<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
Write a python code to draw neural network architecture<|im_end|>
<|im_start|>assistant
You can use a library such as `matplotlib` or `plotly` to create visualizations of neural networks.

Here is an example code using `matplotlib`:

```python
import matplotlib.pyplot as plt

def draw_neural_network(ax, left, right, bottom, top, layer_sizes):
    '''
    Draw a neural network cartoon using matplotlib.
    
    :usage:
        >>> fig = plt.figure(figsize=(12, 12))
        >>> draw_neural_network(fig.gca(), 0.1, 0.9, 0.1, 0.9, [4, 7, 2])
    
    :parameters:
        - ax : matplotlib.axes.AxesSubplot
            The axes on which to plot the cartoon (get e.g. by plt.gca())
        - left : float
            The center of the leftmost node(s) will be placed here
        - right : float
            The center of the rightmost node(s) will be placed here
        - bottom : float
            The center of the bottommost node(s) will be placed here
        - top : float
            The center of the topmost node(s) will be placed here
        - layer_sizes : list of int
            List of layer sizes, including input and output layer
    '''
    n_layers = len(layer_sizes)
    v_spacing = (top - bottom)/float(max(layer_sizes))
    h_spacing = (right - left)/float(len(layer_sizes) - 1)
    # Nodes
    for n, layer_size in enumerate(layer_sizes):
        layer_top = v_spacing*(layer_size - 1)/2. + (top + bottom)/2.
        for m in range(layer_size):
            circle = plt.Circle((n*h_spacing + left, layer_top - m*v_spacing), v_spacing/4.,
                                color='w', ec='k', zorder=4)
            ax.add_artist(circle)
    # Edges
    for n, (layer_size_a, layer_size_b) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):
        layer_top_a = v_spacing*(layer_size_a - 1)/2. + (top + bottom)/2.
        layer_top_b = v_spacing*(layer_size_b - 1)/2. + (top + bottom)/2.
        for m in range(layer_size_a):
            for o in range(layer_size_b):
                line = plt.Line2D([n*h_spacing + left, (n + 1)*h_spacing + left],
                                  [layer_top_a - m*v_spacing, layer_top_b - o*v_spacing], c='k')
                ax.add_artist(line)

fig = plt.figure(figsize=(12, 12))
ax = fig.gca()
ax.axis('off')
draw_neural_network(ax, 0.1, 0.9, 0.1, 0.9, [4, 7, 2])
plt.show()
```

This code will draw a simple neural network with an input layer of 4 nodes, a hidden layer of 7 nodes, and an output layer of 2 nodes.<|im_end|>
<|im_start|>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?<|im_end|>
<|im_start|>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.<|im_end|>
<|im_start|>user
Hey, thanks for giving me the code to draw a neural network using Matplotlib in Python! It looks like a solid foundation for tweaking the number of nodes and layers, and even shifting around the nodes to make more intricate architectures. I'm gonna give it a shot and let you know if I need any help.<|im_end|>
<|im_start|>assistant
You're welcome! I'm glad the code was helpful. Feel free to ask any questions or reach out if you need further assistance. Good luck with your neural network architecture!<|im_end|>