This Repository contains my own Convolutional Neural Network usable for Object Dection within Images or to decide over even simpler Tasks.
For the Sake of Simplicity, I've developed the CNN using pure and Object-Oriented Java.
At First, let's have a Look at the Config.
The first Constants are used after each Training Epoch, when the Learning Rate will be changed.
STARTING_LEARNING_RATE: Initial Learning RateADJUST_LEARNING_RATE_DIFFandADJUST_LEARNING_RATE: At the Start of an Epoch, the Accuracy Difference of the previous and the Epoch before will be compared. If the absolute Difference is smaller or equal toADJUST_LEARNING_RATE_DIFF, the Learning Rate for this Epoch will be multiplied by (1 +ADJUST_LEARNING_RATE). Else, it will be divided by (1 +ADJUST_LEARNING_RATE).LEARNING_RATE_MINandLEARNING_RATE_MAX: To prevent the Learning Rate from getting too small or large, these Constants define the lower and upper Limit of the Learning Rate.INERTIA: To prevent the Learning Rate from changing too much each Epoch, the Inertia is used to take a part of the previous Learning Rate to the next Epoch. The Parts will be averaged out. Not used by now.
Next, the initial Weights are set with
DEFAULT_WEIGHT_MINandDEFAULT_WEIGHT_MAX: Every single Weight will start with a Value between both of the Constants.
You can also set Training Limits:
AMING_ACCURACY: When the Model reached an Accuracy higher than this Value during Training, the Training Process will stop and be considered as a Success.MAX_TRAINING_ITERATIONS: When the Model trained with more Epochs than this Value, the Training Process will be aborted. The Amount of Epochs that are necessary for a successful Training will vary, depending on the Problem Complexity and Training Dataset Length.
The last Section is used for Debugging Settings.
LOGGING_LEVEL: Select how detailed the Debugging Messages will be.
Instantiate a Neural Network with
CNN cnn = new CNN();
After that, you can add Layers by using
cnn.addLayer(Layer layer);