Epoch 3/20 - 1s - loss: 0.0296 - acc: 0.9911 - val_loss: 0.0237 - val acc: 0.9928
Epoch 4/20 - 1s - loss: 0.0223 - acc: 0.9930 - val_loss: 0.0227 - val acc: 0.9935
Epoch 5/20 - 1s - loss: 0.0194 - acc: 0.9940 - val_loss: 0.0235 - val acc: 0.9928

Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 6/20 - 1s - loss: 0.0170 - acc: 0.9949 - val_loss: 0.0223 - val acc: 0.9927
Epoch 7/20 - 1s - loss: 0.0143 - acc: 0.9954 - val_loss: 0.0217 - val acc: 0.9935
Epoch 8/20 - 1s - loss: 0.0127 - acc: 0.9961 - val_loss: 0.0228 - val acc: 0.9927

Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 9/20 - 1s - loss: 0.0129 - acc: 0.9959 - val_loss: 0.0223 - val acc: 0.9934

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 10/20 - 1s - loss: 0.0099 - acc: 0.9971 - val_loss: 0.0220 - val acc: 0.9935

Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.001.
2020-07-08 09:31:23 DEBUG : Metrics(Test loss & Test Accuracy): [0.021735972817311995, 0.9934999942779541]
2020-07-08 09:31:23 DEBUG : Mutant ID: 1594174369341240832
2020-07-08 09:31:23 INFO : Current scores - TrnFitness: 0.9970666766166687 TstFitness: 0.9934999942779541
Scaled_TstFitness: 0.9934999942779541

val_loss val acc loss acc lr
0 0.038622 0.9879 0.278572 0.920483 0.001
1 0.028302 0.9908 0.040911 0.987333 0.001
2 0.023730 0.9928 0.029634 0.991050 0.001
3 0.022738 0.9935 0.022293 0.992967 0.001
4 0.023473 0.9928 0.019388 0.994017 0.001
5 0.022286 0.9927 0.017044 0.994867 0.001
6 0.021736 0.9935 0.014343 0.995367 0.001
7 0.022818 0.9927 0.012741 0.996050 0.001
8 0.022330 0.9934 0.012906 0.995883 0.001
9 0.021981 0.9935 0.009880 0.997067 0.001

2020-07-08 09:31:25 DEBUG : get_Shannon(Model)
2020-07-08 09:31:25 DEBUG : Model Entropy: 8.782720565795898
2020-07-08 09:31:25 DEBUG : Model Entropy normalized: 0.0013872564469745536
2020-07-08 09:31:25 DEBUG : Layers Sum Entropy: 140.8023567199707
2020-07-08 09:31:25 DEBUG : Layer Entropy: ['8.782721', '8.232946', '15.135464', '14.727745', '11.707179', '14.107707', '14.097927', '12.107032', '17.550873', '11.704294', '12.648469']
2020-07-08 09:31:25 DEBUG : Layer Entropy normalized: ['2.1956801414489746', '2.7443154652913413', '3.7838659286499023', '0.02556900183359782', '0.18292467296123505', '3.5269267559051514', '3.524481773376465', '0.18917237222194672', '0.00373105289173775', '0.18287959694862366', '0.015057701156252906']
2020-07-08 09:31:25 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_500_2', 'DV_812_500_6', 'DV_625_750_2', 'DV_625_625_2', 'DV_844_500_7', 'DV_750_250_7', 'DV_938_750_1', 'DV_1000_500_25']
2020-07-08 09:31:25 DEBUG : out get_Shannon(Model)
2020-07-08 09:31:25 INFO : ######## End of step index : 18 of test 32 ########
2020-07-08 09:31:25 INFO : _________________________________
2020-07-08 09:31:25 INFO : _________________________________
NodeDict['From'] : [(0.0, 0.5)] Node_Type: Conv2D
NodeDict['From'] : [(0.25, 0.5)] Node_Type: SeparableConv2D
NodeDict['From'] : [(0.375, 0.5)] Node_Type: Conv2D
NodeDict['From'] : [(0.4375, 0.5)] Node_Type: Flatten
NodeDict['From'] : [(0.5, 0.5)] Node_Type: Dense
NodeDict['From'] : [(0.4375, 0.5)] Node_Type: Conv2D
NodeDict['From'] : [(0.25, 0.5)] Node_Type: Conv2D
NodeDict['From'] : [(0.25, 0.5)] Node_Type: Conv2D
NodeDict['From'] : [(0.625, 0.5)] Node_Type: Dropout
NodeDict['From'] : [(0.75, 0.5)] Node_Type: Activation
NodeDict['From'] : [(0.78125, 0.5)] Node_Type: Dense
NodeDict['From'] : [(0.8125, 0.5)] Node_Type: Activation
NodeDict['From'] : [(0.828125, 0.5)] Node_Type: Dense
NodeDict['From'] : [(0.84375, 0.5)] Node_Type: Dense
NodeDict['From'] : [(0.875, 0.5)] Node_Type: AlphaDropout
NodeDict['From'] : [(0.75, 0.5)] Node_Type: Dense
{'Age': 0,
'Coordinate': (2, 0.5),
'FromCoordinate': (1.0, 0.5),
'Id': '',
'ModelName': '',
'ModelPath': '',
'Parameters': [],
'Signal_Shape': [10],
'Type': 'SignalChannel',
'Version': 1}
Model: "model_20"

<table><thead><tr><th>Layer (type)</th><th>Output Shape</th><th>Param #</th><th>Connected to</th></tr></thead><tbody><tr><td>DV_0_500_1 (InputLayer)</td><td>(None, 28, 28, 1)</td><td>0</td><td></td></tr><tr><td>DV_250_500_1 (Conv2D)</td><td>(None, 14, 14, 48)</td><td>816</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_375_500_1 (SeparableConv2D)</td><td>(None, 7, 7, 48)</td><td>2736</td><td>DV_250_500_1[0][0]</td></tr><tr><td>dropout_16 (Dropout)</td><td>(None, 7, 7, 48)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>DV_438_500_1 (Conv2D)</td><td>(None, 3, 3, 64)</td><td>49216</td><td>dropout_16[0][0]</td></tr><tr><td>DV_500_500_4 (Flatten)</td><td>(None, 576)</td><td>0</td><td>DV_438_500_1[0][0]</td></tr><tr><td>dropout_17 (Dropout)</td><td>(None, 576)</td><td>0</td><td>DV_500_500_4[0][0]</td></tr><tr><td>DV_625_500_2 (Dense)</td><td>(None, 64)</td><td>36864</td><td>dropout_17[0][0]</td></tr><tr><td>DV_750_500_10 (Dropout)</td><td>(None, 64)</td><td>0</td><td>DV_625_500_2[0][0]</td></tr><tr><td>DV_781_500_3 (Activation)</td><td>(None, 64)</td><td>0</td><td>DV_750_500_10[0][0]</td></tr><tr><td>DV_812_500_6 (Dense)</td><td>(None, 64)</td><td>4160</td><td>DV_781_500_3[0][0]</td></tr><tr><td>DV_625_750_2 (Conv2D)</td><td>(None, 7, 7, 32)</td><td>24576</td><td>DV_250_500_1[0][0]</td></tr><tr><td>DV_625_625_2 (Conv2D)</td><td>(None, 7, 7, 32)</td><td>24576</td><td>DV_250_500_1[0][0]</td></tr><tr><td>DV_828_500_2 (Activation)</td><td>(None, 64)</td><td>0</td><td>DV_812_500_6[0][0]</td></tr><tr><td>flatten_38 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_750_2[0][0]</td></tr><tr><td>flatten_39 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>flatten_40 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>DV_844_500_7 (Dense)</td><td>(None, 84)</td><td>5376</td><td>DV_828_500_2[0][0]</td></tr><tr><td>DV_750_250_7_C (Concatenate)</td><td>(None, 4704)</td><td>0</td><td>flatten_38[0][0]<br>flatten_39[0][0]<br>flatten_40[0][0]</td></tr><tr><td>DV_875_500_11 (Dense)</td><td>(None, 48)</td><td>4032</td><td>DV_844_500_7[0][0]</td></tr><tr><td>DV_750_250_7 (Dense)</td><td>(None, 52)</td><td>244660</td><td>DV_750_250_7_C[0][0]</td></tr><tr><td>DV_938_500_5 (AlphaDropout)</td><td>(None, 48)</td><td>0</td><td>DV_875_500_11[0][0]</td></tr><tr><td>DV_938_750_2 (Dense)</td><td>(None, 64)</td><td>4096</td><td>DV_750_500_10[0][0]</td></tr><tr><td>DV_1000_500_26_C (Concatenate)</td><td>(None, 804)</td><td>0</td><td>DV_750_250_7[0][0]<br>dropout_17[0][0]<br>DV_625_500_2[0][0]<br>DV_938_500_5[0][0]<br>DV_938_750_2[0][0]</td></tr><tr><td>DV_1000_500_26 (Dense)</td><td>(None, 10)</td><td>8050</td><td>DV_1000_500_26_C[0][0]</td></tr></tbody></table>

Total params: 409,158
Trainable params: 409,158
Non-trainable params: 0

None
2020-07-08 09:31:27 INFO : Original scores - TrnFitness: 0.9940000176429749 ValFitness: False TstFitness: 0.9944000244140625 Scaled_TstFitness: 0.9944000244140625
Train on 60000 samples, validate on 10000 samples
Epoch 1/20 - 2s - loss: 0.2959 - acc: 0.9147 - val_loss: 0.0347 - val_acc: 0.9889
Epoch 2/20 - 1s - loss: 0.0404 - acc: 0.9876 - val_loss: 0.0270 - val_acc: 0.9916
Epoch 3/20 - 1s - loss: 0.0294 - acc: 0.9909 - val_loss: 0.0239 - val_acc: 0.9927
Epoch 4/20 - 1s - loss: 0.0222 - acc: 0.9931 - val_loss: 0.0230 - val_acc: 0.9929
Epoch 5/20 - 1s - loss: 0.0194 - acc: 0.9940 - val_loss: 0.0218 - val_acc: 0.9936
Epoch 6/20 - 1s - loss: 0.0159 - acc: 0.9950 - val_loss: 0.0215 - val_acc: 0.9931
Epoch 7/20 - 1s - loss: 0.0152 - acc: 0.9953 - val_loss: 0.0212 - val_acc: 0.9932
Epoch 8/20 - 1s - loss: 0.0133 - acc: 0.9958 - val_loss: 0.0207 - val_acc: 0.9940
Epoch 9/20 - 1s - loss: 0.0119 - acc: 0.9964 - val_loss: 0.0237 - val_acc: 0.9926

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 10/20 - 1s - loss: 0.0115 - acc: 0.9963 - val_loss: 0.0228 - val_acc: 0.9926

Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 11/20 - 1s - loss: 0.0114 - acc: 0.9962 - val_loss: 0.0224 - val_acc: 0.9930

Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.001.
2020-07-08 09:31:50 DEBUG : Metrics(Test loss & Test Accuracy): [0.020655150778993266, 0.9940000176429749]
2020-07-08 09:31:50 DEBUG : Mutant ID: 1594174369341240832
2020-07-08 09:31:50 INFO : Current scores - TrnFitness: 0.9962000250816345 TstFitness: 0.9940000176429749
Scaled_TstFitness: 0.9940000176429749

val_loss val_acc loss acc lr
0 0.034732 0.9889 0.295854 0.914683 0.001
1 0.026992 0.9916 0.040369 0.987650 0.001
2 0.023855 0.9927 0.029405 0.990883 0.001
3 0.022983 0.9929 0.022197 0.993117 0.001
4 0.021803 0.9936 0.019390 0.994000 0.001
5 0.021470 0.9931 0.015890 0.995017 0.001
6 0.021186 0.9932 0.015208 0.995333 0.001
7 0.020655 0.9940 0.013341 0.995833 0.001
8 0.023716 0.9926 0.011889 0.996433 0.001
9 0.022828 0.9926 0.011467 0.996267 0.001
10 0.022439 0.9930 0.011358 0.996200 0.001

2020-07-08 09:31:52 DEBUG : get_Shannon(Model)
2020-07-08 09:31:52 DEBUG : Model Entropy: 8.777618408203125
2020-07-08 09:31:52 DEBUG : Model Entropy normalized: 0.0013760179351313881
2020-07-08 09:31:52 DEBUG : Layers Sum Entropy: 152.30174922943115
2020-07-08 09:31:52 DEBUG : Layer Entropy: ['8.777618', '8.231894', '15.134873', '14.723663', '11.680616', '14.091345', '14.079248', '12.099478', '11.682067', '17.514065', '11.7081375', '12.578744']
2020-07-08 09:31:52 DEBUG : Layer Entropy normalized: ['2.1944046020507812', '2.743964513142904', '3.7837183475494385', '0.025561915503607854', '0.1825096309185028', '3.522836208343506', '3.5198121070861816', '0.18905434012413025', '0.13907222520737422', '0.0037232280588474402', '0.18293964862823486', '0.01564520389879521']
2020-07-08 09:31:52 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_500_2', 'DV_812_500_6', 'DV_625_750_2', 'DV_625_625_2', 'DV_844_500_7', 'DV_875_500_11', 'DV_750_250_7', 'DV_938_750_2', 'DV_1000_500_26']
2020-07-08 09:31:52 DEBUG : out get_Shannon(Model)
2020-07-08 09:31:52 INFO : ######## End of step index: 19 of test 32 ########