Keras is a powerful and handy library for creating machine learning models, particularly deep learning models. One of its features is to plot our model into a diagram for easier understanding and troubleshooting. Sometimes running keras.utils.plot_model might throw errors indicating missing software requirements, specifically pydot and graphviz. You’re expected to install both of them. Nevertheless, even after installing them, you may still get the same error message. This is due to paths and configuration settings not being properly set. With this article, we’ll walk through the process of resolving this particular issue.
The key to the solution is to realize that pydot and graphviz need to be installed and configured in a particular order and manner. The solution also depends on the operating system you’re using. Here are the general steps:
- Install graphviz
- Download and install pydot
- Set the path for graphviz installation in your Python path
Now, we’ll dig into the Python code to implement this solution.
In your Python coding environment (like Jupyter notebook or PyCharm), you can use the following code to install the necessary libraries:
!apt-get install -y graphviz !pip install pydot
Setting the path for graphviz varies depending on your operating system. On Windows, you can do it like this:
import os os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'
Where ‘C:/Program Files (x86)/Graphviz2.38/bin/’ is the location where graphviz is installed.
Now, you can call keras.utils.plot_model again and it should work:
from keras.utils import plot_model plot_model(model, to_file='model.png')
Returning ‘model.png’ will be the graphical representation of your model.
Installation and Environment Path Setting
Installing software and setting environment paths aren’t always straightforward tasks. These activities often require administrative privileges and potentially, elevated technical competencies. If simply installing the software doesn’t immediately allow you to use it, don’t be discouraged. It’s not unusual to have to manually add the newly installed software’s location to your compute’s path.
The keras.utils.plot_model API facilitates interactive visualization of your Keras-based neural network model. It becomes incredibly helpful when working with complex models, where a visual representation aids in understanding the flow and relationship between layers. Your model is plotted to an image file, which you can view at your convenience.
In conclusion, any complexities in setting up your system for keras.utils.plot_model can be navigated with patience and a structured approach. This article should serve as a guide for that purpose.