## GÉNÉRATION 05

<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_3 (Conv2D)</td><td>(None, 27, 27, 10)</td><td>50</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_4 (Conv2D)</td><td>(None, 14, 14, 4)</td><td>644</td><td>DV_250_500_3[0][0]</td></tr><tr><td>DV_500_750_1 (DepthwiseConv2D)</td><td>(None, 14, 14, 10)</td><td>100</td><td>DV_250_500_3[0][0]</td></tr><tr><td>DV_625_500_3_C (Concatenate)</td><td>(None, 14, 14, 14)</td><td>0</td><td>DV_500_500_4[0][0]<br>DV_500_750_1[0][0]</td></tr><tr><td>DV_625_500_3 (DepthwiseConv2D)</td><td>(None, 14, 14, 14)</td><td>224</td><td>DV_625_500_3_C[0][0]</td></tr><tr><td>DV_688_500_1 (DepthwiseConv2D)</td><td>(None, 14, 14, 14)</td><td>126</td><td>DV_625_500_3[0][0]</td></tr><tr><td>DV_750_500_7 (Flatten)</td><td>(None, 2744)</td><td>0</td><td>DV_688_500_1[0][0]</td></tr><tr><td>DV_1000_500_7 (Dense)</td><td>(None, 10)</td><td>27450</td><td>DV_750_500_7[0][0]</td></tr></tbody><tfoot><tr><td colspan="4">Total params: 28,594</td></tr></tfoot></table>

## GÉNÉRATION 06

<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_3 (Conv2D)</td><td>(None, 27, 27, 10)</td><td>50</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_4 (Conv2D)</td><td>(None, 14, 14, 4)</td><td>644</td><td>DV_250_500_3[0][0]</td></tr><tr><td>DV_500_750_1 (DepthwiseConv2D)</td><td>(None, 14, 14, 10)</td><td>100</td><td>DV_250_500_3[0][0]</td></tr><tr><td>DV_625_500_3_C (Concatenate)</td><td>(None, 14, 14, 14)</td><td>0</td><td>DV_500_500_4[0][0]<br>DV_500_750_1[0][0]</td></tr><tr><td>DV_625_500_3 (DepthwiseConv2D)</td><td>(None, 14, 14, 14)</td><td>224</td><td>DV_625_500_3_C[0][0]</td></tr><tr><td>DV_688_500_3_BatchNorm (BatchNo)</td><td>(None, 14, 14, 14)</td><td>56</td><td>DV_625_500_3[0][0]</td></tr><tr><td>DV_688_500_3 (DepthwiseConv2D)</td><td>(None, 14, 14, 14)</td><td>126</td><td>DV_688_500_3_BatchNorm[0][0]</td></tr><tr><td>DV_750_500_8 (Flatten)</td><td>(None, 2744)</td><td>0</td><td>DV_688_500_3[0][0]</td></tr><tr><td>DV_1000_500_8 (Dense)</td><td>(None, 10)</td><td>27450</td><td>DV_750_500_8[0][0]</td></tr></tbody><tfoot><tr><td colspan="4">Total params: 28,650</td></tr></tfoot></table>
GÉNÉRATION 07

<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_3 (Conv2D)</td><td>(None, 27, 27, 10)</td><td>50</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_4 (Conv2D)</td><td>(None, 14, 14, 4)</td><td>644</td><td>DV_250_500_3[0][0]</td></tr><tr><td>DV_500_750_1 (DepthwiseConv2D)</td><td>(None, 14, 14, 10)</td><td>100</td><td>DV_250_500_3[0][0]</td></tr><tr><td>DV_625_500_3_C (Concatenate)</td><td>(None, 14, 14, 14)</td><td>0</td><td>DV_500_500_4[0][0]<br>DV_500_750_1[0][0]</td></tr><tr><td>DV_625_500_3 (DepthwiseConv2D)</td><td>(None, 14, 14, 14)</td><td>224</td><td>DV_625_500_3_C[0][0]</td></tr><tr><td>DV_656_500_1 (AveragePooling2D)</td><td>(None, 7, 7, 14)</td><td>0</td><td>DV_625_500_3[0][0]</td></tr><tr><td>DV_688_500_4_BatchNorm (BatchNo)</td><td>(None, 7, 7, 14)</td><td>56</td><td>DV_656_500_1[0][0]</td></tr><tr><td>DV_688_500_4 (DepthwiseConv2D)</td><td>(None, 7, 7, 14)</td><td>126</td><td>DV_688_500_4_BatchNorm[0][0]</td></tr><tr><td>DV_750_500_9 (Flatten)</td><td>(None, 686)</td><td>0</td><td>DV_688_500_4[0][0]</td></tr><tr><td>DV_1000_500_9 (Dense)</td><td>(None, 10)</td><td>6870</td><td>DV_750_500_9[0][0]</td></tr></tbody><tfoot><tr><td colspan="4">Total params: 8,070</td></tr></tfoot></table>
GÉNÉRATION 08

<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_3 (Conv2D)</td><td>(None, 27, 27, 10)</td><td>50</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_5 (MaxPooling2D)</td><td>(None, 14, 14, 10)</td><td>0</td><td>DV_250_500_3[0][0]</td></tr><tr><td>DV_500_750_2 (DepthwiseConv2D)</td><td>(None, 14, 14, 10)</td><td>100</td><td>DV_250_500_3[0][0]</td></tr><tr><td>DV_625_500_4_C (Concatenate)</td><td>(None, 14, 14, 20)</td><td>0</td><td>DV_500_500_5[0][0]<br>DV_500_750_2[0][0]</td></tr><tr><td>DV_625_500_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 20)</td><td>320</td><td>DV_625_500_4_C[0][0]</td></tr><tr><td>DV_656_500_2 (AveragePooling2D)</td><td>(None, 7, 7, 20)</td><td>0</td><td>DV_625_500_4[0][0]</td></tr><tr><td>DV_688_500_5_BatchNorm (BatchNo)</td><td>(None, 7, 7, 20)</td><td>80</td><td>DV_656_500_2[0][0]</td></tr><tr><td>DV_688_500_5 (DepthwiseConv2D)</td><td>(None, 7, 7, 20)</td><td>180</td><td>DV_688_500_5_BatchNorm[0][0]</td></tr><tr><td>DV_750_500_10 (Flatten)</td><td>(None, 980)</td><td>0</td><td>DV_688_500_5[0][0]</td></tr><tr><td>DV_1000_500_10 (Dense)</td><td>(None, 10)</td><td>9810</td><td>DV_750_500_10[0][0]</td></tr></tbody><tfoot><tr><td colspan="4">Total params: 10,540</td></tr></tfoot></table>
GÉNÉRATION 09

<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_3 (Conv2D)</td><td>(None, 27, 27, 10)</td><td>50</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 10)</td><td>10</td><td>DV_250_500_3[0][0]</td></tr><tr><td>DV_500_750_3 (DepthwiseConv2D)</td><td>(None, 14, 14, 10)</td><td>100</td><td>DV_250_500_3[0][0]</td></tr><tr><td>DV_625_500_5_C (Concatenate)</td><td>(None, 14, 14, 20)</td><td>0</td><td>DV_500_500_6[0][0]<br>DV_500_750_3[0][0]</td></tr><tr><td>DV_625_500_5 (DepthwiseConv2D)</td><td>(None, 14, 14, 20)</td><td>320</td><td>DV_625_500_5_C[0][0]</td></tr><tr><td>DV_656_500_4_C (Concatenate)</td><td>(None, 14, 14, 30)</td><td>0</td><td>DV_625_500_5[0][0]<br>DV_500_750_3[0][0]</td></tr><tr><td>DV_656_500_4 (AveragePooling2D)</td><td>(None, 7, 7, 30)</td><td>0</td><td>DV_656_500_4_C[0][0]</td></tr><tr><td>DV_688_500_7_BatchNorm (BatchNo)</td><td>(None, 7, 7, 30)</td><td>120</td><td>DV_656_500_4[0][0]</td></tr><tr><td>DV_688_500_7 (DepthwiseConv2D)</td><td>(None, 7, 7, 30)</td><td>270</td><td>DV_688_500_7_BatchNorm[0][0]</td></tr><tr><td>DV_750_500_12 (Flatten)</td><td>(None, 1470)</td><td>0</td><td>DV_688_500_7[0][0]</td></tr><tr><td>DV_1000_500_12 (Dense)</td><td>(None, 10)</td><td>14710</td><td>DV_750_500_12[0][0]</td></tr></tbody><tfoot><tr><td colspan="4">Total params: 15,580</td></tr></tfoot></table>
GÉNÉRATION 10

<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_3 (Conv2D)</td><td>(None, 27, 27, 10)</td><td>50</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 10)</td><td>10</td><td>DV_250_500_3[0][0]</td></tr><tr><td>DV_500_750_3 (DepthwiseConv2D)</td><td>(None, 14, 14, 10)</td><td>100</td><td>DV_250_500_3[0][0]</td></tr><tr><td>DV_625_500_5_C (Concatenate)</td><td>(None, 14, 14, 20)</td><td>0</td><td>DV_500_500_6[0][0]<br>DV_500_750_3[0][0]</td></tr><tr><td>DV_625_500_5 (DepthwiseConv2D)</td><td>(None, 14, 14, 20)</td><td>320</td><td>DV_625_500_5_C[0][0]</td></tr><tr><td>DV_656_500_4_C (Concatenate)</td><td>(None, 14, 14, 30)</td><td>0</td><td>DV_625_500_5[0][0]<br>DV_500_750_3[0][0]</td></tr><tr><td>DV_656_500_4 (AveragePooling2D)</td><td>(None, 7, 7, 30)</td><td>0</td><td>DV_656_500_4_C[0][0]</td></tr><tr><td>DV_688_500_7_BatchNorm (BatchNo)</td><td>(None, 7, 7, 30)</td><td>120</td><td>DV_656_500_4[0][0]</td></tr><tr><td>DV_688_500_7 (DepthwiseConv2D)</td><td>(None, 7, 7, 30)</td><td>270</td><td>DV_688_500_7_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_1 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>204</td><td>DV_500_500_6[0][0]</td></tr><tr><td>DV_750_500_13_C (Concatenate)</td><td>(None, 7, 7, 34)</td><td>0</td><td>DV_688_500_7[0][0]<br>DV_688_750_1[0][0]</td></tr><tr><td>DV_750_500_13 (Flatten)</td><td>(None, 1666)</td><td>0</td><td>DV_750_500_13_C[0][0]</td></tr><tr><td>DV_1000_500_13 (Dense)</td><td>(None, 10)</td><td>16670</td><td>DV_750_500_13[0][0]</td></tr></tbody></table>

Total params: 17,744
GÉNÉRATION 11

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_656_500_6_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_750_2_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_6 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_500_6_C[0][0]</td></tr><tr><td>DV_656_750_2 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_2_C[0][0]</td></tr><tr><td>DV_688_500_9_C (Concatenate)</td><td>(None, 7, 7, 180)</td><td>0</td><td>DV_656_500_6[0][0]<br>DV_656_750_2[0][0]</td></tr><tr><td>DV_688_500_9_BatchNorm (BatchNo)</td><td>(None, 7, 7, 180)</td><td>720</td><td>DV_688_500_9_C[0][0]</td></tr><tr><td>DV_688_500_9 (DepthwiseConv2D)</td><td>(None, 7, 7, 180)</td><td>1620</td><td>DV_688_500_9_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_3 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_750_500_15_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>DV_688_500_9[0][0]<br>DV_688_750_3[0][0]</td></tr><tr><td>DV_750_500_15 (Flatten)</td><td>(None, 9016)</td><td>0</td><td>DV_750_500_15_C[0][0]</td></tr><tr><td>DV_1000_500_15 (Dense)</td><td>(None, 10)</td><td>90170</td><td>DV_750_500_15[0][0]</td></tr></tbody></table>

Total params: 94,554
GÉNÉRATION 12

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_656_500_6_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_750_2_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_6 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_500_6_C[0][0]</td></tr><tr><td>DV_656_750_2 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_2_C[0][0]</td></tr><tr><td>DV_688_500_9_C (Concatenate)</td><td>(None, 7, 7, 180)</td><td>0</td><td>DV_656_500_6[0][0]<br>DV_656_750_2[0][0]</td></tr><tr><td>DV_688_500_9_BatchNorm (BatchNo)</td><td>(None, 7, 7, 180)</td><td>720</td><td>DV_688_500_9_C[0][0]</td></tr><tr><td>DV_688_500_9 (DepthwiseConv2D)</td><td>(None, 7, 7, 180)</td><td>1620</td><td>DV_688_500_9_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_3 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_750_500_15_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>DV_688_500_9[0][0]<br>DV_688_750_3[0][0]</td></tr><tr><td>DV_750_500_15 (Flatten)</td><td>(None, 9016)</td><td>0</td><td>DV_750_500_15_C[0][0]</td></tr><tr><td>DV_688_625_1 (GlobalMaxPooling2D)</td><td>(None, 90)</td><td>0</td><td>DV_656_750_2[0][0]</td></tr><tr><td>DV_1000_500_16_C (Concatenate)</td><td>(None, 9106)</td><td>0</td><td>DV_750_500_15[0][0]<br>DV_688_625_1[0][0]</td></tr><tr><td>DV_1000_500_16 (Dense)</td><td>(None, 10)</td><td>91070</td><td>DV_1000_500_16_C[0][0]</td></tr></tbody><tfoot><tr><td colspan="4">Total params: 95,454</td></tr></tfoot></table>
GENERATION 13

Changement de fonction d'activation non visible au résumé

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_656_500_6_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_750_2_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_6 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_500_6_C[0][0]</td></tr><tr><td>DV_656_750_2 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_2_C[0][0]</td></tr><tr><td>DV_688_500_9_C (Concatenate)</td><td>(None, 7, 7, 180)</td><td>0</td><td>DV_656_500_6[0][0]<br>DV_656_750_2[0][0]</td></tr><tr><td>DV_688_500_9_BatchNorm (BatchNo)</td><td>(None, 7, 7, 180)</td><td>720</td><td>DV_688_500_9_C[0][0]</td></tr><tr><td>DV_688_500_9 (DepthwiseConv2D)</td><td>(None, 7, 7, 180)</td><td>1620</td><td>DV_688_500_9_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_4 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_750_500_15_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>DV_688_500_9[0][0]<br>DV_688_750_4[0][0]</td></tr><tr><td>DV_750_500_15 (Flatten)</td><td>(None, 9016)</td><td>0</td><td>DV_750_500_15_C[0][0]</td></tr><tr><td>DV_688_625_1 (GlobalMaxPooling2)</td><td>(None, 90)</td><td>0</td><td>DV_656_750_2[0][0]</td></tr><tr><td>DV_1000_500_16_C (Concatenate)</td><td>(None, 9106)</td><td>0</td><td>DV_750_500_15[0][0]<br>DV_688_625_1[0][0]</td></tr><tr><td>DV_1000_500_16 (Dense)</td><td>(None, 10)</td><td>91070</td><td>DV_1000_500_16_C[0][0]</td></tr></tbody><tfoot><tr><td colspan="4">Total params: 95,454</td></tr></tfoot></table>
GÉNÉRATION 14

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_625_750_1 (Dense)</td><td>(None, 14, 14, 4)</td><td>120</td><td>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_7_C (Concatenate)</td><td>(None, 14, 14, 64)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_625_750_1[0][0]</td></tr><tr><td>DV_656_750_3_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_7 (AveragePooling2D)</td><td>(None, 7, 7, 64)</td><td>0</td><td>DV_656_500_7_C[0][0]</td></tr><tr><td>DV_656_750_3 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_3_C[0][0]</td></tr><tr><td>DV_688_500_10_C (Concatenate)</td><td>(None, 7, 7, 154)</td><td>0</td><td>DV_656_500_7[0][0]<br>DV_656_750_3[0][0]</td></tr><tr><td>DV_688_500_10_BatchNorm (BatchN)</td><td>(None, 7, 7, 154)</td><td>616</td><td>DV_688_500_10_C[0][0]</td></tr><tr><td>DV_688_500_10 (DepthwiseConv2D)</td><td>(None, 7, 7, 154)</td><td>1386</td><td>DV_688_500_10_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_5 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_750_500_16_C (Concatenate)</td><td>(None, 7, 7, 158)</td><td>0</td><td>DV_688_500_10[0][0]<br>DV_688_750_5[0][0]</td></tr><tr><td>DV_750_500_16 (Flatten)</td><td>(None, 7742)</td><td>0</td><td>DV_750_500_16_C[0][0]</td></tr><tr><td>DV_688_625_2 (GlobalMaxPooling2D)</td><td>(None, 90)</td><td>0</td><td>DV_656_750_3[0][0]</td></tr><tr><td>DV_1000_500_17_C (Concatenate)</td><td>(None, 7832)</td><td>0</td><td>DV_750_500_16[0][0]<br>DV_688_625_2[0][0]</td></tr><tr><td>DV_1000_500_17 (Dense)</td><td>(None, 10)</td><td>78330</td><td>DV_1000_500_17_C[0][0]</td></tr></tbody><tfoot><tr><td colspan="4">Total params: 82,496</td></tr></tfoot></table>
GÉNÉRATION 15

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_625_750_1 (Dense)</td><td>(None, 14, 14, 4)</td><td>120</td><td>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_7_C (Concatenate)</td><td>(None, 14, 14, 64)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_625_750_1[0][0]</td></tr><tr><td>DV_656_500_7 (AveragePooling2D)</td><td>(None, 7, 7, 64)</td><td>0</td><td>DV_656_500_7_C[0][0]</td></tr><tr><td>DV_656_750_3_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>dropout_1 (Dropout)</td><td>(None, 7, 7, 64)</td><td>0</td><td>DV_656_500_7[0][0]</td></tr><tr><td>DV_656_750_3 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_3_C[0][0]</td></tr><tr><td>DV_688_500_10_C (Concatenate)</td><td>(None, 7, 7, 154)</td><td>0</td><td>dropout_1[0][0]<br>DV_656_750_3[0][0]</td></tr><tr><td>DV_688_500_10_BatchNorm (BatchN)</td><td>(None, 7, 7, 154)</td><td>616</td><td>DV_688_500_10_C[0][0]</td></tr><tr><td>DV_688_500_10 (DepthwiseConv2D)</td><td>(None, 7, 7, 154)</td><td>1386</td><td>DV_688_500_10_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_5 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_750_500_16_C (Concatenate)</td><td>(None, 7, 7, 158)</td><td>0</td><td>DV_688_500_10[0][0]<br>DV_688_750_5[0][0]</td></tr><tr><td>DV_750_500_16 (Flatten)</td><td>(None, 7742)</td><td>0</td><td>DV_750_500_16_C[0][0]</td></tr><tr><td>DV_688_625_2 (GlobalMaxPooling2D)</td><td>(None, 90)</td><td>0</td><td>DV_656_750_3[0][0]</td></tr><tr><td>DV_750_250_1 (GlobalMaxPooling2D)</td><td>(None, 60)</td><td>0</td><td>DV_625_500_6[0][0]</td></tr><tr><td>DV_1000_500_18_C (Concatenate)</td><td>(None, 7892)</td><td>0</td><td>DV_750_500_16[0][0]<br>DV_688_625_2[0][0]<br>DV_750_250_1[0][0]</td></tr><tr><td>DV_1000_500_18 (Dense)</td><td>(None, 10)</td><td>78930</td><td>DV_1000_500_18_C[0][0]</td></tr></tbody><tfoot><tr><td colspan="4">Total params: 83,096</td></tr></tfoot></table>
GENERATION 16

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_625_750_1 (Dense)</td><td>(None, 14, 14, 4)</td><td>120</td><td>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8_C (Concatenate)</td><td>(None, 14, 14, 94)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_625_750_1[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8 (AveragePooling2D)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8_C[0][0]</td></tr><tr><td>DV_656_750_4_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>dropout_2 (Dropout)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8[0][0]</td></tr><tr><td>DV_656_750_4 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_4_C[0][0]</td></tr><tr><td>DV_688_500_11_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_2[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_688_500_11_BatchNorm (BatchN)</td><td>(None, 7, 7, 184)</td><td>736</td><td>DV_688_500_11_C[0][0]</td></tr><tr><td>DV_688_500_11 (DepthwiseConv2D)</td><td>(None, 7, 7, 184)</td><td>1656</td><td>DV_688_500_11_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_6 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_750_500_18_C (Concatenate)</td><td>(None, 7, 7, 188)</td><td>0</td><td>DV_688_500_11[0][0]<br>DV_688_750_6[0][0]</td></tr><tr><td>DV_750_250_3_F (Flatten)</td><td>(None, 11760)</td><td>0</td><td>DV_625_500_6[0][0]</td></tr><tr><td>DV_750_500_18 (Flatten)</td><td>(None, 9212)</td><td>0</td><td>DV_750_500_18_C[0][0]</td></tr><tr><td>DV_688_625_3 (GlobalMaxPooling2)</td><td>(None, 90)</td><td>0</td><td>DV_656_750_4[0][0]</td></tr><tr><td>DV_750_250_3 (Dense)</td><td>(None, 4)</td><td>47044</td><td>DV_750_250_3_F[0][0]</td></tr><tr><td>DV_1000_500_20_C (Concatenate)</td><td>(None, 9306)</td><td>0</td><td>DV_750_500_18[0][0]<br>DV_688_625_3[0][0]<br>DV_750_250_3[0][0]</td></tr><tr><td>DV_1000_500_20 (Dense)</td><td>(None, 10)</td><td>93070</td><td>DV_1000_500_20_C[0][0]</td></tr></tbody><tfoot><tr><td colspan="4">Total params: 144,670</td></tr></tfoot></table>
GENERATION 17

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_625_750_1 (Dense)</td><td>(None, 14, 14, 4)</td><td>120</td><td>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8_C (Concatenate)</td><td>(None, 14, 14, 94)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_625_750_1[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8 (AveragePooling2D)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8_C[0][0]</td></tr><tr><td>DV_656_750_4_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>dropout_3 (Dropout)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8[0][0]</td></tr><tr><td>DV_656_750_4 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_4_C[0][0]</td></tr><tr><td>DV_688_500_12_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_3[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_688_500_12_BatchNorm (BatchN)</td><td>(None, 7, 7, 184)</td><td>736</td><td>DV_688_500_12_C[0][0]</td></tr><tr><td>DV_688_500_12 (DepthwiseConv2D)</td><td>(None, 7, 7, 184)</td><td>1656</td><td>DV_688_500_12_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_7 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_750_500_19_C (Concatenate)</td><td>(None, 7, 7, 188)</td><td>0</td><td>DV_688_500_12[0][0]<br>DV_688_750_7[0][0]</td></tr><tr><td>DV_672_500_1 (Dense)</td><td>(None, 7, 7, 2)</td><td>182</td><td>DV_656_750_4[0][0]</td></tr><tr><td>DV_750_250_4_F (Flatten)</td><td>(None, 11760)</td><td>0</td><td>DV_625_500_6[0][0]</td></tr><tr><td>DV_750_500_19 (Flatten)</td><td>(None, 9212)</td><td>0</td><td>DV_750_500_19_C[0][0]</td></tr><tr><td>DV_688_625_4 (GlobalMaxPooling2)</td><td>(None, 2)</td><td>0</td><td>DV_672_500_1[0][0]</td></tr><tr><td>DV_750_250_4 (Dense)</td><td>(None, 4)</td><td>47044</td><td>DV_750_250_4_F[0][0]</td></tr><tr><td>DV_1000_500_21_C (Concatenate)</td><td>(None, 9218)</td><td>0</td><td>DV_750_500_19[0][0]<br>DV_688_625_4[0][0]<br>DV_750_250_4[0][0]</td></tr><tr><td>DV_1000_500_21 (Dense)</td><td>(None, 10)</td><td>92190</td><td>DV_1000_500_21_C[0][0]</td></tr></tbody></table>

Total params: 143,972
GÉNÉRATION 18

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_625_750_1 (Dense)</td><td>(None, 14, 14, 4)</td><td>120</td><td>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8_C (Concatenate)</td><td>(None, 14, 14, 94)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_625_750_1[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8 (AveragePooling2D)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8_C[0][0]</td></tr><tr><td>DV_656_750_4_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>dropout_4 (Dropout)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8[0][0]</td></tr><tr><td>DV_656_750_4 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_4_C[0][0]</td></tr><tr><td>DV_688_500_12_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_4[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_688_500_12_BatchNorm (BatchN)</td><td>(None, 7, 7, 184)</td><td>736</td><td>DV_688_500_12_C[0][0]</td></tr><tr><td>DV_375_500_1 (DepthwiseConv2D)</td><td>(None, 28, 28, 1)</td><td>16</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_688_500_12 (DepthwiseConv2D)</td><td>(None, 7, 7, 184)</td><td>1656</td><td>DV_688_500_12_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_7 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>flatten_1 (Flatten)</td><td>(None, 11760)</td><td>0</td><td>DV_625_500_6[0][0]</td></tr><tr><td>flatten_2 (Flatten)</td><td>(None, 784)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>DV_750_500_20_C (Concatenate)</td><td>(None, 7, 7, 188)</td><td>0</td><td>DV_688_500_12[0][0]<br>DV_688_750_7[0][0]</td></tr><tr><td>DV_672_500_1 (Dense)</td><td>(None, 7, 7, 2)</td><td>182</td><td>DV_656_750_4[0][0]</td></tr><tr><td>DV_750_250_5_C (Concatenate)</td><td>(None, 12544)</td><td>0</td><td>flatten_1[0][0]<br>flatten_2[0][0]</td></tr><tr><td>DV_750_500_20 (Flatten)</td><td>(None, 9212)</td><td>0</td><td>DV_750_500_20_C[0][0]</td></tr><tr><td>DV_688_625_4 (GlobalMaxPooling2)</td><td>(None, 2)</td><td>0</td><td>DV_672_500_1[0][0]</td></tr><tr><td>DV_750_250_5 (Dense)</td><td>(None, 4)</td><td>50180</td><td>DV_750_250_5_C[0][0]</td></tr><tr><td>DV_1000_500_22_C (Concatenate)</td><td>(None, 9218)</td><td>0</td><td>DV_750_500_20[0][0]<br>DV_688_625_4[0][0]<br>DV_750_250_5[0][0]</td></tr><tr><td>DV_1000_500_22 (Dense)</td><td>(None, 10)</td><td>92190</td><td>DV_1000_500_22_C[0][0]</td></tr></tbody></table>

Total params: 147,124
GÉNÉRATION 19

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_625_750_1 (Dense)</td><td>(None, 14, 14, 4)</td><td>120</td><td>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8_C (Concatenate)</td><td>(None, 14, 14, 94)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_625_750_1[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8 (AveragePooling2D)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8_C[0][0]</td></tr><tr><td>DV_656_750_4_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>dropout_5 (Dropout)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8[0][0]</td></tr><tr><td>DV_656_750_4 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_4_C[0][0]</td></tr><tr><td>DV_688_500_13_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_5[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_688_500_13_BatchNorm (BatchN)</td><td>(None, 7, 7, 184)</td><td>736</td><td>DV_688_500_13_C[0][0]</td></tr><tr><td>DV_375_500_1 (DepthwiseConv2D)</td><td>(None, 28, 28, 1)</td><td>16</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_688_500_13 (DepthwiseConv2D)</td><td>(None, 7, 7, 184)</td><td>1656</td><td>DV_688_500_13_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_8 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_664_500_1 (SeparableConv2D)</td><td>(None, 7, 7, 2)</td><td>992</td><td>DV_656_750_4[0][0]</td></tr><tr><td>flatten_3 (Flatten)</td><td>(None, 11760)</td><td>0</td><td>DV_625_500_6[0][0]</td></tr><tr><td>flatten_4 (Flatten)</td><td>(None, 784)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>DV_750_500_21_C (Concatenate)</td><td>(None, 7, 7, 188)</td><td>0</td><td>DV_688_500_13[0][0]<br>DV_688_750_8[0][0]</td></tr><tr><td>DV_672_500_2 (Dense)</td><td>(None, 7, 7, 2)</td><td>6</td><td>DV_664_500_1[0][0]</td></tr><tr><td>DV_750_250_6_C (Concatenate)</td><td>(None, 12544)</td><td>0</td><td>flatten_3[0][0]<br>flatten_4[0][0]</td></tr><tr><td>DV_750_500_21 (Flatten)</td><td>(None, 9212)</td><td>0</td><td>DV_750_500_21_C[0][0]</td></tr><tr><td>DV_688_625_5 (GlobalMaxPooling2)</td><td>(None, 2)</td><td>0</td><td>DV_672_500_2[0][0]</td></tr><tr><td>DV_750_250_6 (Dense)</td><td>(None, 4)</td><td>50180</td><td>DV_750_250_6_C[0][0]</td></tr><tr><td>DV_1000_500_23_C (Concatenate)</td><td>(None, 9218)</td><td>0</td><td>DV_750_500_21[0][0]<br>DV_688_625_5[0][0]<br>DV_750_250_6[0][0]</td></tr><tr><td>DV_1000_500_23 (Dense)</td><td>(None, 10)</td><td>92190</td><td>DV_1000_500_23_C[0][0]</td></tr></tbody></table>
---------------------------------------------------------------------------
Total params: 147,940
---------------------------------------------------------------------------
GÉNÉRATION 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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_625_750_1 (Dense)</td><td>(None, 14, 14, 4)</td><td>120</td><td>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8_C (Concatenate)</td><td>(None, 14, 14, 94)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_625_750_1[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8 (AveragePooling2D)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8_C[0][0]</td></tr><tr><td>DV_656_750_4_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>dropout_6 (Dropout)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8[0][0]</td></tr><tr><td>DV_656_750_4 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_4_C[0][0]</td></tr><tr><td>DV_688_500_14_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_6[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_375_500_1 (DepthwiseConv2D)</td><td>(None, 28, 28, 1)</td><td>16</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_688_500_14_BatchNorm (BatchN)</td><td>(None, 7, 7, 184)</td><td>736</td><td>DV_688_500_14_C[0][0]</td></tr><tr><td>DV_680_500_1 (MaxPooling2D)</td><td>(None, 14, 14, 1)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>DV_688_500_14 (DepthwiseConv2D)</td><td>(None, 7, 7, 184)</td><td>1656</td><td>DV_688_500_14_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_9 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_664_500_1 (SeparableConv2D)</td><td>(None, 7, 7, 2)</td><td>992</td><td>DV_656_750_4[0][0]</td></tr><tr><td>flatten_5 (Flatten)</td><td>(None, 11760)</td><td>0</td><td>DV_625_500_6[0][0]</td></tr><tr><td>flatten_6 (Flatten)</td><td>(None, 784)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>flatten_7 (Flatten)</td><td>(None, 196)</td><td>0</td><td>DV_680_500_1[0][0]</td></tr><tr><td>DV_750_500_24_C (Concatenate)</td><td>(None, 7, 7, 188)</td><td>0</td><td>DV_688_500_14[0][0]<br>DV_688_750_9[0][0]</td></tr><tr><td>DV_672_500_2 (Dense)</td><td>(None, 7, 7, 2)</td><td>6</td><td>DV_664_500_1[0][0]</td></tr><tr><td>DV_750_250_10_C (Concatenate)</td><td>(None, 12740)</td><td>0</td><td>flatten_5[0][0]<br>flatten_6[0][0]<br>flatten_7[0][0]</td></tr><tr><td>DV_750_500_24 (Flatten)</td><td>(None, 9212)</td><td>0</td><td>DV_750_500_24_C[0][0]</td></tr><tr><td>DV_688_625_6 (GlobalMaxPooling2D)</td><td>(None, 2)</td><td>0</td><td>DV_672_500_2[0][0]</td></tr><tr><td>DV_750_250_10 (Dense)</td><td>(None, 10)</td><td>127410</td><td>DV_750_250_10_C[0][0]</td></tr></tbody></table>
<table><tr><td>DV_688_688_2 (GlobalMaxPooling2 (None, 2))</td><td>0</td><td>DV_672_500_2[0][0]</td></tr><tr><td>DV_1000_500_26_C (Concatenate) (None, 9226)</td><td>0</td><td>DV_750_500_24[0][0]<br>DV_688_625_6[0][0]<br>DV_750_250_10[0][0]<br>DV_688_688_2[0][0]</td></tr><tr><td>DV_1000_500_26 (Dense) (None, 10)</td><td>92270</td><td>DV_1000_500_26_C[0][0]</td></tr></table>
GÉNÉRATION 21

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_625_750_1 (Dense)</td><td>(None, 14, 14, 4)</td><td>120</td><td>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8_C (Concatenate)</td><td>(None, 14, 14, 94)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_625_750_1[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8 (AveragePooling2D)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8_C[0][0]</td></tr><tr><td>DV_656_750_4_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>dropout_7 (Dropout)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8[0][0]</td></tr><tr><td>DV_656_750_4 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_4_C[0][0]</td></tr><tr><td>DV_375_500_1 (DepthwiseConv2D)</td><td>(None, 28, 28, 1)</td><td>16</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_688_500_14_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_7[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_680_500_1 (MaxPooling2D)</td><td>(None, 14, 14, 1)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>DV_688_500_14_BatchNorm (BatchN)</td><td>(None, 7, 7, 184)</td><td>736</td><td>DV_688_500_14_C[0][0]</td></tr><tr><td>flatten_8 (Flatten)</td><td>(None, 11760)</td><td>0</td><td>DV_625_500_6[0][0]</td></tr><tr><td>flatten_9 (Flatten)</td><td>(None, 784)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>flatten_10 (Flatten)</td><td>(None, 196)</td><td>0</td><td>DV_680_500_1[0][0]</td></tr><tr><td>DV_688_500_14 (DepthwiseConv2D)</td><td>(None, 7, 7, 184)</td><td>1656</td><td>DV_688_500_14_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_9 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_664_500_1 (SeparableConv2D)</td><td>(None, 7, 7, 2)</td><td>992</td><td>DV_656_750_4[0][0]</td></tr><tr><td>DV_750_250_10_C (Concatenate)</td><td>(None, 12740)</td><td>0</td><td>flatten_8[0][0]<br>flatten_9[0][0]<br>flatten_10[0][0]</td></tr><tr><td>DV_750_500_24_C (Concatenate)</td><td>(None, 7, 7, 188)</td><td>0</td><td>DV_688_500_14[0][0]<br>DV_688_750_9[0][0]</td></tr><tr><td>DV_672_500_2 (Dense)</td><td>(None, 7, 7, 2)</td><td>6</td><td>DV_664_500_1[0][0]</td></tr><tr><td>DV_750_250_10 (Dense)</td><td>(None, 10)</td><td>127410</td><td>DV_750_250_10_C[0][0]</td></tr><tr><td>DV_750_500_24 (Flatten)</td><td>(None, 9212)</td><td>0</td><td>DV_750_500_24_C[0][0]</td></tr><tr><td>DV_688_625_6 (GlobalMaxPooling2D)</td><td>(None, 2)</td><td>0</td><td>DV_672_500_2[0][0]</td></tr></tbody></table>
<table><tr><td>DV_688_688_2 (GlobalMaxPooling2 (None, 2))</td><td>0</td><td>DV_672_500_2[0][0]</td></tr><tr><td>DV_875_500_1 (Dense) (None, 6)</td><td>66</td><td>DV_750_250_10[0][0]</td></tr><tr><td>DV_1000_500_27_C (Concatenate) (None, 9222)</td><td>0</td><td>DV_750_500_24[0][0]<br>DV_688_625_6[0][0]<br>DV_688_688_2[0][0]<br>DV_875_500_1[0][0]</td></tr><tr><td>DV_1000_500_27 (Dense) (None, 10)</td><td>92230</td><td>DV_1000_500_27_C[0][0]</td></tr></table>
GÉNÉRATION 22

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_625_750_1 (Dense)</td><td>(None, 14, 14, 4)</td><td>120</td><td>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8_C (Concatenate)</td><td>(None, 14, 14, 94)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_625_750_1[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8 (AveragePooling2D)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8_C[0][0]</td></tr><tr><td>DV_656_750_4_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>dropout_8 (Dropout)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8[0][0]</td></tr><tr><td>DV_656_750_4 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_4_C[0][0]</td></tr><tr><td>DV_375_500_1 (DepthwiseConv2D)</td><td>(None, 28, 28, 1)</td><td>16</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_688_500_15_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_8[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_680_500_1 (MaxPooling2D)</td><td>(None, 14, 14, 1)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>DV_688_500_15_BatchNorm (BatchN)</td><td>(None, 7, 7, 184)</td><td>736</td><td>DV_688_500_15_C[0][0]</td></tr><tr><td>DV_664_500_1 (SeparableConv2D)</td><td>(None, 7, 7, 2)</td><td>992</td><td>DV_656_750_4[0][0]</td></tr><tr><td>flatten_11 (Flatten)</td><td>(None, 11760)</td><td>0</td><td>DV_625_500_6[0][0]</td></tr><tr><td>flatten_12 (Flatten)</td><td>(None, 784)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>flatten_13 (Flatten)</td><td>(None, 196)</td><td>0</td><td>DV_680_500_1[0][0]</td></tr><tr><td>DV_688_500_15 (DepthwiseConv2D)</td><td>(None, 7, 7, 184)</td><td>1656</td><td>DV_688_500_15_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_10 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_672_500_2 (Dense)</td><td>(None, 7, 7, 2)</td><td>6</td><td>DV_664_500_1[0][0]</td></tr><tr><td>DV_750_250_11_C (Concatenate)</td><td>(None, 12740)</td><td>0</td><td>flatten_11[0][0]<br>flatten_12[0][0]<br>flatten_13[0][0]</td></tr><tr><td>DV_750_500_25_C (Concatenate)</td><td>(None, 7, 7, 188)</td><td>0</td><td>DV_688_500_15[0][0]<br>DV_688_750_10[0][0]</td></tr><tr><td>DV_688_625_7_F (Flatten)</td><td>(None, 98)</td><td>0</td><td>DV_672_500_2[0][0]</td></tr><tr><td>DV_750_250_11 (Dense)</td><td>(None, 10)</td><td>127410</td><td>DV_750_250_11_C[0][0]</td></tr><tr><td>DV_750_500_25 (Flatten)</td><td>(None, 9212)</td><td>0</td><td>DV_750_500_25_C[0][0]</td></tr></tbody></table>
<table><tr><td>DV_688_625_7 (Dense)</td><td>(None, 6)</td><td>594</td><td>DV_688_625_7_F[0][0]</td></tr><tr><td>DV_688_688_3 (GlobalMaxPooling2</td><td>(None, 2)</td><td>0</td><td>DV_672_500_2[0][0]</td></tr><tr><td>DV_875_500_4 (Dense)</td><td>(None, 26)</td><td>286</td><td>DV_750_250_11[0][0]</td></tr><tr><td>DV_938_500_2 (GlobalAveragePool</td><td>(None, 184)</td><td>0</td><td>DV_688_500_15[0][0]</td></tr><tr><td>DV_1000_500_30_C (Concatenate)</td><td>(None, 9430)</td><td>0</td><td>DV_750_500_25[0][0]<br>DV_688_625_7[0][0]<br>DV_688_688_3[0][0]<br>DV_875_500_4[0][0]<br>DV_938_500_2[0][0]</td></tr><tr><td>DV_1000_500_30 (Dense)</td><td>(None, 10)</td><td>94310</td><td>DV_1000_500_30_C[0][0]</td></tr></table>

Total params: 228,170
GÉNÉRATION 23

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_625_750_1 (Dense)</td><td>(None, 14, 14, 4)</td><td>120</td><td>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8_C (Concatenate)</td><td>(None, 14, 14, 94)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_625_750_1[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8 (AveragePooling2D)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8_C[0][0]</td></tr><tr><td>DV_656_750_4_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>dropout_9 (Dropout)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8[0][0]</td></tr><tr><td>DV_656_750_4 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_4_C[0][0]</td></tr><tr><td>DV_375_500_1 (DepthwiseConv2D)</td><td>(None, 28, 28, 1)</td><td>16</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_688_500_16_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_9[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_660_500_1 (Conv2D)</td><td>(None, 7, 7, 2)</td><td>182</td><td>DV_656_750_4[0][0]</td></tr><tr><td>DV_680_500_2 (MaxPooling2D)</td><td>(None, 14, 14, 1)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>DV_688_500_16_BatchNorm (BatchN)</td><td>(None, 7, 7, 184)</td><td>736</td><td>DV_688_500_16_C[0][0]</td></tr><tr><td>DV_664_500_2 (SeparableConv2D)</td><td>(None, 7, 7, 2)</td><td>24</td><td>DV_660_500_1[0][0]</td></tr><tr><td>flatten_14 (Flatten)</td><td>(None, 11760)</td><td>0</td><td>DV_625_500_6[0][0]</td></tr><tr><td>flatten_15 (Flatten)</td><td>(None, 784)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>flatten_16 (Flatten)</td><td>(None, 196)</td><td>0</td><td>DV_680_500_2[0][0]</td></tr><tr><td>DV_688_500_16 (DepthwiseConv2D)</td><td>(None, 7, 7, 184)</td><td>1656</td><td>DV_688_500_16_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_11 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_672_500_3 (Dense)</td><td>(None, 7, 7, 2)</td><td>6</td><td>DV_664_500_2[0][0]</td></tr><tr><td>DV_750_250_12_C (Concatenate)</td><td>(None, 12740)</td><td>0</td><td>flatten_14[0][0]<br>flatten_15[0][0]<br>flatten_16[0][0]</td></tr><tr><td>DV_750_500_26_C (Concatenate)</td><td>(None, 7, 7, 188)</td><td>0</td><td>DV_688_500_16[0][0]<br>DV_688_750_11[0][0]</td></tr><tr><td>DV_688_625_8_F (Flatten)</td><td>(None, 98)</td><td>0</td><td>DV_672_500_3[0][0]</td></tr><tr><td>DV_750_250_12 (Dense)</td><td>(None, 10)</td><td>127410</td><td>DV_750_250_12_C[0][0]</td></tr></tbody></table>
<table><tr><td>DV_750_500_26 (Flatten)</td><td>(None, 9212)</td><td>0</td><td>DV_750_500_26_C[0][0]</td></tr><tr><td>DV_688_625_8 (Dense)</td><td>(None, 6)</td><td>594</td><td>DV_688_625_8_F[0][0]</td></tr><tr><td>DV_688_688_4 (GlobalMaxPooling2)</td><td>(None, 2)</td><td>0</td><td>DV_672_500_3[0][0]</td></tr><tr><td>DV_875_500_5 (Dense)</td><td>(None, 26)</td><td>286</td><td>DV_750_250_12[0][0]</td></tr><tr><td>DV_938_500_3 (GlobalAveragePool)</td><td>(None, 184)</td><td>0</td><td>DV_688_500_16[0][0]</td></tr><tr><td>DV_1000_500_33_C (Concatenate)</td><td>(None, 9430)</td><td>0</td><td>DV_750_500_26[0][0]<br>DV_688_625_8[0][0]<br>DV_688_688_4[0][0]<br>DV_875_500_5[0][0]<br>DV_938_500_3[0][0]</td></tr><tr><td>DV_1000_500_33 (Dense)</td><td>(None, 10)</td><td>94300</td><td>DV_1000_500_33_C[0][0]</td></tr></table>

Total params: 227,374

## GÉNÉRATION 24

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_625_750_2 (Dense)</td><td>(None, 14, 14, 4)</td><td>120</td><td>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8_C (Concatenate)</td><td>(None, 14, 14, 94)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_625_750_2[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8 (AveragePooling2D)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8_C[0][0]</td></tr><tr><td>DV_656_750_4_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>dropout_10 (Dropout)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8[0][0]</td></tr><tr><td>DV_656_750_4 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_4_C[0][0]</td></tr><tr><td>DV_375_500_1 (DepthwiseConv2D)</td><td>(None, 28, 28, 1)</td><td>16</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_688_500_17_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_10[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_660_500_1 (Conv2D)</td><td>(None, 7, 7, 2)</td><td>182</td><td>DV_656_750_4[0][0]</td></tr><tr><td>DV_680_500_3 (MaxPooling2D)</td><td>(None, 14, 14, 1)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>DV_688_500_17_BatchNorm (BatchN)</td><td>(None, 7, 7, 184)</td><td>736</td><td>DV_688_500_17_C[0][0]</td></tr><tr><td>DV_664_500_3 (Dropout)</td><td>(None, 7, 7, 2)</td><td>0</td><td>DV_660_500_1[0][0]</td></tr><tr><td>flatten_17 (Flatten)</td><td>(None, 11760)</td><td>0</td><td>DV_625_500_6[0][0]</td></tr></tbody></table>
<table><tr><td>flatten_18 (Flatten)</td><td>(None, 784)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>flatten_19 (Flatten)</td><td>(None, 196)</td><td>0</td><td>DV_680_500_3[0][0]</td></tr><tr><td>DV_688_500_17 (DepthwiseConv2D)</td><td>(None, 7, 7, 184)</td><td>1656</td><td>DV_688_500_17_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_12 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_672_500_4 (Dense)</td><td>(None, 7, 7, 2)</td><td>6</td><td>DV_664_500_3[0][0]</td></tr><tr><td>DV_750_250_13_C (Concatenate)</td><td>(None, 12740)</td><td>0</td><td>flatten_17[0][0]<br>flatten_18[0][0]<br>flatten_19[0][0]</td></tr><tr><td>DV_750_500_27_C (Concatenate)</td><td>(None, 7, 7, 188)</td><td>0</td><td>DV_688_500_17[0][0]<br>DV_688_750_12[0][0]</td></tr><tr><td>DV_688_625_9_F (Flatten)</td><td>(None, 98)</td><td>0</td><td>DV_672_500_4[0][0]</td></tr><tr><td>DV_750_250_13 (Dense)</td><td>(None, 10)</td><td>127410</td><td>DV_750_250_13_C[0][0]</td></tr><tr><td>DV_938_500_5_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_10[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_750_500_27 (Flatten)</td><td>(None, 9212)</td><td>0</td><td>DV_750_500_27_C[0][0]</td></tr><tr><td>DV_688_625_9 (Dense)</td><td>(None, 6)</td><td>594</td><td>DV_688_625_9_F[0][0]</td></tr><tr><td>DV_688_688_5 (GlobalMaxPooling2)</td><td>(None, 2)</td><td>0</td><td>DV_672_500_4[0][0]</td></tr><tr><td>DV_875_500_6 (Dense)</td><td>(None, 26)</td><td>286</td><td>DV_750_250_13[0][0]</td></tr><tr><td>DV_938_500_5 (GlobalAveragePool)</td><td>(None, 184)</td><td>0</td><td>DV_938_500_5_C[0][0]</td></tr><tr><td>DV_1000_500_35_C (Concatenate)</td><td>(None, 9430)</td><td>0</td><td>DV_750_500_27[0][0]<br>DV_688_625_9[0][0]<br>DV_688_688_5[0][0]<br>DV_875_500_6[0][0]<br>DV_938_500_5[0][0]</td></tr><tr><td>DV_1000_500_35 (Dense)</td><td>(None, 10)</td><td>94300</td><td>DV_1000_500_35_C[0][0]</td></tr></table>

Total params: 227,350

## GÉNÉRATION 25

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_625_750_2 (Dense)</td><td>(None, 14, 14, 4)</td><td>120</td><td>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8_C (Concatenate)</td><td>(None, 14, 14, 94)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_625_750_2[0][0]<br>DV_500_750_4[0][0]</td></tr></tbody></table>
<table><tbody><tr><td>DV_656_500_8 (AveragePooling2D)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8_C[0][0]</td></tr><tr><td>DV_656_750_4_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>dropout_11 (Dropout)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8[0][0]</td></tr><tr><td>DV_656_750_4 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_4_C[0][0]</td></tr><tr><td>DV_375_500_1 (DepthwiseConv2D)</td><td>(None, 28, 28, 1)</td><td>16</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_688_500_17_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_11[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_660_500_1 (Conv2D)</td><td>(None, 7, 7, 2)</td><td>182</td><td>DV_656_750_4[0][0]</td></tr><tr><td>DV_680_500_3 (MaxPooling2D)</td><td>(None, 14, 14, 1)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>DV_688_500_17_BatchNorm (BatchN)</td><td>(None, 7, 7, 184)</td><td>736</td><td>DV_688_500_17_C[0][0]</td></tr><tr><td>DV_664_500_3 (Dropout)</td><td>(None, 7, 7, 2)</td><td>0</td><td>DV_660_500_1[0][0]</td></tr><tr><td>flatten_20 (Flatten)</td><td>(None, 11760)</td><td>0</td><td>DV_625_500_6[0][0]</td></tr><tr><td>flatten_21 (Flatten)</td><td>(None, 784)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>flatten_22 (Flatten)</td><td>(None, 196)</td><td>0</td><td>DV_680_500_3[0][0]</td></tr><tr><td>DV_688_500_17 (DepthwiseConv2D)</td><td>(None, 7, 7, 184)</td><td>1656</td><td>DV_688_500_17_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_12 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_672_500_4 (Dense)</td><td>(None, 7, 7, 2)</td><td>6</td><td>DV_664_500_3[0][0]</td></tr><tr><td>DV_750_250_13_C (Concatenate)</td><td>(None, 12740)</td><td>0</td><td>flatten_20[0][0]<br>flatten_21[0][0]<br>flatten_22[0][0]</td></tr><tr><td>DV_750_500_27_C (Concatenate)</td><td>(None, 7, 7, 188)</td><td>0</td><td>DV_688_500_17[0][0]<br>DV_688_750_12[0][0]</td></tr><tr><td>DV_688_625_9_F (Flatten)</td><td>(None, 98)</td><td>0</td><td>DV_672_500_4[0][0]</td></tr><tr><td>DV_750_250_13 (Dense)</td><td>(None, 10)</td><td>127410</td><td>DV_750_250_13_C[0][0]</td></tr><tr><td>DV_938_500_6_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_11[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_750_500_27 (Flatten)</td><td>(None, 9212)</td><td>0</td><td>DV_750_500_27_C[0][0]</td></tr><tr><td>DV_688_625_9 (Dense)</td><td>(None, 6)</td><td>594</td><td>DV_688_625_9_F[0][0]</td></tr><tr><td>DV_688_688_5 (GlobalMaxPooling2)</td><td>(None, 2)</td><td>0</td><td>DV_672_500_4[0][0]</td></tr><tr><td>DV_875_500_7 (Dropout)</td><td>(None, 10)</td><td>0</td><td>DV_750_250_13[0][0]</td></tr><tr><td>DV_938_500_6 (GlobalAveragePool)</td><td>(None, 184)</td><td>0</td><td>DV_938_500_6_C[0][0]</td></tr><tr><td>DV_1000_500_36_C (Concatenate)</td><td>(None, 9414)</td><td>0</td><td>DV_750_500_27[0][0]<br>DV_688_625_9[0][0]<br>DV_688_688_5[0][0]<br>DV_875_500_7[0][0]<br>DV_938_500_6[0][0]</td></tr><tr><td>DV_1000_500_36 (Dense)</td><td>(None, 10)</td><td>94140</td><td>DV_1000_500_36_C[0][0]</td></tr></tbody></table>

Total params: 226,904
GÉNÉRATION 26

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_625_750_2 (Dense)</td><td>(None, 14, 14, 4)</td><td>120</td><td>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8_C (Concatenate)</td><td>(None, 14, 14, 94)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_625_750_2[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_750_4_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8 (AveragePooling2D)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8_C[0][0]</td></tr><tr><td>DV_656_750_4 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_4_C[0][0]</td></tr><tr><td>dropout_12 (Dropout)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8[0][0]</td></tr><tr><td>DV_660_500_1 (Conv2D)</td><td>(None, 7, 7, 2)</td><td>182</td><td>DV_656_750_4[0][0]</td></tr><tr><td>DV_375_500_1 (DepthwiseConv2D)</td><td>(None, 28, 28, 1)</td><td>16</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_688_500_19_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_12[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_664_500_3 (Dropout)</td><td>(None, 7, 7, 2)</td><td>0</td><td>DV_660_500_1[0][0]</td></tr><tr><td>DV_680_500_3 (MaxPooling2D)</td><td>(None, 14, 14, 1)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>DV_688_500_19_BatchNorm (BatchN)</td><td>(None, 7, 7, 184)</td><td>736</td><td>DV_688_500_19_C[0][0]</td></tr><tr><td>DV_672_500_4 (Dense)</td><td>(None, 7, 7, 2)</td><td>6</td><td>DV_664_500_3[0][0]</td></tr><tr><td>flatten_23 (Flatten)</td><td>(None, 11760)</td><td>0</td><td>DV_625_500_6[0][0]</td></tr><tr><td>flatten_24 (Flatten)</td><td>(None, 784)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>flatten_25 (Flatten)</td><td>(None, 196)</td><td>0</td><td>DV_680_500_3[0][0]</td></tr><tr><td>DV_688_500_19 (DepthwiseConv2D)</td><td>(None, 7, 7, 184)</td><td>1656</td><td>DV_688_500_19_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_14 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_680_250_1 (Dropout)</td><td>(None, 7, 7, 2)</td><td>0</td><td>DV_672_500_4[0][0]</td></tr><tr><td>DV_750_250_15_C (Concatenate)</td><td>(None, 12740)</td><td>0</td><td>flatten_23[0][0]<br>flatten_24[0][0]<br>flatten_25[0][0]</td></tr><tr><td>DV_750_500_29_C (Concatenate)</td><td>(None, 7, 7, 188)</td><td>0</td><td>DV_688_500_19[0][0]<br>DV_688_750_14[0][0]</td></tr><tr><td>DV_688_625_11_F (Flatten)</td><td>(None, 98)</td><td>0</td><td>DV_672_500_4[0][0]</td></tr></tbody></table>
<table><tr><td>DV_688_688_7_F (Flatten)</td><td>(None, 98)</td><td>0</td><td>DV_680_250_1[0][0]</td></tr><tr><td>DV_750_250_15 (Dense)</td><td>(None, 10)</td><td>127410</td><td>DV_750_250_15_C[0][0]</td></tr><tr><td>DV_938_500_8_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_12[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_750_500_29 (Flatten)</td><td>(None, 9212)</td><td>0</td><td>DV_750_500_29_C[0][0]</td></tr><tr><td>DV_688_625_11 (Dense)</td><td>(None, 6)</td><td>594</td><td>DV_688_625_11_F[0][0]</td></tr><tr><td>DV_688_688_7 (Dense)</td><td>(None, 4)</td><td>392</td><td>DV_688_688_7_F[0][0]</td></tr><tr><td>DV_875_500_9 (Dropout)</td><td>(None, 10)</td><td>0</td><td>DV_750_250_15[0][0]</td></tr><tr><td>DV_938_500_8 (GlobalAveragePool)</td><td>(None, 184)</td><td>0</td><td>DV_938_500_8_C[0][0]</td></tr><tr><td>DV_1000_500_38_C (Concatenate)</td><td>(None, 9416)</td><td>0</td><td>DV_750_500_29[0][0]<br>DV_688_625_11[0][0]<br>DV_688_688_7[0][0]<br>DV_875_500_9[0][0]<br>DV_938_500_8[0][0]</td></tr><tr><td>DV_1000_500_38 (Dense)</td><td>(None, 10)</td><td>94160</td><td>DV_1000_500_38_C[0][0]</td></tr></table>

## GÉNÉRATION 27

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_500_500_7 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>30</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_500_750_4 (DepthwiseConv2D)</td><td>(None, 14, 14, 30)</td><td>300</td><td>DV_250_500_5[0][0]</td></tr><tr><td>DV_625_500_6_C (Concatenate)</td><td>(None, 14, 14, 60)</td><td>0</td><td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_625_500_6 (DepthwiseConv2D)</td><td>(None, 14, 14, 60)</td><td>960</td><td>DV_625_500_6_C[0][0]</td></tr><tr><td>DV_625_750_2 (Dense)</td><td>(None, 14, 14, 4)</td><td>120</td><td>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8_C (Concatenate)</td><td>(None, 14, 14, 94)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_625_750_2[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_750_4_C (Concatenate)</td><td>(None, 14, 14, 90)</td><td>0</td><td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td></tr><tr><td>DV_656_500_8 (AveragePooling2D)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8_C[0][0]</td></tr><tr><td>DV_656_750_4 (AveragePooling2D)</td><td>(None, 7, 7, 90)</td><td>0</td><td>DV_656_750_4_C[0][0]</td></tr><tr><td>dropout_13 (Dropout)</td><td>(None, 7, 7, 94)</td><td>0</td><td>DV_656_500_8[0][0]</td></tr><tr><td>DV_660_500_1 (Conv2D)</td><td>(None, 7, 7, 2)</td><td>182</td><td>DV_656_750_4[0][0]</td></tr><tr><td>DV_375_500_1 (DepthwiseConv2D)</td><td>(None, 28, 28, 1)</td><td>16</td><td>DV_0_500_1[0][0]</td></tr><tr><td>DV_688_500_19_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_13[0][0]<br>DV_656_750_4[0][0]</td></tr></tbody></table>
<table><tr><td>DV_664_500_3 (Dropout)</td><td>(None, 7, 7, 2)</td><td>0</td><td>DV_660_500_1[0][0]</td></tr><tr><td>DV_680_500_3 (MaxPooling2D)</td><td>(None, 14, 14, 1)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>DV_688_500_19_BatchNorm (BatchN)</td><td>(None, 7, 7, 184)</td><td>736</td><td>DV_688_500_19_C[0][0]</td></tr><tr><td>DV_672_500_4 (Dense)</td><td>(None, 7, 7, 2)</td><td>6</td><td>DV_664_500_3[0][0]</td></tr><tr><td>flatten_26 (Flatten)</td><td>(None, 11760)</td><td>0</td><td>DV_625_500_6[0][0]</td></tr><tr><td>flatten_27 (Flatten)</td><td>(None, 784)</td><td>0</td><td>DV_375_500_1[0][0]</td></tr><tr><td>flatten_28 (Flatten)</td><td>(None, 196)</td><td>0</td><td>DV_680_500_3[0][0]</td></tr><tr><td>DV_688_500_19 (DepthwiseConv2D)</td><td>(None, 7, 7, 184)</td><td>1656</td><td>DV_688_500_19_BatchNorm[0][0]</td></tr><tr><td>DV_688_750_14 (SeparableConv2D)</td><td>(None, 7, 7, 4)</td><td>604</td><td>DV_500_500_7[0][0]</td></tr><tr><td>DV_680_250_1 (Dropout)</td><td>(None, 7, 7, 2)</td><td>0</td><td>DV_672_500_4[0][0]</td></tr><tr><td>DV_750_250_15_C (Concatenate)</td><td>(None, 12740)</td><td>0</td><td>flatten_26[0][0]<br>flatten_27[0][0]<br>flatten_28[0][0]</td></tr><tr><td>DV_750_500_29_C (Concatenate)</td><td>(None, 7, 7, 188)</td><td>0</td><td>DV_688_500_19[0][0]<br>DV_688_750_14[0][0]</td></tr><tr><td>DV_688_625_11_F (Flatten)</td><td>(None, 98)</td><td>0</td><td>DV_672_500_4[0][0]</td></tr><tr><td>DV_688_688_7_F (Flatten)</td><td>(None, 98)</td><td>0</td><td>DV_680_250_1[0][0]</td></tr><tr><td>DV_750_250_15 (Dense)</td><td>(None, 10)</td><td>127410</td><td>DV_750_250_15_C[0][0]</td></tr><tr><td>DV_938_500_8_C (Concatenate)</td><td>(None, 7, 7, 184)</td><td>0</td><td>dropout_13[0][0]<br>DV_656_750_4[0][0]</td></tr><tr><td>DV_875_250_1_F (Flatten)</td><td>(None, 98)</td><td>0</td><td>DV_660_500_1[0][0]</td></tr><tr><td>DV_750_500_29 (Flatten)</td><td>(None, 9212)</td><td>0</td><td>DV_750_500_29_C[0][0]</td></tr><tr><td>DV_688_625_11 (Dense)</td><td>(None, 6)</td><td>594</td><td>DV_688_625_11_F[0][0]</td></tr><tr><td>DV_688_688_7 (Dense)</td><td>(None, 4)</td><td>392</td><td>DV_688_688_7_F[0][0]</td></tr><tr><td>DV_875_500_9 (Dropout)</td><td>(None, 10)</td><td>0</td><td>DV_750_250_15[0][0]</td></tr><tr><td>DV_938_500_8 (GlobalAveragePool)</td><td>(None, 184)</td><td>0</td><td>DV_938_500_8_C[0][0]</td></tr><tr><td>DV_875_250_1 (Dense)</td><td>(None, 2)</td><td>196</td><td>DV_875_250_1_F[0][0]</td></tr><tr><td>DV_1000_500_39_C (Concatenate)</td><td>(None, 9418)</td><td>0</td><td>DV_750_500_29[0][0]<br>DV_688_625_11[0][0]<br>DV_688_688_7[0][0]<br>DV_875_500_9[0][0]<br>DV_938_500_8[0][0]<br>DV_875_250_1[0][0]</td></tr><tr><td>DV_1000_500_39 (Dense)</td><td>(None, 10)</td><td>94180</td><td>DV_1000_500_39_C[0][0]</td></tr></table>

Total params: 227,532

GÉNÉRATION 28 : CHAMPION TOPOLOGY (ID: 1576176933526592000)

<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_5 (Conv2D)</td><td>(None, 27, 27, 30)</td><td>150</td><td>DV_0_500_1[0][0]</td></tr></tbody></table>
<table>
  <tr>
    <td>DV_500_500_7 (DepthwiseConv2D)</td>
    <td>(None, 14, 14, 30)</td>
    <td>30</td>
    <td>DV_250_500_5[0][0]</td>
  </tr>
  <tr>
    <td>DV_500_750_4 (DepthwiseConv2D)</td>
    <td>(None, 14, 14, 30)</td>
    <td>300</td>
    <td>DV_250_500_5[0][0]</td>
  </tr>
  <tr>
    <td>DV_625_500_6_C (Concatenate)</td>
    <td>(None, 14, 14, 60)</td>
    <td>0</td>
    <td>DV_500_500_7[0][0]<br>DV_500_750_4[0][0]</td>
  </tr>
  <tr>
    <td>DV_625_500_6 (DepthwiseConv2D)</td>
    <td>(None, 14, 14, 60)</td>
    <td>960</td>
    <td>DV_625_500_6_C[0][0]</td>
  </tr>
  <tr>
    <td>DV_625_750_2 (Dense)</td>
    <td>(None, 14, 14, 4)</td>
    <td>120</td>
    <td>DV_500_750_4[0][0]</td>
  </tr>
  <tr>
    <td>DV_656_500_8_C (Concatenate)</td>
    <td>(None, 14, 14, 94)</td>
    <td>0</td>
    <td>DV_625_500_6[0][0]<br>DV_625_750_2[0][0]<br>DV_500_750_4[0][0]</td>
  </tr>
  <tr>
    <td>DV_656_750_4_C (Concatenate)</td>
    <td>(None, 14, 14, 90)</td>
    <td>0</td>
    <td>DV_625_500_6[0][0]<br>DV_500_750_4[0][0]</td>
  </tr>
  <tr>
    <td>DV_656_500_8 (AveragePooling2D)</td>
    <td>(None, 7, 7, 94)</td>
    <td>0</td>
    <td>DV_656_500_8_C[0][0]</td>
  </tr>
  <tr>
    <td>DV_656_750_4 (AveragePooling2D)</td>
    <td>(None, 7, 7, 90)</td>
    <td>0</td>
    <td>DV_656_750_4_C[0][0]</td>
  </tr>
  <tr>
    <td>dropout_14 (Dropout)</td>
    <td>(None, 7, 7, 94)</td>
    <td>0</td>
    <td>DV_656_500_8[0][0]</td>
  </tr>
  <tr>
    <td>DV_660_500_1 (Conv2D)</td>
    <td>(None, 7, 7, 2)</td>
    <td>182</td>
    <td>DV_656_750_4[0][0]</td>
  </tr>
  <tr>
    <td>DV_375_500_1 (DepthwiseConv2D)</td>
    <td>(None, 28, 28, 1)</td>
    <td>16</td>
    <td>DV_0_500_1[0][0]</td>
  </tr>
  <tr>
    <td>DV_688_500_19_C (Concatenate)</td>
    <td>(None, 7, 7, 184)</td>
    <td>0</td>
    <td>dropout_14[0][0]<br>DV_656_750_4[0][0]</td>
  </tr>
  <tr>
    <td>DV_664_500_3 (Dropout)</td>
    <td>(None, 7, 7, 2)</td>
    <td>0</td>
    <td>DV_660_500_1[0][0]</td>
  </tr>
  <tr>
    <td>DV_680_500_3 (MaxPooling2D)</td>
    <td>(None, 14, 14, 1)</td>
    <td>0</td>
    <td>DV_375_500_1[0][0]</td>
  </tr>
  <tr>
    <td>DV_688_500_19_BatchNorm (BatchN)</td>
    <td>(None, 7, 7, 184)</td>
    <td>736</td>
    <td>DV_688_500_19_C[0][0]</td>
  </tr>
  <tr>
    <td>DV_672_500_4 (Dense)</td>
    <td>(None, 7, 7, 2)</td>
    <td>6</td>
    <td>DV_664_500_3[0][0]</td>
  </tr>
  <tr>
    <td>flatten_29 (Flatten)</td>
    <td>(None, 11760)</td>
    <td>0</td>
    <td>DV_625_500_6[0][0]</td>
  </tr>
  <tr>
    <td>flatten_30 (Flatten)</td>
    <td>(None, 784)</td>
    <td>0</td>
    <td>DV_375_500_1[0][0]</td>
  </tr>
  <tr>
    <td>flatten_31 (Flatten)</td>
    <td>(None, 196)</td>
    <td>0</td>
    <td>DV_680_500_3[0][0]</td>
  </tr>
  <tr>
    <td>DV_688_500_19 (DepthwiseConv2D)</td>
    <td>(None, 7, 7, 184)</td>
    <td>1656</td>
    <td>DV_688_500_19_BatchNorm[0][0]</td>
  </tr>
  <tr>
    <td>DV_688_750_14 (SeparableConv2D)</td>
    <td>(None, 7, 7, 4)</td>
    <td>604</td>
    <td>DV_500_500_7[0][0]</td>
  </tr>
  <tr>
    <td>DV_680_250_1 (Dropout)</td>
    <td>(None, 7, 7, 2)</td>
    <td>0</td>
    <td>DV_672_500_4[0][0]</td>
  </tr>
  <tr>
    <td>DV_750_250_15_C (Concatenate)</td>
    <td>(None, 12740)</td>
    <td>0</td>
    <td>flatten_29[0][0]<br>flatten_30[0][0]<br>flatten_31[0][0]</td>
  </tr>
  <tr>
    <td>DV_750_500_29_C (Concatenate)</td>
    <td>(None, 7, 7, 188)</td>
    <td>0</td>
    <td>DV_688_500_19[0][0]<br>DV_688_750_14[0][0]</td>
  </tr>
  <tr>
    <td>DV_688_625_11_F (Flatten)</td>
    <td>(None, 98)</td>
    <td>0</td>
    <td>DV_672_500_4[0][0]</td>
  </tr>
  <tr>
    <td>DV_688_688_7_F (Flatten)</td>
    <td>(None, 98)</td>
    <td>0</td>
    <td>DV_680_250_1[0][0]</td>
  </tr>
  <tr>
    <td>DV_750_250_15 (Dense)</td>
    <td>(None, 10)</td>
    <td>127410</td>
    <td>DV_750_250_15_C[0][0]</td>
  </tr>
  <tr>
    <td>DV_875_250_1_F (Flatten)</td>
    <td>(None, 98)</td>
    <td>0</td>
    <td>DV_660_500_1[0][0]</td>
  </tr>
  <tr>
    <td>DV_750_500_29 (Flatten)</td>
    <td>(None, 9212)</td>
    <td>0</td>
    <td>DV_750_500_29_C[0][0]</td>
  </tr>
  <tr>
    <td>DV_688_625_11 (Dense)</td>
    <td>(None, 6)</td>
    <td>594</td>
    <td>DV_688_625_11_F[0][0]</td>
  </tr>
</table>
<table><tr><td>DV_688_688_7 (Dense)</td><td>(None, 4)</td><td>392</td><td>DV_688_688_7_F[0][0]</td></tr><tr><td>DV_875_500_9 (Dropout)</td><td>(None, 10)</td><td>0</td><td>DV_750_250_15[0][0]</td></tr><tr><td>DV_938_500_9 (GlobalAveragePool)</td><td>(None, 90)</td><td>0</td><td>DV_656_750_4[0][0]</td></tr><tr><td>DV_875_250_1 (Dense)</td><td>(None, 2)</td><td>196</td><td>DV_875_250_1_F[0][0]</td></tr><tr><td>DV_1000_500_40_C (Concatenate)</td><td>(None, 9324)</td><td>0</td><td>DV_750_500_29[0][0]<br>DV_688_625_11[0][0]<br>DV_688_688_7[0][0]<br>DV_875_500_9[0][0]<br>DV_938_500_9[0][0]<br>DV_875_250_1[0][0]</td></tr><tr><td>DV_1000_500_40 (Dense)</td><td>(None, 10)</td><td>93240</td><td>DV_1000_500_40_C[0][0]</td></tr></table>

Total params: 226,592
# ANNEXE 3 : TRACES D'UN APPRENTISSAGE PRL

Résultats du code d'un apprentissage PRL permettant de voir les évolutions et scores au cours du temps ainsi que les informations temporelles.

2020-07-08 09:24:50 INFO : ###### Start Evolutionary Step Index: 0 Test : 32/40 ########################################
NodeDict['From'] : [(0.0, 0.5)] Node_Type: Flatten
NodeDict['From'] : [(0.5, 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_1"

<table><thead><tr><td>Layer (type)</td><td>Output Shape</td><td>Param #</td></tr></thead><tbody><tr><td>DV_0_500_1 (InputLayer)</td><td>(None, 28, 28, 1)</td><td>0</td></tr><tr><td>DV_500_500_1 (Flatten)</td><td>(None, 784)</td><td>0</td></tr><tr><td>DV_1000_500_1 (Dense)</td><td>(None, 10)</td><td>7850</td></tr></tbody></table>

Total params: 7,850
Trainable params: 7,850
Non-trainable params: 0

None

2020-07-08 09:24:50 INFO : Original scores - TrnFitness: 0.08789999783039093 ValFitness: False TstFitness: 0.08789999783039093 Scaled_TstFitness: 0.9944000244140625
2020-07-08 09:24:50.779938: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: Tesla V100-PCIE-32GB major: 7 minor: 0 memoryClockRate(GHz): 1.38
pciBusID: 0000:af:00.0
2020-07-08 09:24:50.781296: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 1 with properties:
name: Tesla V100-PCIE-32GB major: 7 minor: 0 memoryClockRate(GHz): 1.38
pciBusID: 0000:d8:00.0
2020-07-08 09:24:50.781361: I tensorflow/stream NieXOR/platform/default/dso_loader.cc:42] Successfully opened
dynamic library libcudart.so.10.1
2020-07-08 09:24:50.781376: I tensorflow/stream NieXOR/platform/default/dso_loader.cc:42] Successfully opened
dynamic library libcublas.so.10
2020-07-08 09:24:50.781391: I tensorflow/stream NieXOR/platform/default/dso_loader.cc:42] Successfully opened
dynamic library libcufft.so.10
2020-07-08 09:24:50.781405: I tensorflow/stream NieXOR/platform/default/dso_loader.cc:42] Successfully opened
dynamic library libcurand.so.10
2020-07-08 09:24:50.781416: I tensorflow/stream NieXOR/platform/default/dso_loader.cc:42] Successfully opened
dynamic library libcusolver.so.10
2020-07-08 09:24:50.781429: I tensorflow/stream NieXOR/platform/default/dso_loader.cc:42] Successfully opened
dynamic library libcusparse.so.10
2020-07-08 09:24:50.781444: I tensorflow/stream NieXOR/platform/default/dso_loader.cc:42] Successfully opened
dynamic library libcudnn.so.7
2020-07-08 09:24:50.784678: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices:
0, 1
2020-07-08 09:24:50.784720: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect
Stream NieXOR with strength 1 edge matrix:
2020-07-08 09:24:50.784727: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187] 0 1
2020-07-08 09:24:50.784732: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0: N Y
2020-07-08 09:24:50.784737: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 1: Y N
2020-07-08 09:24:50.786874: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device
(/job:localhost/replica:0/task:0/device:GPU:0 with 30591 MB memory) -> physical GPU (device: 0, name: Tesla V100-PCIE-32GB, PCI bus id: 0000:af:00.0, compute capability: 7.0)
2020-07-08 09:24:50.788221: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device
(/job:localhost/replica:0 task:0 /device:GPU:1 with 30591 MB memory) -> physical GPU (device: 1, name: Tesla V100-
PCIE-32GB, pci bus id: 0000:d8:00.0, compute capability: 7.0)

Train on 60000 samples, validate on 10000 samples

Epoch 1/20 - 0s - loss: 1.4766 - acc: 0.6252 - val_loss: 0.8959 - val acc: 0.8190
Epoch 2/20 - 0s - loss: 0.7505 - acc: 0.8348 - val_loss: 0.6078 - val acc: 0.8636
Epoch 3/20 - 0s - loss: 0.5713 - acc: 0.8647 - val_loss: 0.4985 - val acc: 0.8837
Epoch 4/20 - 0s - loss: 0.4891 - acc: 0.8791 - val_loss: 0.4406 - val acc: 0.8941
Epoch 5/20 - 0s - loss: 0.4407 - acc: 0.8880 - val_loss: 0.4044 - val acc: 0.8990
Epoch 6/20 - 0s - loss: 0.4091 - acc: 0.8937 - val_loss: 0.3793 - val acc: 0.9028
Epoch 7/20 - 0s - loss: 0.3858 - acc: 0.8987 - val_loss: 0.3602 - val acc: 0.9074
Epoch 8/20 - 0s - loss: 0.3685 - acc: 0.9020 - val_loss: 0.3461 - val acc: 0.9092
Epoch 9/20 - 0s - loss: 0.3546 - acc: 0.9053 - val_loss: 0.3343 - val acc: 0.9125
Epoch 10/20 - 0s - loss: 0.3437 - acc: 0.9071 - val_loss: 0.3253 - val acc: 0.9131
Epoch 11/20 - 0s - loss: 0.3344 - acc: 0.9096 - val_loss: 0.3186 - val acc: 0.9140
Epoch 12/20 - 0s - loss: 0.3266 - acc: 0.9113 - val_loss: 0.3120 - val acc: 0.9155
Epoch 13/20 - 0s - loss: 0.3201 - acc: 0.9131 - val_loss: 0.3071 - val acc: 0.9163
Epoch 14/20 - 0s - loss: 0.3143 - acc: 0.9146 - val_loss: 0.3024 - val acc: 0.9171
Epoch 15/20 - 0s - loss: 0.3092 - acc: 0.9158 - val_loss: 0.2991 - val acc: 0.9190
Epoch 16/20 - 0s - loss: 0.3046 - acc: 0.9168 - val_loss: 0.2955 - val acc: 0.9180
Epoch 17/20 - 0s - loss: 0.3008 - acc: 0.9175 - val_loss: 0.2926 - val acc: 0.9199
Epoch 18/20 - 0s - loss: 0.2971 - acc: 0.9184 - val_loss: 0.2894 - val acc: 0.9196
Epoch 19/20 - 0s - loss: 0.2938 - acc: 0.9192 - val_loss: 0.2879 - val acc: 0.9204
Epoch 20/20 - 0s - loss: 0.2908 - acc: 0.9202 - val_loss: 0.2850 - val acc: 0.9212
2020-07-08 09:25:01 DEBUG : Metrics(Test loss & Test Accuracy): [0.28500217837691305, 0.9211999773979187]
2020-07-08 09:25:01 DEBUG : Mutant ID: 1594174369341240832
2020-07-08 09:25:01 INFO : Current scores - TrnFitness: 0.9201666712760925 TstFitness: 0.9211999773979187
Scaled_TstFitness: 0.9211999773979187

val_loss val acc loss acc lr
0 0.895883 0.8190 1.476648 0.625150 0.001
1 0.607756 0.8636 0.750542 0.834817 0.001
2 0.498501 0.8837 0.571250 0.864700 0.001
3 0.440588 0.8941 0.489086 0.879100 0.001
4 0.404427 0.8990 0.440711 0.888033 0.001
5 0.379308 0.9028 0.409121 0.893733 0.001
6 0.360159 0.9074 0.385789 0.898733 0.001
7 0.346070 0.9092 0.368479 0.902017 0.001
8 0.334342 0.9125 0.354643 0.905283 0.001
9 0.325276 0.9131 0.343730 0.907117 0.001
10 0.318648 0.9140 0.334392 0.909600 0.001
11 0.312022 0.9155 0.326649 0.911267 0.001
12 0.307080 0.9163 0.320112 0.913117 0.001
13 0.302396 0.9171 0.314294 0.914600 0.001
14 0.299052 0.9190 0.309202 0.915783 0.001
15 0.295502 0.9180 0.304649 0.916850 0.001
16 0.292641 0.9199 0.300817 0.917450 0.001
17 0.289438 0.9196 0.297085 0.918383 0.001
18 0.287936 0.9204 0.293793 0.919233 0.001
19 0.285002 0.9212 0.290779 0.920167 0.001
2020-07-08 09:25:01 DEBUG : get_Shannon(Model)
2020-07-08 09:25:01 DEBUG : Model Entropy: 12.510672569274902
2020-07-08 09:25:01 DEBUG : Model Entropy normalized: 0.015957490522034313
2020-07-08 09:25:01 DEBUG : Layers Sum Entropy: 12.510672569274902
2020-07-08 09:25:01 DEBUG : Layer Entropy: ['12.510673']
2020-07-08 09:25:01 DEBUG : Layer Entropy normalized: ['0.015957490522034313']
2020-07-08 09:25:01 DEBUG : Layer list: ['DV_1000_500_1']
2020-07-08 09:25:01 DEBUG : out get_Shannon(Model)
2020-07-08 09:25:01 INFO : ######## End of step index : 0 of test 32 ################################
2020-07-08 09:25:01 INFO : 
2020-07-08 09:25:01 INFO : ######## Start Evolutionary Step Index: 1 Test : 32/40 ################################
NodeDict['From'] : [(0.0, 0.5)] Node_Type: Conv2D
NodeDict['From'] : [(0.25, 0.5)] Node_Type: Flatten
NodeDict['From'] : [(0.5, 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_2"

<table><thead><tr><th>Layer (type)</th><th>Output Shape</th><th>Param #</th></tr></thead><tbody><tr><td>DV_0_500_1 (InputLayer)</td><td>(None, 28, 28, 1)</td><td>0</td></tr><tr><td>DV_250_500_1 (Conv2D)</td><td>(None, 14, 14, 48)</td><td>816</td></tr><tr><td>DV_500_500_2 (Flatten)</td><td>(None, 9408)</td><td>0</td></tr><tr><td>DV_1000_500_2 (Dense)</td><td>(None, 10)</td><td>94090</td></tr></tbody></table>

Total params: 94,906
Trainable params: 94,906
Non-trainable params: 0

None

2020-07-08 09:25:01 INFO : Original scores - TrnFitness: 0.9750999808311462 ValFitness: False TstFitness: 0.9750999808311462 Scaled_TstFitness: 0.9944000244140625

Train on 60000 samples, validate on 10000 samples

Epoch 1/20 - 1s - loss: 1.2057 - acc: 0.6639 - val_loss: 0.4262 - val_acc: 0.8801
Epoch 2/20 - 1s - loss: 0.3720 - acc: 0.8921 - val_loss: 0.3190 - val_acc: 0.9097
Epoch 3/20 - 1s - loss: 0.3111 - acc: 0.9089 - val_loss: 0.2848 - val_acc: 0.9177
Epoch 4/20 - 1s - loss: 0.2890 - acc: 0.9172 - val_loss: 0.2695 - val_acc: 0.9197
Epoch 5/20 - 1s - loss: 0.2730 - acc: 0.9218 - val_loss: 0.2610 - val_acc: 0.9258
Epoch 6/20 - 1s - loss: 0.2598 - acc: 0.9257 - val_loss: 0.2470 - val_acc: 0.9301
Epoch 7/20 - 1s - loss: 0.2462 - acc: 0.9295 - val_loss: 0.2370 - val_acc: 0.9330
Epoch 8/20 - 1s - loss: 0.2327 - acc: 0.9341 - val_loss: 0.2275 - val_acc: 0.9351
Epoch 9/20 - 1s - loss: 0.2175 - acc: 0.9388 - val_loss: 0.2116 - val_acc: 0.9419
Epoch 10/20 - 1s - loss: 0.2005 - acc: 0.9437 - val_loss: 0.1926 - val_acc: 0.9478
Epoch 11/20 - 1s - loss: 0.1838 - acc: 0.9484 - val_loss: 0.1775 - val_acc: 0.9524
Epoch 12/20 - 1s - loss: 0.1682 - acc: 0.9531 - val_loss: 0.1610 - val_acc: 0.9560
Epoch 13/20 - 1s - loss: 0.1535 - acc: 0.9576 - val_loss: 0.1461 - val_acc: 0.9599
Epoch 14/20 - 1s - loss: 0.1391 - acc: 0.9617 - val_loss: 0.1378 - val_acc: 0.9625
Epoch 15/20 - 1s - loss: 0.1275 - acc: 0.9654 - val_loss: 0.1243 - val_acc: 0.9663
Epoch 16/20 - 1s - loss: 0.1173 - acc: 0.9671 - val_loss: 0.1179 - val_acc: 0.9682
Epoch 17/20 - 1s - loss: 0.1096 - acc: 0.9696 - val_loss: 0.1088 - val_acc: 0.9704
Epoch 18/20 - 1s - loss: 0.1023 - acc: 0.9721 - val_loss: 0.1019 - val_acc: 0.9714
Epoch 19/20 - 1s - loss: 0.0949 - acc: 0.9745 - val_loss: 0.0980 - val_acc: 0.9713
Epoch 20/20 - 1s - loss: 0.0912 - acc: 0.9748 - val_loss: 0.0971 - val_acc: 0.9710

2020-07-08 09:25:12 DEBUG : Metrics(Test loss & Test Accuracy): [0.09713027883917093, 0.9710000157356262]

2020-07-08 09:25:12 DEBUG : Mutant ID: 1594174369341240832

2020-07-08 09:25:12 INFO : Current scores - TrnFitness: 0.9748166799545288 TstFitness: 0.9710000157356262

Scaled_TstFitness: 0.9710000157356262

<table><thead><tr><th></th><th>val_loss</th><th>val_acc</th><th>loss</th><th>acc</th><th>lr</th></tr></thead><tbody><tr><td>0</td><td>0.426165</td><td>0.8801</td><td>1.205655</td><td>0.663850</td><td>0.001</td></tr><tr><td>1</td><td>0.319043</td><td>0.9097</td><td>0.372022</td><td>0.892083</td><td>0.001</td></tr><tr><td>2</td><td>0.284757</td><td>0.9177</td><td>0.311134</td><td>0.908883</td><td>0.001</td></tr><tr><td>3</td><td>0.269461</td><td>0.9197</td><td>0.288989</td><td>0.917167</td><td>0.001</td></tr><tr><td>4</td><td>0.261022</td><td>0.9258</td><td>0.272999</td><td>0.921817</td><td>0.001</td></tr><tr><td>5</td><td>0.247023</td><td>0.9301</td><td>0.259835</td><td>0.925683</td><td>0.001</td></tr><tr><td>6</td><td>0.237004</td><td>0.9330</td><td>0.246189</td><td>0.929550</td><td>0.001</td></tr><tr><td>7</td><td>0.227505</td><td>0.9351</td><td>0.232731</td><td>0.934100</td><td>0.001</td></tr><tr><td>8</td><td>0.211633</td><td>0.9419</td><td>0.217525</td><td>0.938767</td><td>0.001</td></tr><tr><td>9</td><td>0.192566</td><td>0.9478</td><td>0.200489</td><td>0.943733</td><td>0.001</td></tr><tr><td>10</td><td>0.177454</td><td>0.9524</td><td>0.183781</td><td>0.948417</td><td>0.001</td></tr><tr><td>11</td><td>0.161039</td><td>0.9560</td><td>0.168198</td><td>0.953100</td><td>0.001</td></tr><tr><td>12</td><td>0.146100</td><td>0.9599</td><td>0.153501</td><td>0.957583</td><td>0.001</td></tr><tr><td>13</td><td>0.137755</td><td>0.9625</td><td>0.139062</td><td>0.961700</td><td>0.001</td></tr><tr><td>14</td><td>0.124316</td><td>0.9663</td><td>0.127459</td><td>0.965383</td><td>0.001</td></tr><tr><td>15</td><td>0.117876</td><td>0.9682</td><td>0.117339</td><td>0.967133</td><td>0.001</td></tr><tr><td>16</td><td>0.108753</td><td>0.9704</td><td>0.109568</td><td>0.969650</td><td>0.001</td></tr><tr><td>17</td><td>0.101853</td><td>0.9714</td><td>0.102294</td><td>0.972067</td><td>0.001</td></tr><tr><td>18</td><td>0.098033</td><td>0.9713</td><td>0.094882</td><td>0.974533</td><td>0.001</td></tr><tr><td>19</td><td>0.097130</td><td>0.9710</td><td>0.091184</td><td>0.974817</td><td>0.001</td></tr></tbody></table>

2020-07-08 09:25:13 DEBUG : get_Shannon(Model)

2020-07-08 09:25:13 DEBUG : Model Entropy: 8.944354057312012

2020-07-08 09:25:13 DEBUG : Model Entropy normalized: 0.0009503138607428827

2020-07-08 09:25:13 DEBUG : Layers Sum Entropy: 24.477349281311035
2020-07-08 09:25:13 DEBUG : Layer Entropy: ['8.944354', '15.532995']
2020-07-08 09:25:13 DEBUG : Layer Entropy normalized: ['2.236088514328003', '0.0016510411590135016']
2020-07-08 09:25:13 DEBUG : Layer list: ['DV_250_500_1', 'DV_1000_500_2']
2020-07-08 09:25:13 DEBUG : out get_Shannon(Model)
2020-07-08 09:25:13 INFO : ###### End of step index : 1 of test 32 ###########################################
2020-07-08 09:25:13 INFO : _________________________________________________________________
2020-07-08 09:25:13 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: Flatten
NodeDict['From’: [(0.5, 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_3"

<table><thead><tr><td>Layer (type)</td><td>Output Shape</td><td>Param #</td></tr></thead><tbody><tr><td>DV_0_500_1 (InputLayer)</td><td>(None, 28, 28, 1)</td><td>0</td></tr><tr><td>DV_250_500_1 (Conv2D)</td><td>(None, 14, 14, 48)</td><td>816</td></tr><tr><td>DV_375_500_1 (SeparableConv2)</td><td>(None, 7, 7, 48)</td><td>2736</td></tr><tr><td>DV_500_500_3 (Flatten)</td><td>(None, 2352)</td><td>0</td></tr><tr><td>DV_1000_500_3 (Dense)</td><td>(None, 10)</td><td>23530</td></tr></tbody></table>

Total params: 27,082
Trainable params: 27,082
Non-trainable params: 0

None
2020-07-08 09:25:13 INFO : Original scores - TrnFitness: 0.9843000173568726 ValFitness: False TstFitness: 0.9843000173568726 Scaled_TstFitness: 0.9944000244140625
Train on 60000 samples, validate on 10000 samples
Epoch 1/20 - 1s - loss: 1.1689 - acc: 0.7333 - val_loss: 0.4161 - val acc: 0.8785
Epoch 2/20 - 1s - loss: 0.3538 - acc: 0.8971 - val_loss: 0.2891 - val acc: 0.9179
Epoch 3/20 - 1s - loss: 0.2776 - acc: 0.9200 - val_loss: 0.2321 - val acc: 0.9324
Epoch 4/20 - 1s - loss: 0.2213 - acc: 0.9359 - val_loss: 0.1802 - val acc: 0.9476
Epoch 5/20 - 1s - loss: 0.1726 - acc: 0.9503 - val_loss: 0.1390 - val acc: 0.9582
Epoch 6/20 - 1s - loss: 0.1393 - acc: 0.9597 - val_loss: 0.1154 - val acc: 0.9667
Epoch 7/20 - 1s - loss: 0.1182 - acc: 0.9659 - val_loss: 0.0983 - val acc: 0.9701
Epoch 8/20 - 1s - loss: 0.1045 - acc: 0.9694 - val_loss: 0.0870 - val acc: 0.9737
Epoch 9/20 - 1s - loss: 0.0958 - acc: 0.9721 - val_loss: 0.0782 - val acc: 0.9761
Epoch 10/20 - 1s - loss: 0.0884 - acc: 0.9739 - val_loss: 0.0746 - val acc: 0.9765
Epoch 11/20 - 1s - loss: 0.0821 - acc: 0.9760 - val_loss: 0.0670 - val acc: 0.9795
Epoch 12/20 - 1s - loss: 0.0772 - acc: 0.9772 - val_loss: 0.0640 - val acc: 0.9803
Epoch 13/20 - 1s - loss: 0.0745 - acc: 0.9778 - val_loss: 0.0649 - val acc: 0.9799
Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 14/20 - 1s - loss: 0.0701 - acc: 0.9792 - val_loss: 0.0588 - val acc: 0.9815
Epoch 15/20 - 1s - loss: 0.0664 - acc: 0.9798 - val_loss: 0.0594 - val acc: 0.9819
Epoch 00015: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 16/20 - 1s - loss: 0.0643 - acc: 0.9806 - val_loss: 0.0607 - val acc: 0.9810
Epoch 00016: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 17/20 - 1s - loss: 0.0602 - acc: 0.9819 - val_loss: 0.0555 - val acc: 0.9822
Epoch 18/20 - 1s - loss: 0.0590 - acc: 0.9822 - val_loss: 0.0572 - val acc: 0.9827
Epoch 00018: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 19/20 - 1s - loss: 0.0561 - acc: 0.9828 - val_loss: 0.0503 - val acc: 0.9836
Epoch 20/20 - 1s - loss: 0.0543 - acc: 0.9832 - val_loss: 0.0511 - val acc: 0.9835
Epoch 00020: ReduceLROnPlateau reducing learning rate to 0.001.
2020-07-08 09:25:32 DEBUG : Metrics(Test loss & Test Accuracy): [0.051100625203805976, 0.9835000038146973]
2020-07-08 09:25:32 DEBUG : Mutant ID: 1594174369341240832
2020-07-08 09:25:32 INFO : Current scores - TrnFitness: 0.9832166433334351 TstFitness: 0.9835000038146973
Scaled_TstFitness: 0.9835000038146973

| val_loss | val_acc | loss | acc | lr |
|---|---|---|---|---|
| 0 | 0.416119 | 0.8785 | 1.168881 | 0.733283 | 0.001 |
| 1 | 0.289134 | 0.9179 | 0.353817 | 0.897150 | 0.001 |
| 2 | 0.232117 | 0.9324 | 0.277573 | 0.919967 | 0.001 |
| 3 | 0.180238 | 0.9476 | 0.221333 | 0.935850 | 0.001 |
| 4 | 0.138960 | 0.9582 | 0.172635 | 0.950283 | 0.001 |
| 5 | 0.115416 | 0.9667 | 0.139302 | 0.959667 | 0.001 |
| 6 | 0.098306 | 0.9701 | 0.118178 | 0.965900 | 0.001 |
| 7 | 0.086969 | 0.9737 | 0.104534 | 0.969417 | 0.001 |
| 8 | 0.078178 | 0.9761 | 0.095839 | 0.972133 | 0.001 |
| 9 | 0.074620 | 0.9765 | 0.088383 | 0.973850 | 0.001 |
| 10 | 0.066989 | 0.9795 | 0.082075 | 0.976017 | 0.001 |
| 11 | 0.064041 | 0.9803 | 0.077204 | 0.977183 | 0.001 |
| 12 | 0.064888 | 0.9799 | 0.074477 | 0.977750 | 0.001 |
| 13 | 0.058757 | 0.9815 | 0.070093 | 0.979217 | 0.001 |
| 14 | 0.059421 | 0.9819 | 0.066439 | 0.979833 | 0.001 |
| 15 | 0.060729 | 0.9810 | 0.064279 | 0.980600 | 0.001 |
| 16 | 0.055475 | 0.9822 | 0.060199 | 0.981900 | 0.001 |
| 17 | 0.057240 | 0.9827 | 0.058956 | 0.982150 | 0.001 |
| 18 | 0.050294 | 0.9836 | 0.056140 | 0.982767 | 0.001 |
| 19 | 0.051101 | 0.9835 | 0.054313 | 0.983217 | 0.001 |

2020-07-08 09:25:32 DEBUG : get_Shannon(Model)
2020-07-08 09:25:32 DEBUG : Model Entropy: 8.910970687866211
2020-07-08 09:25:32 DEBUG : Model Entropy normalized: 0.003777435645555833
2020-07-08 09:25:32 DEBUG : Layers Sum Entropy: 31.317404747009277
2020-07-08 09:25:32 DEBUG : Layer Entropy: ['8.910971', '8.368004', '14.03843']
2020-07-08 09:25:32 DEBUG : Layer Entropy normalized: ['2.2277426719665527', '2.7893346150716147', '0.005968720329051115']
2020-07-08 09:25:32 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_1000_500_3']
2020-07-08 09:25:32 DEBUG : out get_Shannon(Model)
2020-07-08 09:25:32 INFO : ######## End of step index : 2 of test 32 ########
2020-07-08 09:25:32 INFO : _________
2020-07-08 09:25:32 INFO : ######## Start Evolutionary Step Index: 3 Test : 32/40 ########
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: LeakyReLU
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_4"

<table><thead><tr><td>Layer (type)</td><td>Output Shape</td><td>Param #</td></tr></thead><tbody><tr><td>DV_0_500_1 (InputLayer)</td><td>(None, 28, 28, 1)</td><td>0</td></tr><tr><td>DV_250_500_1 (Conv2D)</td><td>(None, 14, 14, 48)</td><td>816</td></tr><tr><td>DV_375_500_1 (SeparableConv2</td><td>(None, 7, 7, 48)</td><td>2736</td></tr><tr><td>DV_438_500_1 (Conv2D)</td><td>(None, 3, 3, 64)</td><td>49216</td></tr><tr><td>DV_500_500_4 (Flatten)</td><td>(None, 576)</td><td>0</td></tr><tr><td>DV_750_500_2 (LeakyReLU)</td><td>(None, 576)</td><td>0</td></tr></tbody></table>
DV_1000_500_5 (Dense) (None, 10)

5770

Total params: 58,538

Trainable params: 58,538

Non-trainable params: 0

None

2020-07-08 09:25:32 INFO : Original scores - TrnFitness: 0.9896000027656555 ValFitness: False TstFitness: 0.9896000027656555 Scaled_TstFitness: 0.9944000244140625

Train on 60000 samples, validate on 10000 samples

Epoch 1/20 - 1s - loss: 0.4570 - acc: 0.8758 - val_loss: 0.1223 - val acc: 0.9636

Epoch 2/20 - 1s - loss: 0.1107 - acc: 0.9678 - val_loss: 0.0783 - val acc: 0.9758

Epoch 3/20 - 1s - loss: 0.0808 - acc: 0.9758 - val_loss: 0.0617 - val acc: 0.9810

Epoch 4/20 - 1s - loss: 0.0644 - acc: 0.9807 - val_loss: 0.0515 - val acc: 0.9833

Epoch 5/20 - 1s - loss: 0.0562 - acc: 0.9832 - val_loss: 0.0491 - val acc: 0.9845

Epoch 6/20 - 1s - loss: 0.0477 - acc: 0.9855 - val_loss: 0.0450 - val acc: 0.9852

Epoch 7/20 - 1s - loss: 0.0444 - acc: 0.9867 - val_loss: 0.0438 - val acc: 0.9846

Epoch 8/20 - 1s - loss: 0.0411 - acc: 0.9870 - val_loss: 0.0381 - val acc: 0.9874

Epoch 9/20 - 1s - loss: 0.0373 - acc: 0.9887 - val_loss: 0.0398 - val acc: 0.9865

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 10/20 - 1s - loss: 0.0352 - acc: 0.9891 - val_loss: 0.0373 - val acc: 0.9879

Epoch 11/20 - 1s - loss: 0.0329 - acc: 0.9898 - val_loss: 0.0345 - val acc: 0.9887

Epoch 12/20 - 1s - loss: 0.0297 - acc: 0.9907 - val_loss: 0.0369 - val acc: 0.9878

Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 13/20 - 1s - loss: 0.0279 - acc: 0.9915 - val_loss: 0.0382 - val acc: 0.9881

Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 14/20 - 1s - loss: 0.0255 - acc: 0.9924 - val_loss: 0.0390 - val acc: 0.9873

Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.001.

2020-07-08 09:25:47 DEBUG : Metrics(Test loss & Test Accuracy): [0.034473197813367, 0.9886999726295471]

2020-07-08 09:25:47 DEBUG : Mutant ID: 1594174369341240832

2020-07-08 09:25:47 INFO : Current scores - TrnFitness: 0.9923999905586243 TstFitness: 0.9886999726295471

Scaled_TstFitness: 0.9886999726295471

<table><thead><tr><th></th><th>val_loss</th><th>val acc</th><th>loss</th><th>acc</th><th>lr</th></tr></thead><tbody><tr><td>0</td><td>0.122325</td><td>0.9636</td><td>0.456990</td><td>0.875750</td><td>0.001</td></tr><tr><td>1</td><td>0.078255</td><td>0.9758</td><td>0.110654</td><td>0.967750</td><td>0.001</td></tr><tr><td>2</td><td>0.061693</td><td>0.9810</td><td>0.080759</td><td>0.975833</td><td>0.001</td></tr><tr><td>3</td><td>0.051456</td><td>0.9833</td><td>0.064430</td><td>0.980683</td><td>0.001</td></tr><tr><td>4</td><td>0.049063</td><td>0.9845</td><td>0.056158</td><td>0.983167</td><td>0.001</td></tr><tr><td>5</td><td>0.045025</td><td>0.9852</td><td>0.047683</td><td>0.985550</td><td>0.001</td></tr><tr><td>6</td><td>0.043757</td><td>0.9846</td><td>0.044444</td><td>0.986667</td><td>0.001</td></tr><tr><td>7</td><td>0.038086</td><td>0.9874</td><td>0.041104</td><td>0.987000</td><td>0.001</td></tr><tr><td>8</td><td>0.039764</td><td>0.9865</td><td>0.037332</td><td>0.988650</td><td>0.001</td></tr><tr><td>9</td><td>0.037271</td><td>0.9879</td><td>0.035225</td><td>0.989067</td><td>0.001</td></tr><tr><td>10</td><td>0.034473</td><td>0.9887</td><td>0.032936</td><td>0.989767</td><td>0.001</td></tr><tr><td>11</td><td>0.036944</td><td>0.9878</td><td>0.029740</td><td>0.990733</td><td>0.001</td></tr><tr><td>12</td><td>0.038205</td><td>0.9881</td><td>0.027904</td><td>0.991450</td><td>0.001</td></tr><tr><td>13</td><td>0.038962</td><td>0.9873</td><td>0.025470</td><td>0.992400</td><td>0.001</td></tr></tbody></table>

2020-07-08 09:25:48 DEBUG : get_Shannon(Model)

2020-07-08 09:25:48 DEBUG : Model Entropy: 8.921566009521484

2020-07-08 09:25:48 DEBUG : Model Entropy normalized: 0.01519857923257493

2020-07-08 09:25:48 DEBUG : Layers Sum Entropy: 44.55455780029297

2020-07-08 09:25:48 DEBUG : Layer Entropy: ['8.921566', '8.32647', '15.210262', '12.096259']

2020-07-08 09:25:48 DEBUG : Layer Entropy normalized: ['2.230391502380371', '2.775490125020345', '3.802565574645996', '0.021000449856122334']

2020-07-08 09:25:48 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_1000_500_5']

2020-07-08 09:25:48 DEBUG : out get_Shannon(Model)

2020-07-08 09:25:48 INFO : ###### End of step index : 3 of test 32 ###########################################

2020-07-08 09:25:48 INFO : _________________________________________________________________

2020-07-08 09:25:48 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'] : [(0.75, 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_5"

<table><thead><tr><th>Layer (type)</th><th>Output Shape</th><th>Param #</th></tr></thead><tbody><tr><td>DV_0_500_1 (InputLayer)</td><td>(None, 28, 28, 1)</td><td>0</td></tr><tr><td>DV_250_500_1 (Conv2D)</td><td>(None, 14, 14, 48)</td><td>816</td></tr><tr><td>DV_375_500_1 (SeparableConv2)</td><td>(None, 7, 7, 48)</td><td>2736</td></tr><tr><td>DV_438_500_1 (Conv2D)</td><td>(None, 3, 3, 64)</td><td>49216</td></tr><tr><td>DV_500_500_4 (Flatten)</td><td>(None, 576)</td><td>0</td></tr><tr><td>DV_750_500_3 (Dense)</td><td>(None, 64)</td><td>36928</td></tr><tr><td>DV_1000_500_6 (Dense)</td><td>(None, 10)</td><td>650</td></tr></tbody></table>

Total params: 90,346
Trainable params: 90,346
Non-trainable params: 0

None

2020-07-08 09:25:48 INFO : Original scores - TrnFitness: 0.9894000291824341 ValFitness: False TstFitness: 0.9902999997138977 Scaled_TstFitness: 0.9944000244140625

Train on 60000 samples, validate on 10000 samples

Epoch 1/20 - 1s - loss: 0.4371 - acc: 0.8935 - val_loss: 0.1309 - val acc: 0.9692
Epoch 2/20 - 1s - loss: 0.1073 - acc: 0.9739 - val_loss: 0.0825 - val acc: 0.9790
Epoch 3/20 - 1s - loss: 0.0738 - acc: 0.9816 - val_loss: 0.0634 - val acc: 0.9836
Epoch 4/20 - 1s - loss: 0.0581 - acc: 0.9855 - val_loss: 0.0522 - val acc: 0.9858
Epoch 5/20 - 1s - loss: 0.0481 - acc: 0.9880 - val_loss: 0.0482 - val acc: 0.9861
Epoch 6/20 - 1s - loss: 0.0416 - acc: 0.9896 - val_loss: 0.0435 - val acc: 0.9882
Epoch 7/20 - 1s - loss: 0.0366 - acc: 0.9909 - val_loss: 0.0415 - val acc: 0.9884
Epoch 8/20 - 1s - loss: 0.0321 - acc: 0.9917 - val_loss: 0.0394 - val acc: 0.9879
Epoch 9/20 - 1s - loss: 0.0285 - acc: 0.9929 - val_loss: 0.0375 - val acc: 0.9882
Epoch 10/20 - 1s - loss: 0.0257 - acc: 0.9938 - val_loss: 0.0371 - val acc: 0.9880
Epoch 11/20 - 1s - loss: 0.0234 - acc: 0.9941 - val_loss: 0.0341 - val acc: 0.9895
Epoch 12/20 - 1s - loss: 0.0217 - acc: 0.9943 - val_loss: 0.0353 - val acc: 0.9889

Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 13/20 - 1s - loss: 0.0191 - acc: 0.9952 - val_loss: 0.0330 - val acc: 0.9890
Epoch 14/20 - 1s - loss: 0.0176 - acc: 0.9955 - val_loss: 0.0341 - val acc: 0.9900

Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 15/20 - 1s - loss: 0.0157 - acc: 0.9963 - val_loss: 0.0326 - val acc: 0.9886
Epoch 16/20 - 1s - loss: 0.0155 - acc: 0.9962 - val_loss: 0.0308 - val acc: 0.9901
Epoch 17/20 - 1s - loss: 0.0139 - acc: 0.9966 - val_loss: 0.0316 - val acc: 0.9900

Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 18/20 - 1s - loss: 0.0118 - acc: 0.9977 - val_loss: 0.0307 - val acc: 0.9907
Epoch 19/20 - 1s - loss: 0.0112 - acc: 0.9977 - val_loss: 0.0325 - val acc: 0.9890

Epoch 00019: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 20/20 - 1s - loss: 0.0094 - acc: 0.9982 - val_loss: 0.0319 - val acc: 0.9896

Epoch 00020: ReduceLROnPlateau reducing learning rate to 0.001.

2020-07-08 09:26:10 DEBUG : Metrics(Test loss & Test Accuracy): [0.03194184391456656, 0.9896000027656555]

2020-07-08 09:26:10 DEBUG : Mutant ID: 1594174369341240832

2020-07-08 09:26:10 INFO : Current scores - TrnFitness: 0.9982333183288574 TstFitness: 0.9896000027656555
Scaled_TstFitness: 0.9896000027656555

<table><thead><tr><th>val_loss</th><th>val acc</th><th>loss</th><th>acc</th><th>lr</th></tr></thead><tbody><tr><td>0</td><td>0.130898</td><td>0.9692</td><td>0.437109</td><td>0.893450</td><td>0.001</td></tr><tr><td>1</td><td>0.082522</td><td>0.9790</td><td>0.107300</td><td>0.973917</td><td>0.001</td></tr></tbody></table>
2 0.063401 0.9836 0.073823 0.981633 0.001
3 0.052248 0.9858 0.058106 0.985533 0.001
4 0.048153 0.9861 0.048082 0.987950 0.001
5 0.043456 0.9882 0.041623 0.989600 0.001
6 0.041454 0.9884 0.036636 0.990883 0.001
7 0.039422 0.9879 0.032123 0.991700 0.001
8 0.037475 0.9882 0.028542 0.992933 0.001
9 0.037134 0.9880 0.025746 0.993783 0.001
10 0.034140 0.9895 0.023377 0.994100 0.001
11 0.035271 0.9889 0.021657 0.994300 0.001
12 0.032953 0.9890 0.019144 0.995200 0.001
13 0.034080 0.9900 0.017600 0.995550 0.001
14 0.032605 0.9886 0.015652 0.996350 0.001
15 0.030823 0.9901 0.015455 0.996233 0.001
16 0.031643 0.9900 0.013911 0.996550 0.001
17 0.030693 0.9907 0.011803 0.997650 0.001
18 0.032508 0.9890 0.011211 0.997733 0.001
19 0.031942 0.9896 0.009365 0.998233 0.001
2020-07-08 09:26:10 DEBUG : get_Shannon(Model)
2020-07-08 09:26:10 DEBUG : Model Entropy: 8.892518997192383
2020-07-08 09:26:10 DEBUG : Model Entropy normalized: 0.013659783405825472
2020-07-08 09:26:10 DEBUG : Layers Sum Entropy: 56.19849395751953
2020-07-08 09:26:10 DEBUG : Layer Entropy: ['8.892519', '8.286877', '15.173878', '14.829126', '9.016094']
2020-07-08 09:26:10 DEBUG : Layer Entropy normalized: ['2.2231297492980957', '2.762292226155599', '3.7934694290161133', '0.025745011038250394', '0.14087647199630737']
2020-07-08 09:26:10 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_750_500_3', 'DV_1000_500_6']
2020-07-08 09:26:10 DEBUG : out get_Shannon(Model)
2020-07-08 09:26:10 INFO : ######## End of step index : 4 of test 32 ########
2020-07-08 09:26:10 INFO : 
2020-07-08 09:26:10 INFO : ######## Start Evolutionary Step Index: 5 Test : 32/40 ########
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'] : [(0.625, 0.5)] Node_Type: Activation
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 ole': [10],
'Type': 'SignalChannel',
'Version': 1}
Model: "model_6"

<table>
  <thead>
    <tr>
      <th>Layer (type)</th>
      <th>Output Shape</th>
      <th>Param #</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>DV_0_500_1 (InputLayer)</td>
      <td>(None, 28, 28, 1)</td>
      <td>0</td>
    </tr>
    <tr>
      <td>DV_250_500_1 (Conv2D)</td>
      <td>(None, 14, 14, 48)</td>
      <td>816</td>
    </tr>
    <tr>
      <td>DV_375_500_1 (SeparableConv2 (None, 7, 7, 48))</td>
      <td></td>
      <td>2736</td>
    </tr>
    <tr>
      <td>DV_438_500_1 (Conv2D)</td>
      <td>(None, 3, 3, 64)</td>
      <td>49216</td>
    </tr>
    <tr>
      <td>DV_500_500_4 (Flatten)</td>
      <td>(None, 576)</td>
      <td>0</td>
    </tr>
    <tr>
      <td>DV_625_500_1 (Dense)</td>
      <td>(None, 64)</td>
      <td>36864</td>
    </tr>
    <tr>
      <td>DV_750_500_5 (Activation)</td>
      <td>(None, 64)</td>
      <td>0</td>
    </tr>
    <tr>
      <td>DV_1000_500_8 (Dense)</td>
      <td>(None, 10)</td>
      <td>650</td>
    </tr>
  </tbody>
</table>

Total params: 90,282
Trainable params: 90,282
Non-trainable params: 0
None

2020-07-08 09:26:10 INFO : Original scores - TrnFitness: 0.9902999997138977 ValFitness: False TstFitness: 0.9902999997138977 Scaled_TstFitness: 0.9944000244140625

Train on 60000 samples, validate on 10000 samples

Epoch 1/20 - 1s - loss: 0.7420 - acc: 0.8731 - val_loss: 0.2789 - val acc: 0.9673
Epoch 2/20 - 1s - loss: 0.2005 - acc: 0.9736 - val_loss: 0.1451 - val acc: 0.9781
Epoch 3/20 - 1s - loss: 0.1206 - acc: 0.9815 - val_loss: 0.1000 - val acc: 0.9825
Epoch 4/20 - 1s - loss: 0.0865 - acc: 0.9858 - val_loss: 0.0767 - val acc: 0.9860
Epoch 5/20 - 1s - loss: 0.0670 - acc: 0.9880 - val_loss: 0.0641 - val acc: 0.9873
Epoch 6/20 - 1s - loss: 0.0549 - acc: 0.9898 - val_loss: 0.0567 - val acc: 0.9873
Epoch 7/20 - 1s - loss: 0.0465 - acc: 0.9909 - val_loss: 0.0493 - val acc: 0.9888
Epoch 8/20 - 1s - loss: 0.0398 - acc: 0.9922 - val_loss: 0.0494 - val acc: 0.9876

Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 9/20 - 1s - loss: 0.0346 - acc: 0.9933 - val_loss: 0.0443 - val acc: 0.9883
Epoch 10/20 - 1s - loss: 0.0311 - acc: 0.9938 - val_loss: 0.0427 - val acc: 0.9883
Epoch 11/20 - 1s - loss: 0.0285 - acc: 0.9943 - val_loss: 0.0411 - val acc: 0.9887
Epoch 12/20 - 1s - loss: 0.0249 - acc: 0.9952 - val_loss: 0.0362 - val acc: 0.9898
Epoch 13/20 - 1s - loss: 0.0222 - acc: 0.9954 - val_loss: 0.0362 - val acc: 0.9897

Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 14/20 - 1s - loss: 0.0192 - acc: 0.9966 - val_loss: 0.0343 - val acc: 0.9901
Epoch 15/20 - 1s - loss: 0.0174 - acc: 0.9966 - val_loss: 0.0355 - val acc: 0.9896

Epoch 00015: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 16/20 - 1s - loss: 0.0158 - acc: 0.9973 - val_loss: 0.0339 - val acc: 0.9893
Epoch 17/20 - 1s - loss: 0.0144 - acc: 0.9976 - val_loss: 0.0335 - val acc: 0.9893
Epoch 18/20 - 1s - loss: 0.0125 - acc: 0.9980 - val_loss: 0.0320 - val acc: 0.9902
Epoch 19/20 - 1s - loss: 0.0115 - acc: 0.9983 - val_loss: 0.0332 - val acc: 0.9900

Epoch 00019: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 20/20 - 1s - loss: 0.0107 - acc: 0.9984 - val_loss: 0.0335 - val acc: 0.9895

Epoch 00020: ReduceLROnPlateau reducing learning rate to 0.001.

2020-07-08 09:26:32 DEBUG : Metrics(Test loss & Test Accuracy): [0.03351729686707258, 0.9894999861717224]

2020-07-08 09:26:32 DEBUG : Mutant ID: 1594174369341240832

2020-07-08 09:26:32 INFO : Current scores - TrnFitness: 0.9983500242233276 TstFitness: 0.9894999861717224

Scaled_TstFitness: 0.9894999861717224

<table><thead><tr><th></th><th>val_loss</th><th>val acc</th><th>loss</th><th>acc</th><th>lr</th></tr></thead><tbody><tr><td>0</td><td>0.278945</td><td>0.9673</td><td>0.741968</td><td>0.873100</td><td>0.001</td></tr><tr><td>1</td><td>0.145111</td><td>0.9781</td><td>0.200455</td><td>0.973567</td><td>0.001</td></tr><tr><td>2</td><td>0.099991</td><td>0.9825</td><td>0.120553</td><td>0.981550</td><td>0.001</td></tr><tr><td>3</td><td>0.076699</td><td>0.9860</td><td>0.086493</td><td>0.985833</td><td>0.001</td></tr><tr><td>4</td><td>0.064055</td><td>0.9873</td><td>0.066973</td><td>0.987983</td><td>0.001</td></tr><tr><td>5</td><td>0.056701</td><td>0.9873</td><td>0.054950</td><td>0.989750</td><td>0.001</td></tr><tr><td>6</td><td>0.049295</td><td>0.9888</td><td>0.046461</td><td>0.990883</td><td>0.001</td></tr><tr><td>7</td><td>0.049406</td><td>0.9876</td><td>0.039799</td><td>0.992217</td><td>0.001</td></tr><tr><td>8</td><td>0.044317</td><td>0.9883</td><td>0.034614</td><td>0.993267</td><td>0.001</td></tr><tr><td>9</td><td>0.042748</td><td>0.9883</td><td>0.031085</td><td>0.993817</td><td>0.001</td></tr><tr><td>10</td><td>0.041137</td><td>0.9887</td><td>0.028453</td><td>0.994300</td><td>0.001</td></tr><tr><td>11</td><td>0.036161</td><td>0.9898</td><td>0.024864</td><td>0.995183</td><td>0.001</td></tr><tr><td>12</td><td>0.036241</td><td>0.9897</td><td>0.022247</td><td>0.995417</td><td>0.001</td></tr><tr><td>13</td><td>0.034299</td><td>0.9901</td><td>0.019190</td><td>0.996600</td><td>0.001</td></tr><tr><td>14</td><td>0.035488</td><td>0.9896</td><td>0.017371</td><td>0.996650</td><td>0.001</td></tr><tr><td>15</td><td>0.033867</td><td>0.9893</td><td>0.015768</td><td>0.997350</td><td>0.001</td></tr><tr><td>16</td><td>0.033525</td><td>0.9893</td><td>0.014407</td><td>0.997583</td><td>0.001</td></tr><tr><td>17</td><td>0.032014</td><td>0.9902</td><td>0.012467</td><td>0.998000</td><td>0.001</td></tr><tr><td>18</td><td>0.033227</td><td>0.9900</td><td>0.011467</td><td>0.998300</td><td>0.001</td></tr><tr><td>19</td><td>0.033517</td><td>0.9895</td><td>0.010716</td><td>0.998350</td><td>0.001</td></tr></tbody></table>

2020-07-08 09:26:33 DEBUG : get_Shannon(Model)

2020-07-08 09:26:33 DEBUG : Model Entropy: 8.862110137939453

2020-07-08 09:26:33 DEBUG : Model Entropy normalized: 0.013613072408509145

2020-07-08 09:26:33 DEBUG : Layers Sum Entropy: 56.197896003723145

2020-07-08 09:26:33 DEBUG : Layer Entropy: ['8.86211', '8.264378', '15.158968', '14.818532', '9.093908']

2020-07-08 09:26:33 DEBUG : Layer Entropy normalized: ['2.2155275344848633', '2.7547925313313804', '3.7897419929504395', '0.025726618038283453', '0.14209231734275818']

2020-07-08 09:26:33 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_500_1', 'DV_1000_500_8']

2020-07-08 09:26:33 DEBUG : out get_Shannon(Model)

2020-07-08 09:26:33 INFO : ######## End of step index : 5 of test 32 ########
2020-07-08 09:26:33 INFO : ****************************
2020-07-08 09:26:33 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'] : [(0.25, 0.5)] Node_Type: Conv2D
NodeDict['From'] : [(0.625, 0.5)] Node_Type: Activation
{'Age': 0,
'Coordinate': (2, 0.5),
'FromCoordinate': (1.0, 0.5),
'Id': '',
'ModelName': '',
'ModelPath': '',
'Parameters': [],
'Signal_Shape': [10],
'Type': 'SignalChannel',
'Version': 1}
Model: "model_7"

<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>DV_438_500_1 (Conv2D)</td><td>(None, 3, 3, 64)</td><td>49216</td><td>DV_375_500_1[0][0]</td></tr><tr><td>DV_625_750_1 (Conv2D)</td><td>(None, 7, 7, 32)</td><td>24576</td><td>DV_250_500_1[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>flatten_1 (Flatten)</td><td>(None, 576)</td><td>0</td><td>DV_438_500_1[0][0]</td></tr><tr><td>flatten_2 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_750_1[0][0]</td></tr><tr><td>DV_625_500_1 (Dense)</td><td>(None, 64)</td><td>36864</td><td>DV_500_500_4[0][0]</td></tr><tr><td>DV_750_250_2_C (Concatenate)</td><td>(None, 2144)</td><td>0</td><td>flatten_1[0][0]<br>flatten_2[0][0]</td></tr><tr><td>DV_750_500_6 (Activation)</td><td>(None, 64)</td><td>0</td><td>DV_625_500_1[0][0]</td></tr><tr><td>DV_750_250_2 (Dense)</td><td>(None, 32)</td><td>68640</td><td>DV_750_250_2_C[0][0]</td></tr><tr><td>DV_1000_500_10_C (Concatenate)</td><td>(None, 96)</td><td>0</td><td>DV_750_500_6[0][0]<br>DV_750_250_2[0][0]</td></tr><tr><td>DV_1000_500_10 (Dense)</td><td>(None, 10)</td><td>970</td><td>DV_1000_500_10_C[0][0]</td></tr></tbody></table>

Total params: 183,818
Trainable params: 183,818
Non-trainable params: 0

None
2020-07-08 09:26:33 INFO : Original scores - TrnFitness: 0.9919000267982483 ValFitness: False TstFitness: 0.9922999739646912 Scaled_TstFitness: 0.9944000244140625
Train on 60000 samples, validate on 10000 samples
Epoch 1/20 - 1s - loss: 0.3520 - acc: 0.9243 - val_loss: 0.0882 - val acc: 0.9812
Epoch 2/20 - 1s - loss: 0.0668 - acc: 0.9867 - val_loss: 0.0557 - val acc: 0.9877
Epoch 3/20 - 1s - loss: 0.0439 - acc: 0.9915 - val_loss: 0.0446 - val acc: 0.9888
Epoch 4/20 - 1s - loss: 0.0329 - acc: 0.9933 - val_loss: 0.0385 - val acc: 0.9897
Epoch 5/20 - 1s - loss: 0.0263 - acc: 0.9947 - val_loss: 0.0337 - val acc: 0.9907
Epoch 6/20 - 1s - loss: 0.0210 - acc: 0.9961 - val_loss: 0.0335 - val acc: 0.9905
Epoch 7/20 - 1s - loss: 0.0178 - acc: 0.9966 - val_loss: 0.0306 - val acc: 0.9908
Epoch 8/20 - 1s - loss: 0.0145 - acc: 0.9975 - val_loss: 0.0286 - val acc: 0.9917
Epoch 9/20 - 1s - loss: 0.0125 - acc: 0.9979 - val_loss: 0.0278 - val acc: 0.9915
Epoch 10/20 - 1s - loss: 0.0108 - acc: 0.9983 - val_loss: 0.0283 - val acc: 0.9912
Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 11/20 - 1s - loss: 0.0093 - acc: 0.9986 - val_loss: 0.0289 - val acc: 0.9906

Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 12/20 - 1s - loss: 0.0087 - acc: 0.9986 - val_loss: 0.0282 - val acc: 0.9906

Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.001.

2020-07-08 09:26:50 DEBUG : Metrics(Test loss & Test Accuracy): [0.027817875245213508, 0.9915000200271606]

2020-07-08 09:26:50 DEBUG : Mutant ID: 1594174369341240832

2020-07-08 09:26:50 INFO : Current scores - TrnFitness: 0.9986333250999451 TstFitness: 0.9915000200271606

Scaled_TstFitness: 0.9915000200271606

val_loss val acc loss acc lr
0 0.088199 0.9812 0.351992 0.924267 0.001
1 0.055702 0.9877 0.066792 0.986683 0.001
2 0.044551 0.9888 0.043900 0.991450 0.001
3 0.038493 0.9897 0.032889 0.993333 0.001
4 0.033732 0.9907 0.026251 0.994650 0.001
5 0.033475 0.9905 0.021026 0.996133 0.001
6 0.030583 0.9908 0.017811 0.996600 0.001
7 0.028565 0.9917 0.014479 0.997483 0.001
8 0.027818 0.9915 0.012536 0.997900 0.001
9 0.028298 0.9912 0.010775 0.998333 0.001
10 0.028885 0.9906 0.009324 0.998633 0.001
11 0.028171 0.9906 0.008687 0.998633 0.001
2020-07-08 09:26:50 DEBUG : get_Shannon(Model)
2020-07-08 09:26:50 DEBUG : Model Entropy: 8.85185432434082
2020-07-08 09:26:50 DEBUG : Model Entropy normalized: 0.0031267588570613987
2020-07-08 09:26:50 DEBUG : Layers Sum Entropy: 86.64771270751953
2020-07-08 09:26:50 DEBUG : Layer Entropy: ['8.851854', '8.259366', '15.151393', '14.288543', '14.798992', '15.720822', '9.576742']
2020-07-08 09:26:50 DEBUG : Layer Entropy normalized: ['2.212963581085205', '2.753122011820475', '3.7878482341766357', '3.5721356868743896', '0.02569269471698337', '0.007332473103679828', '0.09975773096084595']
2020-07-08 09:26:50 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_750_1', 'DV_625_500_1', 'DV_750_250_2', 'DV_1000_500_10']
2020-07-08 09:26:50 DEBUG : out_get_Shannon(Model)
2020-07-08 09:26:50 INFO : ######## End of step index : 6 of test 32 ########
2020-07-08 09:26:50 INFO : _________________________________
2020-07-08 09:26:50 INFO : ######## Start Evolutionary Step Index: 7 Test : 32/40 ########
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'] : [(0.25, 0.5)] Node_Type: Conv2D
NodeDict['From'] : [(0.625, 0.5)] Node_Type: Activation
{'Age': 0,
'Coordinate': (2, 0.5),
'FromCoordinate': (1.0, 0.5),
'Id': '',
'ModelName': '',
'ModelPath': '',
'Parameters': [],
'Signal_Shape': [10],
'Type': 'SignalChannel',
'Version': 1}
Model: "model_8"

<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>DV_438_500_1 (Conv2D)</td><td>(None, 3, 3, 64)</td><td>49216</td><td>DV_375_500_1[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>DV_625_750_1 (Conv2D)</td><td>(None, 7, 7, 32)</td><td>24576</td><td>DV_250_500_1[0][0]</td></tr></tbody></table>
<table><tr><td>dropout_1 (Dropout)</td><td>(None, 576)</td><td>0</td><td>DV_500_500_4[0][0]</td></tr><tr><td>flatten_3 (Flatten)</td><td>(None, 576)</td><td>0</td><td>DV_438_500_1[0][0]</td></tr><tr><td>flatten_4 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_750_1[0][0]</td></tr><tr><td>DV_625_500_1 (Dense)</td><td>(None, 64)</td><td>36864</td><td>dropout_1[0][0]</td></tr><tr><td>DV_750_250_2_C (Concatenate)</td><td>(None, 2144)</td><td>0</td><td>flatten_3[0][0]<br>flatten_4[0][0]</td></tr><tr><td>DV_750_500_6 (Activation)</td><td>(None, 64)</td><td>0</td><td>DV_625_500_1[0][0]</td></tr><tr><td>DV_750_250_2 (Dense)</td><td>(None, 32)</td><td>68640</td><td>DV_750_250_2_C[0][0]</td></tr><tr><td>DV_1000_500_10_C (Concatenate)</td><td>(None, 96)</td><td>0</td><td>DV_750_500_6[0][0]<br>DV_750_250_2[0][0]</td></tr><tr><td>DV_1000_500_10 (Dense)</td><td>(None, 10)</td><td>970</td><td>DV_1000_500_10_C[0][0]</td></tr></table>

Total params: 183,818

Trainable params: 183,818

Non-trainable params: 0

None

2020-07-08 09:26:50 INFO : Original scores - TrnFitness: 0.9908999800682068 ValFitness: False TstFitness: 0.9919000267982483 Scaled_TstFitness: 0.9944000244140625

Train on 60000 samples, validate on 10000 samples

Epoch 1/20 - 1s - loss: 0.0190 - acc: 0.9954 - val_loss: 0.0302 - val acc: 0.9906

Epoch 2/20 - 1s - loss: 0.0135 - acc: 0.9967 - val_loss: 0.0287 - val acc: 0.9910

Epoch 3/20 - 1s - loss: 0.0114 - acc: 0.9972 - val_loss: 0.0290 - val acc: 0.9901

Epoch 00003: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 4/20 - 1s - loss: 0.0098 - acc: 0.9974 - val_loss: 0.0290 - val acc: 0.9901

Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 5/20 - 1s - loss: 0.0072 - acc: 0.9984 - val_loss: 0.0286 - val acc: 0.9910

Epoch 6/20 - 1s - loss: 0.0064 - acc: 0.9985 - val_loss: 0.0296 - val acc: 0.9906

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 7/20 - 1s - loss: 0.0054 - acc: 0.9988 - val_loss: 0.0340 - val acc: 0.9897

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 8/20 - 1s - loss: 0.0058 - acc: 0.9987 - val_loss: 0.0278 - val acc: 0.9915

Epoch 9/20 - 1s - loss: 0.0042 - acc: 0.9992 - val_loss: 0.0297 - val acc: 0.9912

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 10/20 - 1s - loss: 0.0036 - acc: 0.9994 - val_loss: 0.0279 - val acc: 0.9918

Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 11/20 - 1s - loss: 0.0029 - acc: 0.9995 - val_loss: 0.0279 - val acc: 0.9917

Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.001.

2020-07-08 09:27:06 DEBUG : Metrics(Test loss & Test Accuracy): [0.027835547250567468, 0.9915000200271606]

2020-07-08 09:27:06 DEBUG : Mutant ID: 1594174369341240832

2020-07-08 09:27:06 INFO : Current scores - TrnFitness: 0.9995499849319458 TstFitness: 0.9915000200271606 Scaled_TstFitness: 0.9915000200271606

<table><thead><tr><th></th><th>val_loss</th><th>val acc</th><th>loss</th><th>acc</th><th>lr</th></tr></thead><tbody><tr><td>0</td><td>0.030249</td><td>0.9906</td><td>0.018986</td><td>0.995367</td><td>0.001</td></tr><tr><td>1</td><td>0.028695</td><td>0.9910</td><td>0.013485</td><td>0.996700</td><td>0.001</td></tr><tr><td>2</td><td>0.028953</td><td>0.9901</td><td>0.011450</td><td>0.997183</td><td>0.001</td></tr><tr><td>3</td><td>0.029045</td><td>0.9901</td><td>0.009803</td><td>0.997433</td><td>0.001</td></tr><tr><td>4</td><td>0.028590</td><td>0.9910</td><td>0.007176</td><td>0.998450</td><td>0.001</td></tr><tr><td>5</td><td>0.029625</td><td>0.9906</td><td>0.006370</td><td>0.998533</td><td>0.001</td></tr><tr><td>6</td><td>0.033998</td><td>0.9897</td><td>0.005403</td><td>0.998850</td><td>0.001</td></tr><tr><td>7</td><td>0.027836</td><td>0.9915</td><td>0.005764</td><td>0.998650</td><td>0.001</td></tr><tr><td>8</td><td>0.029678</td><td>0.9912</td><td>0.004191</td><td>0.999167</td><td>0.001</td></tr><tr><td>9</td><td>0.027930</td><td>0.9918</td><td>0.003614</td><td>0.999350</td><td>0.001</td></tr><tr><td>10</td><td>0.027904</td><td>0.9917</td><td>0.002899</td><td>0.999550</td><td>0.001</td></tr></tbody></table>
2020-07-08 09:27:06 DEBUG : Model Entropy: 8.838857650756836
2020-07-08 09:27:06 DEBUG : Model Entropy normalized: 0.0031221680151030857
2020-07-08 09:27:06 DEBUG : Layers Sum Entropy: 86.55589294433594
2020-07-08 09:27:06 DEBUG : Layer Entropy: ['8.838858', '8.252718', '15.149298', '14.259788', '14.785739', '15.671017', '9.598476']
2020-07-08 09:27:06 DEBUG : Layer Entropy normalized: ['2.209714412689209', '2.750905990600586', '3.7873244285583496', '3.5649468898773193', '0.025669685668415494', '0.007309242860594792', '0.0999841292699178']
2020-07-08 09:27:06 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_750_1', 'DV_625_500_1', 'DV_750_250_2', 'DV_1000_500_10']
2020-07-08 09:27:06 DEBUG : out get_Shannon(Model)
2020-07-08 09:27:06 INFO : ######## End of step index : 7 of test 32 ########
2020-07-08 09:27:06 INFO :
2020-07-08 09:27:06 INFO : ######## Start Evolutionary Step Index: 8 Test: 32/40 ########
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']:[(0.25, 0.5)] Node_Type: Conv2D
NodeDict['From']:[(0.25, 0.5)] Node_Type: Conv2D
NodeDict['From']:[(0.625, 0.5)] Node_Type: Activation
('Age': 0,
'Coordinate': (2, 0.5),
'FromCoordinate': (1.0, 0.5),
'Id': '',
'ModelName': '',
'ModelPath': '',
'Parameters': [],
'Signal_Shape': [10],
'Type': 'SignalChannel',
'Version': 1}
Model: "model_9"

<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>DV_438_500_1 (Conv2D)</td><td>(None, 3, 3, 64)</td><td>49216</td><td>DV_375_500_1[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>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>dropout_2 (Dropout)</td><td>(None, 576)</td><td>0</td><td>DV_500_500_4[0][0]</td></tr><tr><td>flatten_5 (Flatten)</td><td>(None, 576)</td><td>0</td><td>DV_438_500_1[0][0]</td></tr><tr><td>flatten_6 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_750_2[0][0]</td></tr><tr><td>flatten_7 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>DV_625_500_2 (Dense)</td><td>(None, 64)</td><td>36864</td><td>dropout_2[0][0]</td></tr><tr><td>DV_750_250_3_C (Concatenate)</td><td>(None, 3712)</td><td>0</td><td>flatten_5[0][0]<br>flatten_6[0][0]<br>flatten_7[0][0]</td></tr><tr><td>DV_750_500_7 (Activation)</td><td>(None, 64)</td><td>0</td><td>DV_625_500_2[0][0]</td></tr><tr><td>DV_750_250_3 (Dense)</td><td>(None, 32)</td><td>118816</td><td>DV_750_250_3_C[0][0]</td></tr><tr><td>DV_1000_500_11_C (Concatenate)</td><td>(None, 96)</td><td>0</td><td>DV_750_500_7[0][0]<br>DV_750_250_3[0][0]</td></tr><tr><td>DV_1000_500_11 (Dense)</td><td>(None, 10)</td><td>970</td><td>DV_1000_500_11_C[0][0]</td></tr></tbody></table>
Total params: 258,570
Trainable params: 258,570
Non-trainable params: 0

None

2020-07-08 09:27:07 INFO : Original scores - TrnFitness: 0.9919999837875366 ValFitness: False TstFitness: 0.9923999905586243 Scaled_TstFitness: 0.9944000244140625

Train on 60000 samples, validate on 10000 samples

Epoch 1/20 - 2s - loss: 0.3123 - acc: 0.9245 - val_loss: 0.0702 - val acc: 0.9816
Epoch 2/20 - 1s - loss: 0.0609 - acc: 0.9863 - val_loss: 0.0465 - val acc: 0.9873
Epoch 3/20 - 1s - loss: 0.0410 - acc: 0.9907 - val_loss: 0.0381 - val acc: 0.9892
Epoch 4/20 - 1s - loss: 0.0306 - acc: 0.9934 - val_loss: 0.0339 - val acc: 0.9902
Epoch 5/20 - 1s - loss: 0.0246 - acc: 0.9943 - val_loss: 0.0305 - val acc: 0.9909
Epoch 6/20 - 1s - loss: 0.0198 - acc: 0.9957 - val_loss: 0.0295 - val acc: 0.9909
Epoch 7/20 - 1s - loss: 0.0164 - acc: 0.9964 - val_loss: 0.0297 - val acc: 0.9896

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 8/20 - 1s - loss: 0.0138 - acc: 0.9970 - val_loss: 0.0278 - val acc: 0.9909
Epoch 9/20 - 1s - loss: 0.0118 - acc: 0.9976 - val_loss: 0.0285 - val acc: 0.9912

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 10/20 - 1s - loss: 0.0105 - acc: 0.9979 - val_loss: 0.0282 - val acc: 0.9911

Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 11/20 - 1s - loss: 0.0084 - acc: 0.9985 - val_loss: 0.0266 - val acc: 0.9919
Epoch 12/20 - 1s - loss: 0.0075 - acc: 0.9987 - val_loss: 0.0265 - val acc: 0.9916

Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 13/20 - 1s - loss: 0.0066 - acc: 0.9989 - val_loss: 0.0264 - val acc: 0.9917
Epoch 14/20 - 1s - loss: 0.0055 - acc: 0.9993 - val_loss: 0.0260 - val acc: 0.9922
Epoch 15/20 - 1s - loss: 0.0054 - acc: 0.9991 - val_loss: 0.0280 - val acc: 0.9919

Epoch 00015: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 16/20 - 1s - loss: 0.0047 - acc: 0.9994 - val_loss: 0.0259 - val acc: 0.9927

Epoch 00016: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 17/20 - 1s - loss: 0.0039 - acc: 0.9995 - val_loss: 0.0267 - val acc: 0.9917

Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 18/20 - 1s - loss: 0.0034 - acc: 0.9998 - val_loss: 0.0278 - val acc: 0.9916

Epoch 00018: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 19/20 - 1s - loss: 0.0029 - acc: 0.9998 - val_loss: 0.0265 - val acc: 0.9919

Epoch 00019: ReduceLROnPlateau reducing learning rate to 0.001.
2020-07-08 09:27:34 DEBUG : Metrics(Test loss & Test Accuracy): [0.025917229831265286, 0.9926999807357788]

2020-07-08 09:27:34 DEBUG : Mutant ID: 1594174369341240832

2020-07-08 09:27:34 INFO : Current scores - TrnFitness: 0.999750018119812 TstFitness: 0.9926999807357788
Scaled_TstFitness: 0.9926999807357788

<table><thead><tr><th></th><th>val_loss</th><th>val acc</th><th>loss</th><th>acc</th><th>lr</th></tr></thead><tbody><tr><td>0</td><td>0.070211</td><td>0.9816</td><td>0.312268</td><td>0.924550</td><td>0.001</td></tr><tr><td>1</td><td>0.046484</td><td>0.9873</td><td>0.060889</td><td>0.986283</td><td>0.001</td></tr><tr><td>2</td><td>0.038112</td><td>0.9892</td><td>0.041035</td><td>0.990717</td><td>0.001</td></tr><tr><td>3</td><td>0.033854</td><td>0.9902</td><td>0.030629</td><td>0.993433</td><td>0.001</td></tr><tr><td>4</td><td>0.030453</td><td>0.9909</td><td>0.024595</td><td>0.994333</td><td>0.001</td></tr><tr><td>5</td><td>0.029472</td><td>0.9909</td><td>0.019821</td><td>0.995717</td><td>0.001</td></tr><tr><td>6</td><td>0.029694</td><td>0.9896</td><td>0.016409</td><td>0.996417</td><td>0.001</td></tr><tr><td>7</td><td>0.027791</td><td>0.9909</td><td>0.013760</td><td>0.997017</td><td>0.001</td></tr><tr><td>8</td><td>0.028485</td><td>0.9912</td><td>0.011848</td><td>0.997633</td><td>0.001</td></tr><tr><td>9</td><td>0.028223</td><td>0.9911</td><td>0.010496</td><td>0.997850</td><td>0.001</td></tr><tr><td>10</td><td>0.026564</td><td>0.9919</td><td>0.008443</td><td>0.998533</td><td>0.001</td></tr><tr><td>11</td><td>0.026505</td><td>0.9916</td><td>0.007543</td><td>0.998717</td><td>0.001</td></tr><tr><td>12</td><td>0.026364</td><td>0.9917</td><td>0.006613</td><td>0.998883</td><td>0.001</td></tr><tr><td>13</td><td>0.025951</td><td>0.9922</td><td>0.005481</td><td>0.999267</td><td>0.001</td></tr><tr><td>14</td><td>0.027985</td><td>0.9919</td><td>0.005423</td><td>0.999133</td><td>0.001</td></tr><tr><td>15</td><td>0.025917</td><td>0.9927</td><td>0.004657</td><td>0.999433</td><td>0.001</td></tr><tr><td>16</td><td>0.026688</td><td>0.9917</td><td>0.003898</td><td>0.999550</td><td>0.001</td></tr><tr><td>17</td><td>0.027756</td><td>0.9916</td><td>0.003354</td><td>0.999750</td><td>0.001</td></tr><tr><td>18</td><td>0.026545</td><td>0.9919</td><td>0.002912</td><td>0.999750</td><td>0.001</td></tr><tr><td>2020-07-08 09:27:35 DEBUG</td><td>get_Shannon(Model)</td><td></td><td></td><td></td><td></td></tr></tbody></table>
2020-07-08 09:27:35 DEBUG : Model Entropy: 8.829680442810059
2020-07-08 09:27:35 DEBUG : Model Entropy normalized: 0.0020053782518305832
2020-07-08 09:27:35 DEBUG : Layers Sum Entropy: 101.754563331604
2020-07-08 09:27:35 DEBUG : Layer Entropy normalized: ['2.2074201107025146', '2.747499465942383', '3.7856297492980957', '3.573122978210449', '3.572046995162964', '0.02577240102820926', '0.004440471016127488', '0.10032556454340617']
2020-07-08 09:27:35 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_750_2', 'DV_625_625_2', 'DV_625_500_2', 'DV_750_250_3', 'DV_1000_500_11']
2020-07-08 09:27:35 DEBUG : out get_Shannon(Model)
2020-07-08 09:27:35 INFO : ###### End of step index : 8 of test 32 ################################
2020-07-08 09:27:35 INFO : _________________________________________________
2020-07-08 09:27:35 INFO : ################################ Start Evolutionary Step Index: 9 Test : 32/40 ################################
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'] : [(0.25, 0.5)] Node_Type: Conv2D
NodeDict['From'] : [(0.25, 0.5)] Node_Type: Conv2D
NodeDict['From'] : [(0.625, 0.5)] Node_Type: Activation
{'Age': 0,
'Coordinate': (2, 0.5),
'FromCoordinate': (1.0, 0.5),
'Id': '',
'ModelName': '',
'ModelPath': '',
'Parameters': [],
'Signal ole Shape': [10],
'Type': 'Signal Channel',
'Version': 1}
Model: "model_10"

<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>DV_438_500_1 (Conv2D)</td><td>(None, 3, 3, 64)</td><td>49216</td><td>DV_375_500_1[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>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>dropout_3 (Dropout)</td><td>(None, 576)</td><td>0</td><td>DV_500_500_4[0][0]</td></tr><tr><td>flatten_8 (Flatten)</td><td>(None, 576)</td><td>0</td><td>DV_438_500_1[0][0]</td></tr><tr><td>flatten_9 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_750_2[0][0]</td></tr><tr><td>flatten_10 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>DV_625_500_2 (Dense)</td><td>(None, 64)</td><td>36864</td><td>dropout_3[0][0]</td></tr><tr><td>DV_750_250_3_C (Concatenate)</td><td>(None, 3712)</td><td>0</td><td>flatten_8[0][0]<br>flatten_9[0][0]<br>flatten_10[0][0]</td></tr><tr><td>DV_750_500_7 (Activation)</td><td>(None, 64)</td><td>0</td><td>DV_625_500_2[0][0]</td></tr><tr><td>DV_750_250_3 (Dense)</td><td>(None, 32)</td><td>118816</td><td>DV_750_250_3_C[0][0]</td></tr><tr><td>DV_1000_500_12_C (Concatenate)</td><td>(None, 672)</td><td>0</td><td>DV_750_500_7[0][0]<br>DV_750_250_3[0][0]<br>dropout_3[0][0]</td></tr></tbody></table>
DV_1000_500_12 (Dense)

(None, 10)

6730

DV_1000_500_12_C[0][0]

Total params: 264,330

Trainable params: 264,330

Non-trainable params: 0

None

2020-07-08 09:27:35 INFO : Original scores - TrnFitness: 0.9919999837875366 ValFitness: False TstFitness: 0.992900013923645 Scaled_TstFitness: 0.9944000244140625

Train on 60000 samples, validate on 10000 samples

Epoch 1/20 - 2s - loss: 0.4005 - acc: 0.8966 - val_loss: 0.0596 - val acc: 0.9815

Epoch 2/20 - 1s - loss: 0.0595 - acc: 0.9818 - val_loss: 0.0411 - val acc: 0.9873

Epoch 3/20 - 1s - loss: 0.0404 - acc: 0.9875 - val_loss: 0.0368 - val acc: 0.9883

Epoch 4/20 - 1s - loss: 0.0323 - acc: 0.9904 - val_loss: 0.0319 - val acc: 0.9890

Epoch 5/20 - 1s - loss: 0.0254 - acc: 0.9922 - val_loss: 0.0306 - val acc: 0.9899

Epoch 6/20 - 1s - loss: 0.0221 - acc: 0.9933 - val_loss: 0.0292 - val acc: 0.9905

Epoch 7/20 - 1s - loss: 0.0182 - acc: 0.9950 - val_loss: 0.0293 - val acc: 0.9899

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 8/20 - 1s - loss: 0.0162 - acc: 0.9954 - val_loss: 0.0274 - val acc: 0.9903

Epoch 9/20 - 1s - loss: 0.0138 - acc: 0.9961 - val_loss: 0.0287 - val acc: 0.9902

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 10/20 - 1s - loss: 0.0115 - acc: 0.9969 - val_loss: 0.0267 - val acc: 0.9908

Epoch 11/20 - 1s - loss: 0.0108 - acc: 0.9972 - val_loss: 0.0269 - val acc: 0.9912

Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 12/20 - 1s - loss: 0.0093 - acc: 0.9976 - val_loss: 0.0283 - val acc: 0.9903

Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 13/20 - 1s - loss: 0.0086 - acc: 0.9978 - val_loss: 0.0285 - val acc: 0.9898

Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.001.

2020-07-08 09:27:56 DEBUG : Metrics(Test loss & Test Accuracy): [0.026715012642150395, 0.9908000230789185]

2020-07-08 09:27:56 DEBUG : Mutant ID: 1594174369341240832

2020-07-08 09:27:56 INFO : Current scores - TrnFitness: 0.9977999925613403 TstFitness: 0.9908000230789185

Scaled_TstFitness: 0.9908000230789185

<table><thead><tr><th></th><th>val_loss</th><th>val acc</th><th>loss</th><th>acc</th><th>lr</th></tr></thead><tbody><tr><td>0</td><td>0.059588</td><td>0.9815</td><td>0.400505</td><td>0.896617</td><td>0.001</td></tr><tr><td>1</td><td>0.041133</td><td>0.9873</td><td>0.059544</td><td>0.981817</td><td>0.001</td></tr><tr><td>2</td><td>0.036825</td><td>0.9883</td><td>0.040423</td><td>0.987500</td><td>0.001</td></tr><tr><td>3</td><td>0.031873</td><td>0.9890</td><td>0.032270</td><td>0.990417</td><td>0.001</td></tr><tr><td>4</td><td>0.030649</td><td>0.9899</td><td>0.025373</td><td>0.992217</td><td>0.001</td></tr><tr><td>5</td><td>0.029165</td><td>0.9905</td><td>0.022150</td><td>0.993283</td><td>0.001</td></tr><tr><td>6</td><td>0.029322</td><td>0.9899</td><td>0.018200</td><td>0.994983</td><td>0.001</td></tr><tr><td>7</td><td>0.027418</td><td>0.9903</td><td>0.016230</td><td>0.995400</td><td>0.001</td></tr><tr><td>8</td><td>0.028681</td><td>0.9902</td><td>0.013847</td><td>0.996050</td><td>0.001</td></tr><tr><td>9</td><td>0.026715</td><td>0.9908</td><td>0.011512</td><td>0.996917</td><td>0.001</td></tr><tr><td>10</td><td>0.026920</td><td>0.9912</td><td>0.010779</td><td>0.997150</td><td>0.001</td></tr><tr><td>11</td><td>0.028259</td><td>0.9903</td><td>0.009336</td><td>0.997583</td><td>0.001</td></tr><tr><td>12</td><td>0.028542</td><td>0.9898</td><td>0.008556</td><td>0.997800</td><td>0.001</td></tr></tbody></table>

2020-07-08 09:27:56 DEBUG : get_Shannon(Model)

2020-07-08 09:27:56 DEBUG : Model Entropy: 8.825361251831055

2020-07-08 09:27:56 DEBUG : Model Entropy normalized: 0.0017725168210144717

2020-07-08 09:27:56 DEBUG : Layers Sum Entropy: 104.30861473083496

2020-07-08 09:27:56 DEBUG : Layer Entropy: ['8.825361', '8.24152', '15.140001', '14.265633', '14.262846', '14.816523', '16.428823', '12.327908']

2020-07-08 09:27:56 DEBUG : Layer Entropy normalized: ['2.2063403129577637', '2.747173309326172', '3.7850003242492676', '3.566408157348633', '3.565711498260498', '0.025723129510879517', '0.004425868391990662', '0.018345100539071218']

2020-07-08 09:27:56 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_750_2', 'DV_625_625_2', 'DV_625_500_2', 'DV_750_250_3', 'DV_1000_500_12']

2020-07-08 09:27:56 DEBUG : out get_Shannon(Model)

2020-07-08 09:27:56 INFO : ###### End of step index : 9 of test 32 ###########################################

2020-07-08 09:27:56 INFO : _________________________________________________________________

2020-07-08 09:27:56 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’: [(0.25, 0.5)] Node_Type: Conv2D
NodeDict['From’: [(0.25, 0.5)] Node_Type: Conv2D
NodeDict['From’: [(0.625, 0.5)] Node_Type: Activation
{’Age’: 0,
’Coordinate’: (2, 0.5),
’FromCoordinate’: (1.0, 0.5),
’Id’: ’’,
’ModelName’: ’’,
’ModelPath’: ’’,
’Parameters’: [],
’Signal_Shape’: [10],
’Type’: ’SignalChannel’,
’Version’: 1}
Model: "model_11"

<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>DV_438_500_1 (Conv2D)</td><td>(None, 3, 3, 64)</td><td>49216</td><td>DV_375_500_1[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>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>dropout_4 (Dropout)</td><td>(None, 576)</td><td>0</td><td>DV_500_500_4[0][0]</td></tr><tr><td>flatten_11 (Flatten)</td><td>(None, 576)</td><td>0</td><td>DV_438_500_1[0][0]</td></tr><tr><td>flatten_12 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_750_2[0][0]</td></tr><tr><td>flatten_13 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>DV_625_500_2 (Dense)</td><td>(None, 64)</td><td>36864</td><td>dropout_4[0][0]</td></tr><tr><td>DV_750_250_3_C (Concatenate)</td><td>(None, 3712)</td><td>0</td><td>flatten_11[0][0]<br>flatten_12[0][0]<br>flatten_13[0][0]</td></tr><tr><td>DV_750_500_7 (Activation)</td><td>(None, 64)</td><td>0</td><td>DV_625_500_2[0][0]</td></tr><tr><td>DV_750_250_3 (Dense)</td><td>(None, 32)</td><td>118816</td><td>DV_750_250_3_C[0][0]</td></tr><tr><td>DV_1000_500_13_C (Concatenate)</td><td>(None, 736)</td><td>0</td><td>DV_750_500_7[0][0]<br>DV_750_250_3[0][0]<br>dropout_4[0][0]<br>DV_625_500_2[0][0]</td></tr><tr><td>DV_1000_500_13 (Dense)</td><td>(None, 10)</td><td>7370</td><td>DV_1000_500_13_C[0][0]</td></tr></tbody></table>

Total params: 264,970
Trainable params: 264,970
Non-trainable params: 0

None
2020-07-08 09:27:57 INFO : Original scores - TrnFitness: 0.9923999905586243 ValFitness: False TstFitness: 0.9929999709129333 Scaled_TstFitness: 0.9944000244140625
Train on 60000 samples, validate on 10000 samples
Epoch 1/20 - 2s - loss: 0.4716 - acc: 0.8784 - val_loss: 0.0515 - val acc: 0.9839
Epoch 2/20 - 1s - loss: 0.0499 - acc: 0.9850 - val_loss: 0.0383 - val acc: 0.9874
Epoch 3/20 - 1s - loss: 0.0343 - acc: 0.9902 - val_loss: 0.0320 - val acc: 0.9894
Epoch 4/20 - 1s - loss: 0.0267 - acc: 0.9921 - val_loss: 0.0291 - val acc: 0.9903
Epoch 5/20 - 1s - loss: 0.0220 - acc: 0.9936 - val_loss: 0.0265 - val acc: 0.9909
Epoch 6/20 - 1s - loss: 0.0178 - acc: 0.9949 - val_loss: 0.0249 - val acc: 0.9915
Epoch 7/20 - 1s - loss: 0.0156 - acc: 0.9959 - val_loss: 0.0245 - val acc: 0.9921

Epoch 8/20 - 1s - loss: 0.0134 - acc: 0.9965 - val_loss: 0.0254 - val acc: 0.9914

Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 9/20 - 1s - loss: 0.0113 - acc: 0.9969 - val_loss: 0.0246 - val acc: 0.9918

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 10/20 - 1s - loss: 0.0104 - acc: 0.9974 - val_loss: 0.0246 - val acc: 0.9919

Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.001.

2020-07-08 09:28:13 DEBUG : Metrics(Test loss & Test Accuracy): [0.024456385533342836, 0.9921000003814697]

2020-07-08 09:28:13 DEBUG : Mutant ID: 1594174369341240832

2020-07-08 09:28:13 INFO : Current scores - TrnFitness: 0.9973666667938232 TstFitness: 0.9921000003814697

Scaled_TstFitness: 0.9921000003814697

<table><thead><tr><th></th><th>val_loss</th><th>val_acc</th><th>loss</th><th>acc</th><th>lr</th></tr></thead><tbody><tr><td>0</td><td>0.051461</td><td>0.9839</td><td>0.471587</td><td>0.878350</td><td>0.001</td></tr><tr><td>1</td><td>0.038332</td><td>0.9874</td><td>0.049941</td><td>0.985017</td><td>0.001</td></tr><tr><td>2</td><td>0.032013</td><td>0.9894</td><td>0.034325</td><td>0.990167</td><td>0.001</td></tr><tr><td>3</td><td>0.029053</td><td>0.9903</td><td>0.026733</td><td>0.992067</td><td>0.001</td></tr><tr><td>4</td><td>0.026472</td><td>0.9909</td><td>0.022028</td><td>0.993550</td><td>0.001</td></tr><tr><td>5</td><td>0.024905</td><td>0.9915</td><td>0.017819</td><td>0.994950</td><td>0.001</td></tr><tr><td>6</td><td>0.024456</td><td>0.9921</td><td>0.015573</td><td>0.995867</td><td>0.001</td></tr><tr><td>7</td><td>0.025403</td><td>0.9914</td><td>0.013383</td><td>0.996500</td><td>0.001</td></tr><tr><td>8</td><td>0.024560</td><td>0.9918</td><td>0.011260</td><td>0.996933</td><td>0.001</td></tr><tr><td>9</td><td>0.024554</td><td>0.9919</td><td>0.010444</td><td>0.997367</td><td>0.001</td></tr></tbody></table>

2020-07-08 09:28:14 DEBUG : get_Shannon(Model)

2020-07-08 09:28:14 DEBUG : Model Entropy: 8.812922477722168

2020-07-08 09:28:14 DEBUG : Model Entropy normalized: 0.0017475555180888694

2020-07-08 09:28:14 DEBUG : Layers Sum Entropy: 104.36413288116455

2020-07-08 09:28:14 DEBUG : Layer Entropy: ['8.8129225', '8.24308', '15.139083', '14.246231', '14.248705', '14.798869', '16.409634', '12.465609']

2020-07-08 09:28:14 DEBUG : Layer Entropy normalized: ['2.203230619430542', '2.7476933797200522', '3.7847707271575928', '3.5615577697753906', '3.56217622756958', '0.025692481133672927', '0.004420698716722685', '0.0169369682021763']

2020-07-08 09:28:14 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_750_2', 'DV_625_625_2', 'DV_625_500_2', 'DV_750_250_3', 'DV_1000_500_13']

2020-07-08 09:28:14 DEBUG : out get_Shannon(Model)

2020-07-08 09:28:14 INFO : ###########################################

2020-07-08 09:28:14 INFO : ###########################################

2020-07-08 09:28:14 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'] : [(0.25, 0.5)] Node_Type: Conv2D
NodeDict['From'] : [(0.25, 0.5)] Node_Type: Conv2D
NodeDict['From'] : [(0.625, 0.5)] Node_Type: Activation
NodeDict['From'] : [(0.75, 0.5)] Node_Type: Dense
NodeDict['From'] : [(0.8125, 0.5)] Node_Type: GaussianDropout
{'Age': 0,
'Coordinate': (2, 0.5),
'FromCoordinate': (1.0, 0.5),
'Id': '',
'ModelName': '',
'ModelPath': '',
'Parameters': [],
'Signal_Shape': [10],
'Type': 'SignalChannel',
'Version': 1}
Model: "model_12"

<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>DV_438_500_1 (Conv2D)</td><td>(None, 3, 3, 64)</td><td>49216</td><td>DV_375_500_1[0][0]</td></tr></tbody></table>
<table><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_5 (Dropout)</td><td>(None, 576)</td><td>0</td><td>DV_500_500_4[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_625_500_2 (Dense)</td><td>(None, 64)</td><td>36864</td><td>dropout_5[0][0]</td></tr><tr><td>flatten_14 (Flatten)</td><td>(None, 576)</td><td>0</td><td>DV_438_500_1[0][0]</td></tr><tr><td>flatten_15 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_750_2[0][0]</td></tr><tr><td>flatten_16 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>DV_750_500_7 (Activation)</td><td>(None, 64)</td><td>0</td><td>DV_625_500_2[0][0]</td></tr><tr><td>DV_750_250_3_C (Concatenate)</td><td>(None, 3712)</td><td>0</td><td>flatten_14[0][0]<br>flatten_15[0][0]<br>flatten_16[0][0]</td></tr><tr><td>DV_812_500_1 (Dense)</td><td>(None, 64)</td><td>4160</td><td>DV_750_500_7[0][0]</td></tr><tr><td>DV_750_250_3 (Dense)</td><td>(None, 32)</td><td>118816</td><td>DV_750_250_3_C[0][0]</td></tr><tr><td>DV_875_500_2 (GaussianDropout)</td><td>(None, 64)</td><td>0</td><td>DV_812_500_1[0][0]</td></tr><tr><td>DV_1000_500_15_C (Concatenate)</td><td>(None, 736)</td><td>0</td><td>DV_750_250_3[0][0]<br>dropout_5[0][0]<br>DV_625_500_2[0][0]<br>DV_875_500_2[0][0]</td></tr></table>

<table><tr><td>DV_1000_500_15 (Dense)</td><td>(None, 10)</td><td>7370</td><td>DV_1000_500_15_C[0][0]</td></tr></table>

Total params: 269,130
Trainable params: 269,130
Non-trainable params: 0

None

2020-07-08 09:28:15 INFO : Original scores - TrnFitness: 0.9926999807357788 ValFitness: False TstFitness: 0.9926999807357788 Scaled_TstFitness: 0.9944000244140625

Train on 60000 samples, validate on 10000 samples

Epoch 1/20 - 2s - loss: 0.3611 - acc: 0.8974 - val_loss: 0.0446 - val acc: 0.9855
Epoch 2/20 - 1s - loss: 0.0449 - acc: 0.9865 - val_loss: 0.0319 - val acc: 0.9897
Epoch 3/20 - 1s - loss: 0.0312 - acc: 0.9908 - val_loss: 0.0284 - val acc: 0.9902
Epoch 4/20 - 1s - loss: 0.0239 - acc: 0.9926 - val_loss: 0.0252 - val acc: 0.9917
Epoch 5/20 - 1s - loss: 0.0204 - acc: 0.9939 - val_loss: 0.0246 - val acc: 0.9917
Epoch 6/20 - 1s - loss: 0.0165 - acc: 0.9952 - val_loss: 0.0237 - val acc: 0.9919
Epoch 7/20 - 1s - loss: 0.0142 - acc: 0.9959 - val_loss: 0.0232 - val acc: 0.9925
Epoch 8/20 - 1s - loss: 0.0124 - acc: 0.9965 - val_loss: 0.0230 - val acc: 0.9929
Epoch 9/20 - 1s - loss: 0.0101 - acc: 0.9973 - val_loss: 0.0227 - val acc: 0.9928
Epoch 10/20 - 1s - loss: 0.0087 - acc: 0.9976 - val_loss: 0.0218 - val acc: 0.9931
Epoch 11/20 - 1s - loss: 0.0077 - acc: 0.9981 - val_loss: 0.0216 - val acc: 0.9929
Epoch 12/20 - 1s - loss: 0.0071 - acc: 0.9983 - val_loss: 0.0226 - val acc: 0.9933

Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 13/20 - 1s - loss: 0.0068 - acc: 0.9983 - val_loss: 0.0222 - val acc: 0.9933

Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 14/20 - 1s - loss: 0.0054 - acc: 0.9988 - val_loss: 0.0220 - val acc: 0.9931

Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.001.
2020-07-08 09:28:40 DEBUG : Metrics(Test loss & Test Accuracy): [0.021605231425212697, 0.992900013923645]

2020-07-08 09:28:40 DEBUG : Mutant ID: 1594174369341240832
2020-07-08 09:28:40 INFO : Current scores - TrnFitness: 0.9987999796867371 TstFitness: 0.992900013923645
Scaled_TstFitness: 0.992900013923645

<table><thead><tr><th></th><th>val_loss</th><th>val acc</th><th>loss</th><th>acc</th><th>lr</th></tr></thead><tbody><tr><td>0</td><td>0.044563</td><td>0.9855</td><td>0.361072</td><td>0.897417</td><td>0.001</td></tr><tr><td>1</td><td>0.031896</td><td>0.9897</td><td>0.044883</td><td>0.986500</td><td>0.001</td></tr></tbody></table>
2 0.028402 0.9902 0.031245 0.990850 0.001
3 0.025247 0.9917 0.023894 0.992600 0.001
4 0.024588 0.9917 0.020395 0.993900 0.001
5 0.023653 0.9919 0.016484 0.995233 0.001
6 0.023225 0.9925 0.014170 0.995950 0.001
7 0.022989 0.9929 0.012405 0.996483 0.001
8 0.022736 0.9928 0.010121 0.997250 0.001
9 0.021756 0.9931 0.008748 0.997600 0.001
10 0.021605 0.9929 0.007715 0.998117 0.001
11 0.022572 0.9933 0.007071 0.998317 0.001
12 0.022174 0.9933 0.006763 0.998283 0.001
13 0.021964 0.9931 0.005397 0.998800 0.001
2020-07-08 09:28:41 DEBUG : get_Shannon(Model)
2020-07-08 09:28:41 DEBUG : Model Entropy: 8.806816101074219
2020-07-08 09:28:41 DEBUG : Model Entropy normalized: 0.0017244597809035088
2020-07-08 09:28:41 DEBUG : Layers Sum Entropy: 115.93941116333008
2020-07-08 09:28:41 DEBUG : Layer Entropy: ['8.806816', '8.240873', '15.136676', '14.22297', '14.220619', '14.7776165', '11.705934', '16.384174', '12.443732']
2020-07-08 09:28:41 DEBUG : Layer Entropy normalized: ['2.2017040252685547', '2.746957778930664', '3.7841689586639404', '3.5557425022125244', '3.555154800415039', '0.02565558420287238', '0.18290521204471588', '0.004413840071908359', '0.01690724492073059']
2020-07-08 09:28:41 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_750_2', 'DV_625_625_2', 'DV_625_500_2', 'DV_812_500_1', 'DV_750_250_3', 'DV_1000_500_15']
2020-07-08 09:28:41 DEBUG : out get_Shannon(Model)
2020-07-08 09:28:41 INFO : ######## End of step index : 11 of test 32 ########
2020-07-08 09:28:41 INFO :
2020-07-08 09:28:41 INFO : ######## Start Evolutionary Step Index: 12 Test : 32/40 ########
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: Activation
NodeDict['From'] : [(0.75, 0.5)] Node_Type: Dense
NodeDict['From'] : [(0.8125, 0.5)] Node_Type: GaussianDropout
{'Age': 0,
'Coordinate': (2, 0.5),
'FromCoordinate': (1.0, 0.5),
'Id': '',
'ModelName': '',
'ModelPath': '',
'Parameters': [],
'Signal_Shape': [10],
'Type': 'SignalChannel',
'Version': 1}
Model: "model_13"

<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>DV_438_500_1 (Conv2D)</td><td>(None, 3, 3, 64)</td><td>49216</td><td>DV_375_500_1[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_6 (Dropout)</td><td>(None, 576)</td><td>0</td><td>DV_500_500_4[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_625_500_2 (Dense)</td><td>(None, 64)</td><td>36864</td><td>dropout_6[0][0]</td></tr><tr><td>flatten_17 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_750_2[0][0]</td></tr></tbody></table>
<table><tr><td>flatten_18 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>flatten_19 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>DV_750_500_8 (Activation)</td><td>(None, 64)</td><td>0</td><td>DV_625_500_2[0][0]</td></tr><tr><td>DV_750_250_4_C (Concatenate)</td><td>(None, 4704)</td><td>0</td><td>flatten_17[0][0]<br>flatten_18[0][0]<br>flatten_19[0][0]</td></tr><tr><td>DV_812_500_2 (Dense)</td><td>(None, 64)</td><td>4160</td><td>DV_750_500_8[0][0]</td></tr><tr><td>DV_750_250_4 (Dense)</td><td>(None, 32)</td><td>150560</td><td>DV_750_250_4_C[0][0]</td></tr><tr><td>DV_875_500_3 (GaussianDropout)</td><td>(None, 64)</td><td>0</td><td>DV_812_500_2[0][0]</td></tr><tr><td>DV_1000_500_16_C (Concatenate)</td><td>(None, 736)</td><td>0</td><td>DV_750_250_4[0][0]<br>dropout_6[0][0]<br>DV_625_500_2[0][0]<br>DV_875_500_3[0][0]</td></tr><tr><td>DV_1000_500_16 (Dense)</td><td>(None, 10)</td><td>7370</td><td>DV_1000_500_16_C[0][0]</td></tr></table>

Total params: 300,874

Trainable params: 300,874

Non-trainable params: 0

None

2020-07-08 09:28:41 INFO : Original scores - TrnFitness: 0.9921000003814697 ValFitness: False TstFitness: 0.9927999973297119 Scaled_TstFitness: 0.9944000244140625

Train on 60000 samples, validate on 10000 samples

Epoch 1/20 - 2s - loss: 0.2826 - acc: 0.9194 - val_loss: 0.0456 - val acc: 0.9846

Epoch 2/20 - 1s - loss: 0.0450 - acc: 0.9865 - val_loss: 0.0311 - val acc: 0.9894

Epoch 3/20 - 1s - loss: 0.0321 - acc: 0.9902 - val_loss: 0.0271 - val acc: 0.9912

Epoch 4/20 - 1s - loss: 0.0253 - acc: 0.9927 - val_loss: 0.0264 - val acc: 0.9915

Epoch 5/20 - 1s - loss: 0.0202 - acc: 0.9940 - val_loss: 0.0244 - val acc: 0.9913

Epoch 6/20 - 1s - loss: 0.0164 - acc: 0.9950 - val_loss: 0.0247 - val acc: 0.9922

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 7/20 - 1s - loss: 0.0145 - acc: 0.9959 - val_loss: 0.0229 - val acc: 0.9927

Epoch 8/20 - 1s - loss: 0.0123 - acc: 0.9967 - val_loss: 0.0237 - val acc: 0.9925

Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 9/20 - 1s - loss: 0.0115 - acc: 0.9969 - val_loss: 0.0236 - val acc: 0.9924

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 10/20 - 1s - loss: 0.0105 - acc: 0.9968 - val_loss: 0.0230 - val acc: 0.9926

Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.001.

2020-07-08 09:28:59 DEBUG : Metrics(Test loss & Test Accuracy): [0.022913675841756048, 0.9926999807357788]

2020-07-08 09:28:59 DEBUG : Mutant ID: 1594174369341240832

2020-07-08 09:28:59 INFO : Current scores - TrnFitness: 0.9968166947364807 TstFitness: 0.9926999807357788

Scaled_TstFitness: 0.9926999807357788

<table><thead><tr><th></th><th>val_loss</th><th>val_acc</th><th>loss</th><th>acc</th><th>lr</th></tr></thead><tbody><tr><td>0</td><td>0.045628</td><td>0.9846</td><td>0.282630</td><td>0.919400</td><td>0.001</td></tr><tr><td>1</td><td>0.031071</td><td>0.9894</td><td>0.045017</td><td>0.986500</td><td>0.001</td></tr><tr><td>2</td><td>0.027071</td><td>0.9912</td><td>0.032115</td><td>0.990200</td><td>0.001</td></tr><tr><td>3</td><td>0.026361</td><td>0.9915</td><td>0.025268</td><td>0.992733</td><td>0.001</td></tr><tr><td>4</td><td>0.024409</td><td>0.9913</td><td>0.020209</td><td>0.993967</td><td>0.001</td></tr><tr><td>5</td><td>0.024688</td><td>0.9922</td><td>0.016418</td><td>0.995017</td><td>0.001</td></tr><tr><td>6</td><td>0.022914</td><td>0.9927</td><td>0.014482</td><td>0.995850</td><td>0.001</td></tr><tr><td>7</td><td>0.023664</td><td>0.9925</td><td>0.012283</td><td>0.996667</td><td>0.001</td></tr><tr><td>8</td><td>0.023593</td><td>0.9924</td><td>0.011529</td><td>0.996867</td><td>0.001</td></tr><tr><td>9</td><td>0.023028</td><td>0.9926</td><td>0.010464</td><td>0.996817</td><td>0.001</td></tr></tbody></table>

2020-07-08 09:29:00 DEBUG : get_Shannon(Model)

2020-07-08 09:29:00 DEBUG : Model Entropy: 8.805408477783203

2020-07-08 09:29:00 DEBUG : Model Entropy normalized: 0.0014437462662376132

2020-07-08 09:29:00 DEBUG : Layers Sum Entropy: 116.36462020874023

2020-07-08 09:29:00 DEBUG : Layer Entropy: ['8.8054085', '8.240545', '15.135711', '14.209562', '14.205687', '14.76477', '11.705944', '16.841177', '12.455816']
2020-07-08 09:29:00 DEBUG : Layer Entropy normalized: ['2.201352119445801', '2.7468484242757163', '3.7839276790618896', '3.5523905754089355', '3.551421642303467', '0.025633280475934345', '0.182905375957489', '0.0035801821825455645', '0.016923663408859917']
2020-07-08 09:29:00 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_750_2', 'DV_625_625_2', 'DV_625_500_2', 'DV_812_500_2', 'DV_750_250_4', 'DV_1000_500_16']
2020-07-08 09:29:00 DEBUG : out get_Shannon(Model)
2020-07-08 09:29:00 INFO : ######## End of step index : 12 of test 32 ################################
2020-07-08 09:29:00 INFO : 
2020-07-08 09:29:00 INFO : ######## Start Evolutionary Step Index: 13 Test : 32/40 ################################
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: Dense
NodeDict['From'] : [(0.8125, 0.5)] Node_Type: GaussianDropout
{'Age': 0,
'Coordinate': (2, 0.5),
'FromCoordinate': (1.0, 0.5),
'Id': '',
'ModelName': '',
'ModelPath': '',
'Parameters': [],
'Signal_Shape': [10],
'Type': 'SignalChannel',
'Version': 1}
Model: "model_14"

<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>DV_438_500_1 (Conv2D)</td><td>(None, 3, 3, 64)</td><td>49216</td><td>DV_375_500_1[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_7 (Dropout)</td><td>(None, 576)</td><td>0</td><td>DV_500_500_4[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_625_500_2 (Dense)</td><td>(None, 64)</td><td>36864</td><td>dropout_7[0][0]</td></tr><tr><td>flatten_20 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_750_2[0][0]</td></tr><tr><td>flatten_21 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>flatten_22 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>DV_750_500_9 (Dropout)</td><td>(None, 64)</td><td>0</td><td>DV_625_500_2[0][0]</td></tr><tr><td>DV_750_250_5_C (Concatenate)</td><td>(None, 4704)</td><td>0</td><td>flatten_20[0][0]<br>flatten_21[0][0]<br>flatten_22[0][0]</td></tr><tr><td>DV_812_500_3 (Dense)</td><td>(None, 64)</td><td>4160</td><td>DV_750_500_9[0][0]</td></tr><tr><td>DV_750_250_5 (Dense)</td><td>(None, 32)</td><td>150560</td><td>DV_750_250_5_C[0][0]</td></tr><tr><td>DV_875_500_4 (GaussianDropout)</td><td>(None, 64)</td><td>0</td><td>DV_812_500_3[0][0]</td></tr><tr><td>DV_1000_500_17_C (Concatenate)</td><td>(None, 736)</td><td>0</td><td>DV_750_250_5[0][0]</td></tr></tbody></table>
dropout_7[0][0]
DV_625_500_2[0][0]
DV_875_500_4[0][0]

DV_1000_500_17 (Dense) (None, 10) 7370 DV_1000_500_17_C[0][0]

Total params: 300,874
Trainable params: 300,874
Non-trainable params: 0

None

2020-07-08 09:29:01 INFO : Original scores - TrnFitness: 0.9925000071525574 ValFitness: False TstFitness: 0.9933000206947327 Scaled_TstFitness: 0.9944000244140625

Train on 60000 samples, validate on 10000 samples

Epoch 1/20 - 2s - loss: 0.2863 - acc: 0.9176 - val_loss: 0.0458 - val acc: 0.9858
Epoch 2/20 - 1s - loss: 0.0414 - acc: 0.9872 - val_loss: 0.0324 - val acc: 0.9890
Epoch 3/20 - 1s - loss: 0.0301 - acc: 0.9910 - val_loss: 0.0280 - val acc: 0.9910
Epoch 4/20 - 1s - loss: 0.0225 - acc: 0.9933 - val_loss: 0.0267 - val acc: 0.9912
Epoch 5/20 - 1s - loss: 0.0185 - acc: 0.9944 - val_loss: 0.0257 - val acc: 0.9918
Epoch 6/20 - 1s - loss: 0.0152 - acc: 0.9956 - val_loss: 0.0247 - val acc: 0.9924
Epoch 7/20 - 1s - loss: 0.0131 - acc: 0.9961 - val_loss: 0.0250 - val acc: 0.9920

Epoch 00007: ReduceLRPlateau reducing learning rate to 0.001.
Epoch 8/20 - 1s - loss: 0.0115 - acc: 0.9965 - val_loss: 0.0252 - val acc: 0.9922

Epoch 00008: ReduceLRPlateau reducing learning rate to 0.001.
Epoch 9/20 - 1s - loss: 0.0102 - acc: 0.9972 - val_loss: 0.0251 - val acc: 0.9920

Epoch 00009: ReduceLRPlateau reducing learning rate to 0.001.
2020-07-08 09:29:18 DEBUG : Metrics(Test loss & Test Accuracy): [0.024680099019553745, 0.9923999905586243]
2020-07-08 09:29:18 DEBUG : Mutant ID: 1594174369341240832
2020-07-08 09:29:18 INFO : Current scores - TrnFitness: 0.9971666932106018 TstFitness: 0.9923999905586243
Scaled_TstFitness: 0.9923999905586243

val_loss val acc loss acc lr
0 0.045785 0.9858 0.286273 0.917567 0.001
1 0.032379 0.9890 0.041394 0.987150 0.001
2 0.028022 0.9910 0.030124 0.991000 0.001
3 0.026747 0.9912 0.022549 0.993267 0.001
4 0.025740 0.9918 0.018455 0.994450 0.001
5 0.024680 0.9924 0.015177 0.995567 0.001
6 0.025043 0.9920 0.013126 0.996083 0.001
7 0.025196 0.9922 0.011503 0.996533 0.001
8 0.025092 0.9920 0.010178 0.997167 0.001
2020-07-08 09:29:19 DEBUG : get_Shannon(Model)
2020-07-08 09:29:19 DEBUG : Model Entropy: 8.805061340332031
2020-07-08 09:29:19 DEBUG : Model Entropy normalized: 0.0014436893491280589
2020-07-08 09:29:19 DEBUG : Layers Sum Entropy: 116.3702278137207
2020-07-08 09:29:19 DEBUG : Layer Entropy: ['8.805061', '8.242043', '15.137336', '14.201311', '14.195278', '14.75647', '11.705758', '16.86192', '12.465052']
2020-07-08 09:29:19 DEBUG : Layer Entropy normalized: ['2.201265335083008', '2.7473475138346353', '3.7843339443206787', '3.55032777862549', '3.5488195419311523', '0.025618871053059895', '0.1829024702310562', '0.0035845917098376217', '0.016936211482338284']
2020-07-08 09:29:19 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_750_2', 'DV_625_625_2', 'DV_625_500_2', 'DV_812_500_3', 'DV_750_250_5', 'DV_1000_500_17']
2020-07-08 09:29:19 DEBUG : out_get_Shannon(Model)
2020-07-08 09:29:19 INFO : ######## End of step index : 13 of test 32 ########
2020-07-08 09:29:19 INFO : 
2020-07-08 09:29:19 INFO : ######## Start Evolutionary Step Index: 14 Test : 32/40 ########
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: Dense
NodeDict['From'] : [(0.8125, 0.5)] Node_Type: Dense
NodeDict['From'] : [(0.84375, 0.5)] Node_Type: GaussianDropout
{'Age': 0,
'Coordinate': (2, 0.5),
'FromCoordinate': (1.0, 0.5),
'Id': '',
'ModelName': '',
'ModelPath': '',
'Parameters': [],
'Signal_Shape': [10],
'Type': 'SignalChannel',
'Version': 1
Model: "model_15"

<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>DV_438_500_1 (Conv2D)</td><td>(None, 3, 3, 64)</td><td>49216</td><td>DV_375_500_1[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_8 (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_8[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_750_500_9 (Dropout)</td><td>(None, 64)</td><td>0</td><td>DV_625_500_2[0][0]</td></tr><tr><td>flatten_23 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_750_2[0][0]</td></tr><tr><td>flatten_24 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>flatten_25 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>DV_812_500_3 (Dense)</td><td>(None, 64)</td><td>4160</td><td>DV_750_500_9[0][0]</td></tr><tr><td>DV_750_250_5_C (Concatenate)</td><td>(None, 4704)</td><td>0</td><td>flatten_23[0][0]<br>flatten_24[0][0]<br>flatten_25[0][0]</td></tr><tr><td>DV_844_500_1 (Dense)</td><td>(None, 64)</td><td>4096</td><td>DV_812_500_3[0][0]</td></tr><tr><td>DV_750_250_5 (Dense)</td><td>(None, 32)</td><td>150560</td><td>DV_750_250_5_C[0][0]</td></tr><tr><td>DV_875_500_5 (GaussianDropout)</td><td>(None, 64)</td><td>0</td><td>DV_844_500_1[0][0]</td></tr><tr><td>DV_1000_500_18_C (Concatenate)</td><td>(None, 736)</td><td>0</td><td>DV_750_250_5[0][0]<br>dropout_8[0][0]<br>DV_625_500_2[0][0]<br>DV_875_500_5[0][0]</td></tr><tr><td>DV_1000_500_18 (Dense)</td><td>(None, 10)</td><td>7370</td><td>DV_1000_500_18_C[0][0]</td></tr></tbody></table>

Total params: 304,970
Trainable params: 304,970
Non-trainable params: 0

None

2020-07-08 09:29:21 INFO : Original scores - TrnFitness: 0.9937999844551086 ValFitness: False TstFitness: 0.993399977684021 Scaled_TstFitness: 0.9944000244140625
Train on 60000 samples, validate on 10000 samples
Epoch 1/20 - 2s - loss: 0.3011 - acc: 0.9155 - val_loss: 0.0400 - val acc: 0.9873
Epoch 2/20 - 1s - loss: 0.0398 - acc: 0.9886 - val_loss: 0.0304 - val acc: 0.9906
Epoch 3/20 - 1s - loss: 0.0284 - acc: 0.9918 - val_loss: 0.0262 - val acc: 0.9919
Epoch 4/20 - 1s - loss: 0.0224 - acc: 0.9932 - val_loss: 0.0242 - val acc: 0.9920
Epoch 5/20 - 1s - loss: 0.0190 - acc: 0.9943 - val_loss: 0.0236 - val acc: 0.9920
Epoch 6/20 - 1s - loss: 0.0155 - acc: 0.9956 - val_loss: 0.0241 - val acc: 0.9921

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 7/20 - 1s - loss: 0.0127 - acc: 0.9963 - val_loss: 0.0225 - val acc: 0.9927

Epoch 8/20 - 1s - loss: 0.0112 - acc: 0.9966 - val_loss: 0.0223 - val acc: 0.9928

Epoch 9/20 - 1s - loss: 0.0100 - acc: 0.9973 - val_loss: 0.0240 - val acc: 0.9929

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 10/20 - 1s - loss: 0.0098 - acc: 0.9971 - val_loss: 0.0237 - val acc: 0.9930

Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 11/20 - 1s - loss: 0.0091 - acc: 0.9972 - val_loss: 0.0231 - val acc: 0.9936

Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.001.

2020-07-08 09:29:41 DEBUG : Metrics(Test loss & Test Accuracy): [0.022280257258782514, 0.9927999973297119]

2020-07-08 09:29:41 DEBUG : Mutant ID: 1594174369341240832

2020-07-08 09:29:41 INFO : Current scores - TrnFitness: 0.9971500039100647 TstFitness: 0.9927999973297119

Scaled_TstFitness: 0.9927999973297119

<table><thead><tr><th></th><th>val_loss</th><th>val acc</th><th>loss</th><th>acc</th><th>lr</th></tr></thead><tbody><tr><td>0</td><td>0.040006</td><td>0.9873</td><td>0.301086</td><td>0.915500</td><td>0.001</td></tr><tr><td>1</td><td>0.030367</td><td>0.9906</td><td>0.039783</td><td>0.988567</td><td>0.001</td></tr><tr><td>2</td><td>0.026245</td><td>0.9919</td><td>0.028382</td><td>0.991767</td><td>0.001</td></tr><tr><td>3</td><td>0.024176</td><td>0.9920</td><td>0.022397</td><td>0.993217</td><td>0.001</td></tr><tr><td>4</td><td>0.023569</td><td>0.9920</td><td>0.018961</td><td>0.994267</td><td>0.001</td></tr><tr><td>5</td><td>0.024119</td><td>0.9921</td><td>0.015478</td><td>0.995650</td><td>0.001</td></tr><tr><td>6</td><td>0.022505</td><td>0.9927</td><td>0.012734</td><td>0.996333</td><td>0.001</td></tr><tr><td>7</td><td>0.022280</td><td>0.9928</td><td>0.011231</td><td>0.996550</td><td>0.001</td></tr><tr><td>8</td><td>0.024039</td><td>0.9929</td><td>0.009963</td><td>0.997283</td><td>0.001</td></tr><tr><td>9</td><td>0.023721</td><td>0.9930</td><td>0.009824</td><td>0.997117</td><td>0.001</td></tr><tr><td>10</td><td>0.023141</td><td>0.9936</td><td>0.009071</td><td>0.997150</td><td>0.001</td></tr></tbody></table>

2020-07-08 09:29:42 DEBUG : get_Shannon(Model)

2020-07-08 09:29:42 DEBUG : Model Entropy: 8.804018020629883

2020-07-08 09:29:42 DEBUG : Model Entropy normalized: 0.0014285279929628237

2020-07-08 09:29:42 DEBUG : Layers Sum Entropy: 128.0015172958374

2020-07-08 09:29:42 DEBUG : Layer Entropy normalized: ['8.804018', '8.243589', '15.137894', '14.748881', '14.188121', '14.183282', '11.693251', '11.7189665', '16.827774', '12.455741']

2020-07-08 09:29:42 DEBUG : Layer Entropy normalized: ['2.2010045051574707', '2.7478631337483725', '3.784473419189453', '0.025605696770879958', '3.547030210494995', '3.545820474624634', '0.1827070415019989', '0.183108851313591', '0.0035773329183357914', '0.01692356104436128']

2020-07-08 09:29:42 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_500_2', 'DV_625_750_2', 'DV_625_625_2', 'DV_812_500_3', 'DV_844_500_1', 'DV_750_250_5', 'DV_1000_500_18']

2020-07-08 09:29:42 DEBUG : out get_Shannon(Model)

2020-07-08 09:29:42 INFO : ###### End of step index : 14 of test 32 ###########################################

2020-07-08 09:29:42 INFO :

2020-07-08 09:29:42 INFO : ###### Start Evolutionary Step Index: 15 Test : 32/40 ###########################################

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: Dense

NodeDict['From'] : [(0.84375, 0.5)] Node_Type: GaussianDropout

'Age': 0,
'Coordinate': (2, 0.5),
'FromCoordinate': (1.0, 0.5),
'Id': '',
'ModelName': '',
'ModelPath': '',
'Parameters': [],
'Signal_Shape': [10],
'Type': 'SignalChannel',
'Version': 1}
Model: "model_16"

Layer (type)

Output Shape

Param #

Connected to
<table><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>DV_438_500_1 (Conv2D)</td><td>(None, 3, 3, 64)</td><td>49216</td><td>DV_375_500_1[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_9 (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_9[0][0]</td></tr><tr><td>DV_750_500_9 (Dropout)</td><td>(None, 64)</td><td>0</td><td>DV_625_500_2[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_781_500_2 (Activation)</td><td>(None, 64)</td><td>0</td><td>DV_750_500_9[0][0]</td></tr><tr><td>flatten_26 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_750_2[0][0]</td></tr><tr><td>flatten_27 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>flatten_28 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>DV_812_500_5 (Dense)</td><td>(None, 64)</td><td>4160</td><td>DV_781_500_2[0][0]</td></tr><tr><td>DV_750_250_5_C (Concatenate)</td><td>(None, 4704)</td><td>0</td><td>flatten_26[0][0]<br>flatten_27[0][0]<br>flatten_28[0][0]</td></tr><tr><td>DV_844_500_3 (Dense)</td><td>(None, 64)</td><td>4096</td><td>DV_812_500_5[0][0]</td></tr><tr><td>DV_750_250_5 (Dense)</td><td>(None, 32)</td><td>150560</td><td>DV_750_250_5_C[0][0]</td></tr><tr><td>DV_875_500_7 (GaussianDropout)</td><td>(None, 64)</td><td>0</td><td>DV_844_500_3[0][0]</td></tr><tr><td>DV_1000_500_20_C (Concatenate)</td><td>(None, 736)</td><td>0</td><td>DV_750_250_5[0][0]<br>dropout_9[0][0]<br>DV_625_500_2[0][0]<br>DV_875_500_7[0][0]</td></tr><tr><td>DV_1000_500_20 (Dense)</td><td>(None, 10)</td><td>7370</td><td>DV_1000_500_20_C[0][0]</td></tr></table>

Total params: 304,970
Trainable params: 304,970
Non-trainable params: 0

None

2020-07-08 09:29:43 INFO : Original scores - TrnFitness: 0.992900013923645 ValFitness: False TstFitness: 0.993399977684021 Scaled_TstFitness: 0.9944000244140625

Train on 60000 samples, validate on 10000 samples

Epoch 1/20 - 2s - loss: 0.3375 - acc: 0.9046 - val_loss: 0.0447 - val acc: 0.9854
Epoch 2/20 - 1s - loss: 0.0418 - acc: 0.9877 - val_loss: 0.0325 - val acc: 0.9888
Epoch 3/20 - 1s - loss: 0.0279 - acc: 0.9918 - val_loss: 0.0291 - val acc: 0.9903
Epoch 4/20 - 1s - loss: 0.0222 - acc: 0.9933 - val_loss: 0.0267 - val acc: 0.9915
Epoch 5/20 - 1s - loss: 0.0173 - acc: 0.9951 - val_loss: 0.0241 - val acc: 0.9921
Epoch 6/20 - 1s - loss: 0.0140 - acc: 0.9958 - val_loss: 0.0236 - val acc: 0.9917
Epoch 7/20 - 1s - loss: 0.0126 - acc: 0.9964 - val_loss: 0.0247 - val acc: 0.9924

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 8/20 - 1s - loss: 0.0109 - acc: 0.9967 - val_loss: 0.0252 - val acc: 0.9922

Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 9/20 - 1s - loss: 0.0103 - acc: 0.9970 - val_loss: 0.0244 - val acc: 0.9921

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.001.
2020-07-08 09:30:01 DEBUG : Metrics(Test loss & Test Accuracy): [0.023640528858255128, 0.9916999936103821]
2020-07-08 09:30:01 DEBUG : Mutant ID: 1594174369341240832
2020-07-08 09:30:01 INFO : Current scores - TrnFitness: 0.9969666600227356 TstFitness: 0.9916999936103821
Scaled_TstFitness: 0.9916999936103821

| val_loss | val acc | loss | acc | lr |
|---|---|---|---|---|
| 0 | 0.044672 | 0.9854 | 0.337525 | 0.904600 | 0.001 |
| 1 | 0.032454 | 0.9888 | 0.041791 | 0.987700 | 0.001 |
| 2 | 0.029128 | 0.9903 | 0.027916 | 0.991800 | 0.001 |
| 3 | 0.026721 | 0.9915 | 0.022151 | 0.993250 | 0.001 |
| 4 | 0.024103 | 0.9921 | 0.017267 | 0.995100 | 0.001 |
| 5 | 0.023641 | 0.9917 | 0.014030 | 0.995817 | 0.001 |
| 6 | 0.024709 | 0.9924 | 0.012633 | 0.996433 | 0.001 |
| 7 | 0.025238 | 0.9922 | 0.010858 | 0.996700 | 0.001 |
| 8 | 0.024434 | 0.9921 | 0.010323 | 0.996967 | 0.001 |

2020-07-08 09:30:03 DEBUG : get_Shannon(Model)
2020-07-08 09:30:03 DEBUG : Model Entropy: 8.80427360534668
2020-07-08 09:30:03 DEBUG : Model Entropy normalized: 0.0014285694637914458
2020-07-08 09:30:03 DEBUG : Layers Sum Entropy: 127.94102954864502
2020-07-08 09:30:03 DEBUG : Layer Entropy: ['8.804274', '8.23775', '15.139832', '14.738966', '14.17736', '14.172667', '11.703041', '11.706749', '16.802256', '12.458137']
2020-07-08 09:30:03 DEBUG : Layer Entropy normalized: ['2.20106840133667', '2.7459166844685874', '3.7849578857421875', '0.02558848261833191', '3.544339895248413', '3.5431666374206543', '0.18286001682281494', '0.18291795253753662', '0.0035719080847136826', '0.016926815976267277']
2020-07-08 09:30:03 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_500_2', 'DV_625_750_2', 'DV_625_625_2', 'DV_812_500_5', 'DV_844_500_3', 'DV_750_250_5', 'DV_1000_500_20']
2020-07-08 09:30:03 DEBUG : out get_Shannon(Model)
2020-07-08 09:30:03 INFO : ######## End of step index: 15 of test 32 ########
2020-07-08 09:30:03 INFO : 
2020-07-08 09:30:03 INFO : ######## Start Evolutionary Step Index: 16 Test : 32/40 ########
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: Dense
NodeDict['From']: [(0.84375, 0.5)] Node_Type: GaussianDropout
NodeDict['From']: [(0.875, 0.5)] Node_Type: AlphaDropout
{'Age': 0,
'Coordinate': (2, 0.5),
'FromCoordinate': (1.0, 0.5),
'Id': '',
'ModelName': '',
'ModelPath': '',
'Parameters': [],
'Signal ole': [10],
'Type': 'SignalChannel',
'Version': 1}
Model: "model_17"

<table>
   <thead>
    <tr>
     <td>
      Layer (type)
     </td>
     <td>
      Output Shape
     </td>
     <td>
      Param #
     </td>
     <td>
      Connected to
     </td>
    </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_10 (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_10[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_11 (Dropout)
     </td>
     <td>
      (None, 576)
     </td>
     <td>
      0
     </td>
     <td>
      DV_500_500_4[0][0]
     </td>
    </tr>
   </tbody>
  </table>
<table><tr><td>DV_625_500_2 (Dense)</td><td>(None, 64)</td><td>36864</td><td>dropout_11[0][0]</td></tr><tr><td>DV_750_500_9 (Dropout)</td><td>(None, 64)</td><td>0</td><td>DV_625_500_2[0][0]</td></tr><tr><td>DV_781_500_2 (Activation)</td><td>(None, 64)</td><td>0</td><td>DV_750_500_9[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_812_500_5 (Dense)</td><td>(None, 64)</td><td>4160</td><td>DV_781_500_2[0][0]</td></tr><tr><td>flatten_29 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_750_2[0][0]</td></tr><tr><td>flatten_30 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>flatten_31 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>DV_844_500_3 (Dense)</td><td>(None, 64)</td><td>4096</td><td>DV_812_500_5[0][0]</td></tr><tr><td>DV_750_250_5_C (Concatenate)</td><td>(None, 4704)</td><td>0</td><td>flatten_29[0][0]<br>flatten_30[0][0]<br>flatten_31[0][0]</td></tr><tr><td>DV_875_500_7 (GaussianDropout)</td><td>(None, 64)</td><td>0</td><td>DV_844_500_3[0][0]</td></tr><tr><td>DV_750_250_5 (Dense)</td><td>(None, 32)</td><td>150560</td><td>DV_750_250_5_C[0][0]</td></tr><tr><td>DV_938_500_1 (AlphaDropout)</td><td>(None, 64)</td><td>0</td><td>DV_875_500_7[0][0]</td></tr><tr><td>DV_1000_500_21_C (Concatenate)</td><td>(None, 736)</td><td>0</td><td>DV_750_250_5[0][0]<br>dropout_11[0][0]<br>DV_625_500_2[0][0]<br>DV_938_500_1[0][0]</td></tr><tr><td>DV_1000_500_21 (Dense)</td><td>(None, 10)</td><td>7370</td><td>DV_1000_500_21_C[0][0]</td></tr></table>

Total params: 304,970

Trainable params: 304,970

Non-trainable params: 0

None

2020-07-08 09:30:04 INFO : Original scores - TrnFitness: 0.9932000041007996 ValFitness: False TstFitness: 0.9934999942779541 Scaled_TstFitness: 0.9944000244140625

Train on 60000 samples, validate on 10000 samples

Epoch 1/20 - 2s - loss: 0.3526 - acc: 0.9000 - val_loss: 0.0427 - val acc: 0.9869

Epoch 2/20 - 1s - loss: 0.0513 - acc: 0.9839 - val_loss: 0.0300 - val acc: 0.9900

Epoch 3/20 - 1s - loss: 0.0391 - acc: 0.9880 - val_loss: 0.0273 - val acc: 0.9913

Epoch 4/20 - 1s - loss: 0.0330 - acc: 0.9900 - val_loss: 0.0261 - val acc: 0.9918

Epoch 5/20 - 1s - loss: 0.0268 - acc: 0.9913 - val_loss: 0.0263 - val acc: 0.9914

Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 6/20 - 1s - loss: 0.0230 - acc: 0.9925 - val_loss: 0.0243 - val acc: 0.9916

Epoch 7/20 - 1s - loss: 0.0214 - acc: 0.9930 - val_loss: 0.0247 - val acc: 0.9918

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 8/20 - 1s - loss: 0.0189 - acc: 0.9938 - val_loss: 0.0244 - val acc: 0.9925

Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 9/20 - 1s - loss: 0.0168 - acc: 0.9948 - val_loss: 0.0247 - val acc: 0.9921

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.001.

2020-07-08 09:30:22 DEBUG : Metrics(Test loss & Test Accuracy): [0.024283330682094675, 0.991599977016449]

2020-07-08 09:30:22 DEBUG : Mutant ID: 1594174369341240832

2020-07-08 09:30:22 INFO : Current scores - TrnFitness: 0.9947500228881836 TstFitness: 0.991599977016449 Scaled_TstFitness: 0.991599977016449

<table><thead><tr><th></th><th>val_loss</th><th>val acc</th><th>loss</th><th>acc</th><th>lr</th></tr></thead><tbody><tr><td>0</td><td>0.042687</td><td>0.9869</td><td>0.352634</td><td>0.900000</td><td>0.001</td></tr><tr><td>1</td><td>0.030024</td><td>0.9900</td><td>0.051251</td><td>0.983933</td><td>0.001</td></tr><tr><td>2</td><td>0.027310</td><td>0.9913</td><td>0.039094</td><td>0.987950</td><td>0.001</td></tr><tr><td>3</td><td>0.026117</td><td>0.9918</td><td>0.032985</td><td>0.989950</td><td>0.001</td></tr></tbody></table>
4 0.026283 0.9914 0.026760 0.991267 0.001
5 0.024283 0.9916 0.023010 0.992500 0.001
6 0.024709 0.9918 0.021424 0.992967 0.001
7 0.024427 0.9925 0.018947 0.993800 0.001
8 0.024724 0.9921 0.016838 0.994750 0.001
2020-07-08 09:30:24 DEBUG : get_Shannon(Model)
2020-07-08 09:30:24 DEBUG : Model Entropy: 8.793004035949707
2020-07-08 09:30:24 DEBUG : Model Entropy normalized: 0.0014267408787846352
2020-07-08 09:30:24 DEBUG : Layers Sum Entropy: 127.84410381317139
2020-07-08 09:30:24 DEBUG : Layer Entropy: ['8.793004', '8.238329', '15.139215', '14.731662', '14.16304', '14.155197', '11.686183', '11.694334', '16.77867', '12.46447']
2020-07-08 09:30:24 DEBUG : Layer Entropy normalized: ['2.1982510089874268', '2.74610964457194', '3.784803867340088', '0.025575801730155945', '3.540760040283203', '3.538799285888672', '0.1825966089963913', '0.1827239692211151', '0.0035668939960246185', '0.016935421072918434']
2020-07-08 09:30:24 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_500_2', 'DV_625_750_2', 'DV_625_625_2', 'DV_812_500_5', 'DV_844_500_3', 'DV_750_250_5', 'DV_1000_500_21']
2020-07-08 09:30:24 DEBUG : out get_Shannon(Model)
2020-07-08 09:30:24 INFO : ######## End of step index : 16 of test 32 ########
2020-07-08 09:30:24 INFO :
2020-07-08 09:30:24 INFO : ######## Start Evolutionary Step Index: 17 Test : 32/40 ########
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: GaussianDropout
NodeDict['From'] : [(0.875, 0.5)] Node_Type: AlphaDropout
('Age': 0,
'Coordinate': (2, 0.5),
'FromCoordinate': (1.0, 0.5),
'Id': '',
'ModelName': '',
'ModelPath': '',
'Parameters': [],
'Signal_Shape': [10],
'Type': 'SignalChannel',
'Version': 1}
Model: "model_18"

<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_12 (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_12[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_13 (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_13[0][0]</td></tr><tr><td>DV_750_500_9 (Dropout)</td><td>(None, 64)</td><td>0</td><td>DV_625_500_2[0][0]</td></tr><tr><td>DV_781_500_2 (Activation)</td><td>(None, 64)</td><td>0</td><td>DV_750_500_9[0][0]</td></tr><tr><td>DV_812_500_5 (Dense)</td><td>(None, 64)</td><td>4160</td><td>DV_781_500_2[0][0]</td></tr></tbody></table>
<table><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_1 (Activation)</td><td>(None, 64)</td><td>0</td><td>DV_812_500_5[0][0]</td></tr><tr><td>flatten_32 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_750_2[0][0]</td></tr><tr><td>flatten_33 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>flatten_34 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>DV_844_500_4 (Dense)</td><td>(None, 64)</td><td>4096</td><td>DV_828_500_1[0][0]</td></tr><tr><td>DV_750_250_5_C (Concatenate)</td><td>(None, 4704)</td><td>0</td><td>flatten_32[0][0]<br>flatten_33[0][0]<br>flatten_34[0][0]</td></tr><tr><td>DV_875_500_8 (GaussianDropout)</td><td>(None, 64)</td><td>0</td><td>DV_844_500_4[0][0]</td></tr><tr><td>DV_750_250_5 (Dense)</td><td>(None, 32)</td><td>150560</td><td>DV_750_250_5_C[0][0]</td></tr><tr><td>DV_938_500_2 (AlphaDropout)</td><td>(None, 64)</td><td>0</td><td>DV_875_500_8[0][0]</td></tr><tr><td>DV_1000_500_22_C (Concatenate)</td><td>(None, 736)</td><td>0</td><td>DV_750_250_5[0][0]<br>dropout_13[0][0]<br>DV_625_500_2[0][0]<br>DV_938_500_2[0][0]</td></tr><tr><td>DV_1000_500_22 (Dense)</td><td>(None, 10)</td><td>7370</td><td>DV_1000_500_22_C[0][0]</td></tr></table>

Total params: 304,970

Trainable params: 304,970

Non-trainable params: 0

None

2020-07-08 09:30:26 INFO : Original scores - TrnFitness: 0.9936000108718872 ValFitness: False TstFitness: 0.9936000108718872 Scaled_TstFitness: 0.9944000244140625

Train on 60000 samples, validate on 10000 samples

Epoch 1/20 - 2s - loss: 0.3787 - acc: 0.8882 - val_loss: 0.0461 - val acc: 0.9862

Epoch 2/20 - 1s - loss: 0.0605 - acc: 0.9816 - val_loss: 0.0341 - val acc: 0.9894

Epoch 3/20 - 1s - loss: 0.0430 - acc: 0.9868 - val_loss: 0.0271 - val acc: 0.9910

Epoch 4/20 - 1s - loss: 0.0343 - acc: 0.9892 - val_loss: 0.0253 - val acc: 0.9916

Epoch 5/20 - 1s - loss: 0.0284 - acc: 0.9917 - val_loss: 0.0249 - val acc: 0.9923

Epoch 6/20 - 1s - loss: 0.0258 - acc: 0.9920 - val_loss: 0.0245 - val acc: 0.9917

Epoch 7/20 - 1s - loss: 0.0212 - acc: 0.9933 - val_loss: 0.0246 - val acc: 0.9925

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 8/20 - 1s - loss: 0.0194 - acc: 0.9940 - val_loss: 0.0240 - val acc: 0.9927

Epoch 9/20 - 1s - loss: 0.0181 - acc: 0.9942 - val_loss: 0.0240 - val acc: 0.9935

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 10/20 - 1s - loss: 0.0161 - acc: 0.9948 - val_loss: 0.0232 - val acc: 0.9931

Epoch 11/20 - 1s - loss: 0.0145 - acc: 0.9956 - val_loss: 0.0228 - val acc: 0.9935

Epoch 12/20 - 1s - loss: 0.0130 - acc: 0.9958 - val_loss: 0.0238 - val acc: 0.9926

Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 13/20 - 1s - loss: 0.0132 - acc: 0.9959 - val_loss: 0.0233 - val acc: 0.9922

Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 14/20 - 1s - loss: 0.0118 - acc: 0.9964 - val_loss: 0.0226 - val acc: 0.9925

Epoch 15/20 - 1s - loss: 0.0108 - acc: 0.9968 - val_loss: 0.0231 - val acc: 0.9921

Epoch 00015: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 16/20 - 1s - loss: 0.0103 - acc: 0.9968 - val_loss: 0.0236 - val acc: 0.9923

Epoch 00016: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 17/20 - 1s - loss: 0.0101 - acc: 0.9970 - val_loss: 0.0223 - val acc: 0.9930

Epoch 18/20 - 1s - loss: 0.0081 - acc: 0.9976 - val_loss: 0.0234 - val acc: 0.9925

Epoch 00018: ReduceLROnPlateau reducing learning rate to 0.001.
Epoch 19/20 - ls - loss: 0.0092 - acc: 0.9971 - val_loss: 0.0234 - val acc: 0.9931

Epoch 00019: ReduceLROnPlateau reducing learning rate to 0.001.

Epoch 20/20 - ls - loss: 0.0072 - acc: 0.9979 - val_loss: 0.0230 - val acc: 0.9929

Epoch 00020: ReduceLROnPlateau reducing learning rate to 0.001.

2020-07-08 09:30:59 DEBUG : Metrics(Test loss & Test Accuracy): [0.022286349659936967, 0.9929999709129333]

2020-07-08 09:30:59 DEBUG : Mutant ID: 1594174369341240832

2020-07-08 09:30:59 INFO : Current scores - TrnFitness: 0.9979000091552734 TstFitness: 0.9929999709129333
Scaled_TstFitness: 0.9929999709129333

<table><thead><tr><th></th><th>val_loss</th><th>val acc</th><th>loss</th><th>acc</th><th>lr</th></tr></thead><tbody><tr><td>0</td><td>0.046127</td><td>0.9862</td><td>0.378722</td><td>0.888183</td><td>0.001</td></tr><tr><td>1</td><td>0.034115</td><td>0.9894</td><td>0.060486</td><td>0.981633</td><td>0.001</td></tr><tr><td>2</td><td>0.027079</td><td>0.9910</td><td>0.042975</td><td>0.986750</td><td>0.001</td></tr><tr><td>3</td><td>0.025305</td><td>0.9916</td><td>0.034269</td><td>0.989200</td><td>0.001</td></tr><tr><td>4</td><td>0.024907</td><td>0.9923</td><td>0.028406</td><td>0.991667</td><td>0.001</td></tr><tr><td>5</td><td>0.024467</td><td>0.9917</td><td>0.025763</td><td>0.992050</td><td>0.001</td></tr><tr><td>6</td><td>0.024609</td><td>0.9925</td><td>0.021168</td><td>0.993283</td><td>0.001</td></tr><tr><td>7</td><td>0.023964</td><td>0.9927</td><td>0.019368</td><td>0.994000</td><td>0.001</td></tr><tr><td>8</td><td>0.024006</td><td>0.9935</td><td>0.018134</td><td>0.994167</td><td>0.001</td></tr><tr><td>9</td><td>0.023168</td><td>0.9931</td><td>0.016070</td><td>0.994850</td><td>0.001</td></tr><tr><td>10</td><td>0.022761</td><td>0.9935</td><td>0.014471</td><td>0.995567</td><td>0.001</td></tr><tr><td>11</td><td>0.023797</td><td>0.9926</td><td>0.012993</td><td>0.995817</td><td>0.001</td></tr><tr><td>12</td><td>0.023289</td><td>0.9922</td><td>0.013192</td><td>0.995900</td><td>0.001</td></tr><tr><td>13</td><td>0.022557</td><td>0.9925</td><td>0.011834</td><td>0.996417</td><td>0.001</td></tr><tr><td>14</td><td>0.023121</td><td>0.9921</td><td>0.010753</td><td>0.996767</td><td>0.001</td></tr><tr><td>15</td><td>0.023588</td><td>0.9923</td><td>0.010335</td><td>0.996817</td><td>0.001</td></tr><tr><td>16</td><td>0.022286</td><td>0.9930</td><td>0.010078</td><td>0.997050</td><td>0.001</td></tr><tr><td>17</td><td>0.023367</td><td>0.9925</td><td>0.008122</td><td>0.997583</td><td>0.001</td></tr><tr><td>18</td><td>0.023446</td><td>0.9931</td><td>0.009218</td><td>0.997083</td><td>0.001</td></tr><tr><td>19</td><td>0.023028</td><td>0.9929</td><td>0.007235</td><td>0.997900</td><td>0.001</td></tr></tbody></table>

2020-07-08 09:31:01 DEBUG : get_Shannon(Model)

2020-07-08 09:31:01 DEBUG : Model Entropy: 8.783628463745117

2020-07-08 09:31:01 DEBUG : Model Entropy normalized: 0.001425219611186941

2020-07-08 09:31:01 DEBUG : Layers Sum Entropy: 127.6383867263794

2020-07-08 09:31:01 DEBUG : Layer Entropy: ['8.783628', '8.232107', '15.136115', '14.732756', '11.657583', '14.119755', '14.112352', '11.707453', '16.723648', '12.432989']

2020-07-08 09:31:01 DEBUG : Layer Entropy normalized: ['2.1959071159362793', '2.7440357208251953', '3.7840287685394287', '0.025577700800365873', '0.182149738073349', '3.5299386978149414', '3.528088092803955', '0.18292894959449768', '0.0035551972940665523', '0.016892648261526356']

2020-07-08 09:31:01 DEBUG : Layer list: ['DV_250_500_1', 'DV_375_500_1', 'DV_438_500_1', 'DV_625_500_2', 'DV_812_500_5', 'DV_625_750_2', 'DV_625_625_2', 'DV_844_500_4', 'DV_750_250_5', 'DV_1000_500_22']

2020-07-08 09:31:01 DEBUG : out get_Shannon(Model)

2020-07-08 09:31:01 INFO : ######## End of step index : 17 of test 32 ################################

2020-07-08 09:31:01 INFO : ################################

2020-07-08 09:31:01 INFO : ######## Start Evolutionary Step Index: 18 Test : 32/40 ################################

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: GaussianDropout

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_19"

<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_14 (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_14[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_15 (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_15[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_35 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_750_2[0][0]</td></tr><tr><td>flatten_36 (Flatten)</td><td>(None, 1568)</td><td>0</td><td>DV_625_625_2[0][0]</td></tr><tr><td>flatten_37 (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_35[0][0]<br>flatten_36[0][0]<br>flatten_37[0][0]</td></tr><tr><td>DV_875_500_10 (GaussianDropout)</td><td>(None, 84)</td><td>0</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_4 (AlphaDropout)</td><td>(None, 84)</td><td>0</td><td>DV_875_500_10[0][0]</td></tr><tr><td>DV_938_750_1 (Dense)</td><td>(None, 64)</td><td>4096</td><td>DV_750_500_10[0][0]</td></tr><tr><td>DV_1000_500_25_C (Concatenate)</td><td>(None, 840)</td><td>0</td><td>DV_750_250_7[0][0]<br>dropout_15[0][0]<br>DV_625_500_2[0][0]<br>DV_938_500_4[0][0]<br>DV_938_750_1[0][0]</td></tr><tr><td>DV_1000_500_25 (Dense)</td><td>(None, 10)</td><td>8410</td><td>DV_1000_500_25_C[0][0]</td></tr></tbody></table>

Total params: 405,486
Trainable params: 405,486
Non-trainable params: 0

None
2020-07-08 09:31:03 INFO : Original scores - TrnFitness: 0.9932000041007996 ValFitness: False TstFitness: 0.9940999746322632 Scaled_TstFitness: 0.9944000244140625
Train on 60000 samples, validate on 10000 samples
Epoch 1/20 - 2s - loss: 0.2786 - acc: 0.9205 - val_loss: 0.0386 - val_acc: 0.9879
Epoch 2/20 - 1s - loss: 0.0409 - acc: 0.9873 - val_loss: 0.0283 - val_acc: 0.9908