in enumerate(hidden_nodes):
plt.plot([hidden_node[0], output_node[0][0]], [hidden_node[1], output_node[0][1]], color='gray',
linewidth=2, alpha=abs(weights[-1][j]))
```

Set the plot limits and display the plot

```
plt.xlim(-0.5, 2.5)
plt.ylim(-0.5, 3.0)
plt.axis('off')
plt.show()
```

In this example code, we added the activation functions, biases, and weights used in the neural network. The activation function is displayed next to each hidden node, and the bias is included as a label. The weight of each connection is also shown, with thicker lines representing stronger weights and more transparent lines representing weaker weights.

This additional information can be helpful in visualizing the structure of a neural network and how it processes input data. Note that you can customize the activation functions, biases, and weights based on your specific network architecture and requirements.
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