Ethiopian Banknote Recognition Using Convolutional Neural Network and Its Prototype Development Using Embedded Platform

Table 3

Training accuracy of batch sizes 32, 64, and 128.

Optimization
	Model
	Batch size 32				Batch size 64				Batch size 128
	IV3	MN	XN	RN	IV3	MN	XN	RN	IV3	MN	XN	RN
Adam	96.38	96.96	94.80	94.78	96.30	96.96	95.44	94.85	96.46	96.96	94.61	94.83
SGD	95.75	94.56	91.59	96.56	96.30	92.65	94.12	96.42	96.46	92.93	92.28	96.47
RMSProp	96.59	96.98	92.28	94.65	96.58	96.98	92.20	94.57	96.57	96.98	96.20	94.56
Nadam	96.45	96.96	96.29	94.93	96.35	96.96	96.24	96.00	96.37	96.96	96.25	96.02
Adadelta	59.34	87.31	89.61	94.75	51.15	86.09	91.35	94.99	46.46	87.10	94.30	94.89
Adagrad	91.48	94.82	94.47	96.88	85.34	94.89	94.44	96.87	91.94	96.00	94.61	96.88