user: Write a python code to draw neural network architecture

assistant: To draw a neural network architecture in Python, you can use the Matplotlib library. Here's an example code that shows how to draw a simple neural network with three input nodes, two hidden nodes, and one output node:

```python
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)]

# Plot the nodes
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')

# Connect the nodes with lines
for input_node in input_nodes:
    for hidden_node in hidden_nodes:
        plt.plot([input_node[0], hidden_node[0]], [input_node[1], hidden_node[1]], color='gray')
for hidden_node in hidden_nodes:
    plt.plot([hidden_node[0], output_node[0][0]], [hidden_node[1], output_node[0][1]], color='gray')

# Add labels to the nodes
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}')
plt.text(output_node[0][0]-0.15, output_node[0][1], 'Output')

# 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()
```

This code will create a simple plot of the neural network architecture. You can customize the number of nodes and layers, and adjust the coordinates of the nodes to create a more complex architecture.

user: Wow, this has already helped me a lot! Can you enrich this representation with additional helpful information, which is usually used in the context of neural networks and could be helpful in a dynamic way?

assistant: 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.