log_train.txt

10:46:1 20/7/2023
Random seed: 0

Run start at 10:46:1 20/7/2023
Input data [28, 28, 1]
Train:
#1: MNIST(/home/ggoupy/phd/SNN-PCN/datasets/mnist//X_train.bin, /home/ggoupy/phd/SNN-PCN/datasets/mnist//y_train.bin)
Test:

DefaultOnOffFilter 1:  [28, 28, 2]
Process.DefaultOnOffFilter {
	center_dev: 1.000000
	filter_size: 7
	surround_dev: 2.000000
}

FeatureScaling 2:  [28, 28, 2]
Process.FeatureScaling {
}

LatencyCoding 3:  [28, 28, 2]
InputConverter.LatencyCoding {
}

Convolution 4: conv1 [24, 24, 16]
Layer.Convolution (conv1) {
	annealing: 0.950000
	epoch: 25
	filter_height: 5
	filter_number: 16
	filter_width: 5
	lr_th: 1.000000
	min_th: 2.000000
	padding_x: 0
	padding_y: 0
	stdp: STDP.Multiplicative {
		am: 0.100000
		ap: 0.100000
		beta: 1.000000
	}
	stride_x: 1
	stride_y: 1
	t_obj: 0.750000
	th: 5.000000 [16]
	w: Gaussian(mean: 0.500000, dev: 0.010000) [5, 5, 2, 16]
	wta_infer: 0
}

Pooling 5: pool1 [6, 6, 16]
Layer.Pooling (pool1) {
	filter_height: 4
	filter_number: 16
	filter_width: 4
	padding_x: 0
	padding_y: 0
	stride_x: 4
	stride_y: 4
}


Output 1 of conv1 [24, 24, 16]: train-conv1
OutputConverter.DefaultOutput {
	max: 1.000000
	min: 0.000000
}

Output 1, Analysis: 1
Analysis.Coherence {
}

Output 2 of pool1 [6, 6, 16]: train-pool1
OutputConverter.DefaultOutput {
	max: 1.000000
	min: 0.000000
}

Output 2, Analysis: 1
Analysis.Activity {
}

Output 3 of pool1 [6, 6, 16]: train-pool1
OutputConverter.SpikeTiming {
}

Output 3, Analysis: 1
Analysis.SaveFeatureNumpy {
}

Load 60000 train samples from MNIST(/home/ggoupy/phd/SNN-PCN/datasets/mnist//X_train.bin, /home/ggoupy/phd/SNN-PCN/datasets/mnist//y_train.bin)
Save trained parameters at /home/ggoupy/phd/SNN-PCN/scripts/tmp//csnn//model//Process.FeatureScaling/
train-conv1, analysis Coherence:
===Coherence===
Mean weights: 0.245774
------
N: 120
Min: 0.00311696
Q1: 0.0992967
Q2: 0.276067
Q3: 0.465661
Max: 0.870946

Save trained parameters at /home/ggoupy/phd/SNN-PCN/scripts/tmp//csnn//model//Layer.Convolution.conv1/
train-pool1, analysis Activity:
===Activity===
* train set:
Sparsity: 0.439407
Active unit: 53.5977%
Quiet: 0%
train-pool1, analysis SaveFeatureNumpy:
Run end at 10:46:35 20/7/2023
Duration: 0h 0m 34s