Table C1. Open in new tab... | Oxford Academic

Table C1.

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Hyperparameters used in a CNN architecture trained with noiseless spectra. These values are selected using a Bayesian optimization algorithm.

	Hyperparameters	Optimized value
Data Input	window size (ws)	259
	cnpix	5
CNN	L2	0.0
Architecture	dropout	0.0
	conv_filter_1	128
	conv_filter_2	128
	conv_filter_3	128
	conv_kernel_1	8
	conv_kernel_2	7
	conv_kernel_3	8
	dense_1	128
	dense_2_ID	256
	dense_2_N	512
	dense_2_z	256
	dense_2_b	128

	Hyperparameters	Optimized value
Data Input	window size (ws)	259
	cnpix	5
CNN	L2	0.0
Architecture	dropout	0.0
	conv_filter_1	128
	conv_filter_2	128
	conv_filter_3	128
	conv_kernel_1	8
	conv_kernel_2	7
	conv_kernel_3	8
	dense_1	128
	dense_2_ID	256
	dense_2_N	512
	dense_2_z	256
	dense_2_b	128

Table C1.

Open in new tab

Hyperparameters used in a CNN architecture trained with noiseless spectra. These values are selected using a Bayesian optimization algorithm.

	Hyperparameters	Optimized value
Data Input	window size (ws)	259
	cnpix	5
CNN	L2	0.0
Architecture	dropout	0.0
	conv_filter_1	128
	conv_filter_2	128
	conv_filter_3	128
	conv_kernel_1	8
	conv_kernel_2	7
	conv_kernel_3	8
	dense_1	128
	dense_2_ID	256
	dense_2_N	512
	dense_2_z	256
	dense_2_b	128

	Hyperparameters	Optimized value
Data Input	window size (ws)	259
	cnpix	5
CNN	L2	0.0
Architecture	dropout	0.0
	conv_filter_1	128
	conv_filter_2	128
	conv_filter_3	128
	conv_kernel_1	8
	conv_kernel_2	7
	conv_kernel_3	8
	dense_1	128
	dense_2_ID	256
	dense_2_N	512
	dense_2_z	256
	dense_2_b	128