Hands-On Machine Learning with C++

Receptive field

An essential component of the convolutional neural network architecture is a reduction in the amount of data from the input to the output of the model while still increasing the channel depth. As mentioned earlier, this is usually done by choosing a convolution step (stride) or pooling layers. The receptive field determines how much of the original input from the source is processed at the output. The expansion of the receptive field allows convolutional layers to combine low-level features (lines, edges) to create higher-level features (curves, textures):

The receptive field, , of layer k can be given by the following formula: ...