Write a python code to draw neural network architecture
user
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.
assistant
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?
user
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.
assistant