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Tools for calculating output dimensions and spatial relationships in neural network layers.
Distinct from Spatial Dimension Controllers: Distinct from Spatial Dimension Controllers: focuses on the calculation and analysis of dimensions rather than active modification or control of the layers.
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This project provides a collection of visual guides, technical documentation, and animation generation tools designed to explain the mathematical mechanics of neural network layer operations. It serves as an educational resource for understanding the architecture and data mapping processes involved in deep learning. The toolset distinguishes itself by programmatically generating visual representations of standard, transposed, and dilated convolution layers. By utilizing a declarative configuration model, it maps mathematical parameters—such as kernel sizes, strides, and padding—to coordinate-
Calculates output dimensions and spatial relationships for convolution layers based on kernel, stride, and padding parameters.