Feature/grads #4
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This PR introduces a comprehensive gradient visualization system for CNN training by implementing automated gradient collection and analysis. The new functionality captures gradients from every layer during the backward pass and generates histograms to visualize their distributions. Throughout the training process, gradients are stored in an efficient data structure, allowing for both real-time monitoring and post-training analysis.
As training progresses, the system generates a sequence of histograms (one per epoch) that illustrate how gradient distributions evolve over time. This visualization capability enables developers to monitor gradient flow, detect potential vanishing or exploding gradient issues, and identify problematic layers that may need attention. The feature can be enabled through a simple configuration flag and has been optimized to minimize its impact on training performance.
This addition will enhance model debugging and optimization workflows by providing clear insights into training dynamics through visual representation of gradient behavior.