During training with dropout rate p=0.5, the binary mask retains neurons where (row + col) is even (0-indexed). Dropped neurons are zeroed. Surviving neurons are scaled by 1/(1-p) = 2 to compensate, clamped to [0,9]. Apply this dropout mask to the activation pattern in Grid 1.