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Application of Convolutional Neural Networks to Determine Induction Soldering Process Technological Stages

Table 1: Results of DenseNet Convolutional Neural Network Application

Performance ArchitectureDenseNet169
Number of parameters (Total) 12,649,540
Learning Parameter Count 12,491,140
Step 1: Adam Optimizer
Number of eras 15
Image size 224x224
Image count 2482
Packet size 16
Optimizer Adam
Learning speed 10-4
Loss function Categorical cross-entropy
Classifier function softmax
Exactitude 0.9965
Accuracy on test sample 0.9915
Learning time for one era 66с
Step 2: SGD Optimizer
Number of eras 15
Image size 224x224
Image count 2482
Packet size 16
Optimizer SGD
Learning speed 10-4
Loss function Categorical cross-entropy
Classifier function softmax
Exactitude 0.9662
Accuracy on test sample 0.9454
Learning time for one era 66c
Best Precision (Training) 0.9965
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