# Blue Read Tool Training Parameters

The Training tool parameters control the training process. If any changes are made to the Training tool parameters after the tool has been trained, it will invalidate the training and necessitate that the tool be retrained.

Parameter | Description
--- | ---
Training Set | Launches the Training Set dialog, which is used to specify the sample sets and percentage of labeled images to randomly select as training samples for the neural network each time a new training is initiated.
Epoch Count | Specifies the number of optimization iterations done during training. This setting can be reduced when your application has limited complexity, or if a lower quality model may be useful when testing different parameter settings. An epoch is the term for passing your entire Training Set into the neural network. The tools typically need to see the training image set data somewhere on the order of 50 times (and is in the Fine Tuning area), which is the default setting, and is typically sufficient for most standard applications. Choosing to use fewer epochs could result in the neural network being stuck in learning, or unable to accurately solve the problem, while too many epochs could result in overfitting (aka over-training) the results (in other words, it will learn only the trained images, and anything outside of the trained images will be considered invalid). It is important to train the network to the point where it is generalized off of your training image set. If you increase the epochs too much, you run the risk of over-training and overfitting the training image set data. Tip: If your training image set contains a large number of images, or the size of the images in your training image set are large compared to the Feature Size, you may need to increase the Epoch Count setting. There is a risk of overfitting if you have a very small training image set. If you have a large training image set, and your network is addressing all of the samples in the set, the network is much more general than if you have a smaller training image set and the network is keyed into that small sample set.