Abstract:
The neural network algorithms are used to train different models for forecasting,
classification, interpret, and analyze the results. The motive of the
study was to train the data set with different neural network algorithms and
check the classification accuracy and make comparison, which algorithm
is best among all of them. The "German traffic sign" dataset is used to
classify the different traffic signs. Each algorithm run for thirty times with
different number of layers and hidden layers. A neural network depends on
the learning rate, hidden layers, layers and activation function. The network
gave calibration values, validation values, different results for different
number of layers, and hidden layers. Smallest absolute gradient gave the
best results for calibration (87.73) and validation (84.05) and at 2, 3,and 4
number of layers it gave the best classification accuracy. Smallest learning
rate gave the least results for calibration (83.34) and validation (78.23). The
outcome suggested that although the differences among the algorithms are
not big, the SAG gave the highest classification accuracy.
