Complex machine learning models in Keras can be challenging to track and understand without proper visualization. Visual tools help clarify the architecture and provide insights into how models are structured and function. Keras offers functionalities such as `plot_model` that allow for accurate representations of model layers and their connections. Other useful resources like TensorBoard help visualize performance metrics over time, while custom functions can enhance the visualization experience further. Utilizing these tools can notably improve debugging and model optimization processes.
Visualizing complex Keras models can significantly enhance understanding of model architecture, making it easier to track changes and debug code.
Tools like `plot_model`, TensorBoard, and custom functions can effectively render Keras model structures to provide graphical insights into layers and connections.
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