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log_train.txt

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  • log_train.txt 2.19 KiB
    10:48:52 20/7/2023
    Random seed: 0
    
    Run start at 10:48:52 20/7/2023
    Input data [28, 28, 1]
    Train:
    #1: MNIST(/home/ggoupy/phd/SNN-PCN/datasets/mnist/with-val//X_train.bin, /home/ggoupy/phd/SNN-PCN/datasets/mnist/with-val//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 54000 train samples from MNIST(/home/ggoupy/phd/SNN-PCN/datasets/mnist/with-val//X_train.bin, /home/ggoupy/phd/SNN-PCN/datasets/mnist/with-val//y_train.bin)
    Save trained parameters at /home/ggoupy/phd/SNN-PCN/scripts/tmp2//csnn//model//Process.FeatureScaling/
    train-conv1, analysis Coherence:
    ===Coherence===
    Mean weights: 0.248656
    ------
    N: 120
    Min: 0.000932025
    Q1: 0.0853569
    Q2: 0.307391
    Q3: 0.466387
    Max: 0.822632
    
    Save trained parameters at /home/ggoupy/phd/SNN-PCN/scripts/tmp2//csnn//model//Layer.Convolution.conv1/
    train-pool1, analysis Activity:
    ===Activity===
    * train set:
    Sparsity: 0.47984
    Active unit: 48.2265%
    Quiet: 0%
    train-pool1, analysis SaveFeatureNumpy:
    Run end at 10:49:21 20/7/2023
    Duration: 0h 0m 29s