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

Table 8

Sensitivity of batch sizes 32, 64, and 128 for classifiers and optimizers.

Optimization
	Model
	Batch size 32				Batch size 64				Batch size 128
	IV3	MN	XN	RN	IV3	MN	XN	RN	IV3	MN	XN	RN
Adam	95.98	93.87	93.52	89.96	96.1	96.26	92.44	90.83	95.9	95.84	94.96	89.38
SGD	94.56	95.56	91.59	93.56	95.3	96.10	94.42	93.6	94.46	95.7	91.92	93.5
RMSProp	95.95	96.53	89.81	96.02	95.85	96.45	95.32	96.08	95.75	96.49	95.12	95.82
Nadam	94.54	94.35	95.29	89.26	96.01	95.95	96.04	90.36	95.73	95.02	95.25	93.36
Adadelta	69.34	91.57	91.17	79.31	69.41	96.10	92.66	82.73	66.57	94.99	93.43	84.53
Adagrad	87.84	96.01	95.47	85.82	89.34	96.15	89.67	86.62	87.65	96.08	94.18	84.82