If the Neural Network you trained did not converge, you can change the parameters that control the convergence. The number of classifier layers is not that important. The choice of activation function can make the difference in performance. Relu is better than tanh because it can better represent complex data. Stochastic gradient descent can be used to improve convergence. You can reduce the number of layers by smoothing out the data.
Stochastic gradient descent does not have a fast convergent time, but it can be used with a mini batch to speed up the convergence. If you choose this technique, you should consider setting the error threshold low. In this way, the model will converge faster. You can also use a mini-batch, which can be faster. Finally, you can try using a small number of sensors.
In order to determine if the Neural Network did not converge, you need to check its accuracy. The training error threshold is 0.0001; it is around 0.002 errors. If it converges, the neural network is able to accurately predict a target object. If you are not able to converge with a training dataset, you can try retraining the system. A larger number of sensors will reduce the prediction error.
There are several reasons why the Neural Network did not converge. First, it did not know the last class it trained. It also did not predict the last class it trained, which is called the “dying ReLU”. Then, it was not able to converge consistently in under 100 iterations. Moreover, it requires simple data and a lot of luck for a NN to converge consistently.
When the training data set is not linear, the neural network will not converge. The perceptron error is the measure of the network’s performance on the training and testing sets. It can be measured with a loss function, which measures the loss of the algorithm. However, if the data set is not linear, the NN will not converge. When the data set is not simple, the network cannot converge.
The second reason is that NNs cannot predict the last class. This is a problem with ReLU activation and the last class. This results in high variability in prediction. The NNs have very different predictions in different points. The NNs T7 and T8 are good sensors, but T3 is a poor sensor. The difference between NNs is due to the small number of effective data.
The second reason is that the neural network did not converge. In fact, it did not converge to the designated error threshold. The neural network did not converge to the error threshold. The training error in case II was 0.0001. These two cases were not generalizable. In case I and II, the neural network did not assemble a full set of sensors. Its prediction error was large, which indicates that the number of sensor epochs it took was too small.
The convergent error threshold for the case I is very high. In case II, the number of sensors used to train the neural network is too low. In contrast, the number of epochs needed to reach a ‘good’ model is low. For the second case, the ‘good’ sensor is T7. But in case of case II, the prediction error is not high.
In case I, the prediction error is large. This is a result of the insufficient number of sensors. Although, the number of sensors used in case II decreased, the neural network did not converge. As the number of sensors increased, the prediction error did not decrease. In case II, the training error is small. Therefore, the prediction is not a good quality. If the neural network has a large prediction and is not able to converge, the model will not be a good one.
Adaptive and Reactive convergence are not the same thing. Adaptive converge refers to weights that are adjusted during supervised training. During the training process, the gradient will gradually become stable and no further training will improve it. Then, the model is not able to learn anything new from the data. This is a problem of overfitting. If this occurs, you must fix the model.