How Backpropagation Works with Artificial Neural Networks
What is backpropagation?
Backpropagation is an abbreviation which originally stands for “backward propagation of errors”. It is a technological method used in training artificial neural networks. Artificial neural networks are ideally used to solve problems that cannot be solved using the traditional computational methods. The artificial neural networks learn from different inputs how to carry out desired tasks and generate varied outputs. Backpropagation is generally used in situations where there are huge sets of input or output data yet it is difficult to relate this to the output.
In general, backpropagation is a supervised learning method that generalizes the delta rule and works with a particular dataset of outputs. These desired outputs for the different inputs form the training set for the artificial neural networks. Backpropagation is very useful in training of feed forward networks and it requires for activation functions used in the artificial neurons to be easily differentiated. Most networks that use backpropagation therefore do not have feedback.
Phases of backpropagation
The backpropagation learning is carried out using specific algorithm. This algorithm is divided into two major phases. During training, each of these phases is repeated until the performance of the artificial neural network is satisfactory.
- This is also divided into two steps. The forward propagation is the first step and it is where the training pattern’s input is fed through the neural network so that the propagation’s desired output activations can be generated. The backward output is the second step and this involves generating the deltas of every output and hidden neuron by propagating output activations using training pattern targets.
- Weight update. There are two steps involved here. First, the output delta is multiplied with the input activation
to get the gradient of the weight. Then the ration or percentage of the gradient is subtracted from the weight. In this case, the ratio is important because it determines the learning rate as it influences the quality and speed of learning. A greater ratio therefore indicates faster training for the neutrons. A lower ratio on the other hand indicates high accuracy of the training. The gradient sign of the weight can be positive or negative and it will indicate areas where the errors are on an increase. The weight must therefore be updated in opposite direction.
Modes of learning in backpropagation
In backpropagation, there are three modes of learning that can be used with the neural networks.
- The online mode of learning. This mode of learning is preferred with dynamic environments especially those that provide continuous streams of new patterns during training. Here the propagation is immediately followed by a weight update.
- Stochastic mode of learning. This is used with more static patterns. This method goes through the data sets in a random order. It is also fast because like the online mode of learning, the weights are updated immediately after the propagation.
- Batch mode. It is more or less similar to the stochastic mode of learning in that it also makes use of static patterns. However in this mode of learning, several propagations must occur before the weights are updated.
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