One exact forward-count is one pass. This lesson repeats the same toy layer count over a small number of toy training steps.
Start with one forward count
This toy layer has rows=4, input features=3, and output units=2. One forward pass touches 4 times 3 times 2 = 24 multiply-add cells.
4 × 3 × 2 = 24 4\times3\times2=24 4 × 3 × 2 = 24 Training Repeats the Forward Count The same exact one-layer forward count repeats over toy steps. training repeats the forward count piece exact count meaning rows 4 displayed training rows features 3 input features per row units 2 output units in one layer one forward pass 24 4*3*2 multiply-add cells toy steps 5 repeated passes repeated total 120 24*5 visible cells one exact forward-count repeated over toy steps toy count for one layer and one repeated loop; training repeats the forward count; NOT hardware benchmark; NOT runtime estimate; NOT training quality; NOT generalization toy count for one layer and one repeated loop NOT hardware benchmark; NOT runtime estimate NOT training quality; NOT generalization
Repeat the pass over toy steps
Now repeat that same one-layer count for 5 toy training steps. The repeated total is 24 times 5 = 120 visible multiply-add cells.
24 × 5 = 120 24\times5=120 24 × 5 = 120 Training Repeats the Forward Count The same exact one-layer forward count repeats over toy steps. training repeats the forward count piece exact count meaning rows 4 displayed training rows features 3 input features per row units 2 output units in one layer one forward pass 24 4*3*2 multiply-add cells toy steps 5 repeated passes repeated total 120 24*5 visible cells one exact forward-count repeated over toy steps toy count for one layer and one repeated loop; training repeats the forward count; NOT hardware benchmark; NOT runtime estimate; NOT training quality; NOT generalization toy count for one layer and one repeated loop NOT hardware benchmark; NOT runtime estimate NOT training quality; NOT generalization
What this count is not
This is a toy count for one layer and one repeated loop. It is not a hardware benchmark, not a runtime estimate, and not a training-quality result. No claim about generalization is made.
one layer, repeated loop, exact integer count \text{one layer, repeated loop, exact integer count} one layer, repeated loop, exact integer count Training Repeats the Forward Count The same exact one-layer forward count repeats over toy steps. training repeats the forward count piece exact count meaning rows 4 displayed training rows features 3 input features per row units 2 output units in one layer one forward pass 24 4*3*2 multiply-add cells toy steps 5 repeated passes repeated total 120 24*5 visible cells one exact forward-count repeated over toy steps toy count for one layer and one repeated loop; training repeats the forward count; NOT hardware benchmark; NOT runtime estimate; NOT training quality; NOT generalization toy count for one layer and one repeated loop NOT hardware benchmark; NOT runtime estimate NOT training quality; NOT generalization