@@ -30,23 +30,23 @@ class CnnModel {
3030 this .properties = properties ;
3131
3232 MultiLayerConfiguration configuration = new NeuralNetConfiguration .Builder ()
33- .seed (1611 )
34- .optimizationAlgo (OptimizationAlgorithm .STOCHASTIC_GRADIENT_DESCENT )
35- .learningRate (properties .getLearningRate ())
36- .regularization (true )
37- .updater (properties .getOptimizer ())
38- .list ()
39- .layer (0 , conv5x5 ())
40- .layer (1 , pooling2x2Stride2 ())
41- .layer (2 , conv3x3Stride1Padding2 ())
42- .layer (3 , pooling2x2Stride1 ())
43- .layer (4 , conv3x3Stride1Padding1 ())
44- .layer (5 , pooling2x2Stride1 ())
45- .layer (6 , dense ())
46- .pretrain (false )
47- .backprop (true )
48- .setInputType (dataSetService .inputType ())
49- .build ();
33+ .seed (1611 )
34+ .optimizationAlgo (OptimizationAlgorithm .STOCHASTIC_GRADIENT_DESCENT )
35+ .learningRate (properties .getLearningRate ())
36+ .regularization (true )
37+ .updater (properties .getOptimizer ())
38+ .list ()
39+ .layer (0 , conv5x5 ())
40+ .layer (1 , pooling2x2Stride2 ())
41+ .layer (2 , conv3x3Stride1Padding2 ())
42+ .layer (3 , pooling2x2Stride1 ())
43+ .layer (4 , conv3x3Stride1Padding1 ())
44+ .layer (5 , pooling2x2Stride1 ())
45+ .layer (6 , dense ())
46+ .pretrain (false )
47+ .backprop (true )
48+ .setInputType (dataSetService .inputType ())
49+ .build ();
5050
5151 network = new MultiLayerNetwork (configuration );
5252 }
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