Can you over train a neural network?

In the specific case of neural networks, this effect is called overtraining or overfitting. Overtraining occurs if the neural network is too powerful for the current problem. It then does not “recognize” the underlying trend in the data, but learns the data by heart (including the noise in the data).

How do you increase the accuracy of a neural network?

Now we’ll check out the proven way to improve the performance(Speed and Accuracy both) of neural network models:

1. Increase hidden Layers.
2. Change Activation function.
3. Change Activation function in Output layer.
4. Increase number of neurons.
5. Weight initialization.
6. More data.
7. Normalizing/Scaling data.

What problem can happen if you over train a neural network?

One of the problems that occur during neural network training is called overfitting. The error on the training set is driven to a very small value, but when new data is presented to the network the error is large. The network has memorized the training examples, but it has not learned to generalize to new situations.

How do you determine when to stop training a neural network?

A neural network is stopped training when the error, i.e., the difference between the desired output and the expected output is below some threshold value or the number of iterations or epochs is above some threshold value.

How can I use two different dataset as a train and test set?

Something you can do is to combine the two datasets and randomly shuffle them. Then, split the resulting dataset into train/dev/test sets.

How can training accuracy be improved?

8 Methods to Boost the Accuracy of a Model

1. Add more data. Having more data is always a good idea.
2. Treat missing and Outlier values.
3. Feature Engineering.
4. Feature Selection.
5. Multiple algorithms.
6. Algorithm Tuning.
7. Ensemble methods.

How do you increase training speed in neural network?

Start with a very small learning rate (around 1e-8) and increase the learning rate linearly. Plot the loss at each step of LR. Stop the learning rate finder when loss stops going down and starts increasing.