Multiple Back-Propagation is an easy to use application specially designed for the training of neural networks.
This is done with the help of the Back-Propagation and the Multiple Back-Propagation algorithms.
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Multiple Back-Propagation
A back-propagation approach is the basis of using the gradient descent algorithm to minimise a cost function.
It is basically a probabilistic approach, where each input vector (or pattern) of the network is not directly linked to a single output vector, but to a large number of them.
It is this multiplicity, that is the key to its’multiple’ label.
Using back-propagation, each pattern is probabilistically associated with an output pattern that is equally likely, and on which it is evaluated.
During training, new patterns are tried to be associated with their expected output patterns, using the back-propagation algorithm.
When the number of training patterns is large enough, the neural network can be considered as learned.
The expectation is that the trained network can successfully guess future inputs that were not used during the training.
If all input patterns are successfully guessed, the neural network is considered as learned.
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To use the Multiple Back-Propagation algorithm, the problem to be solved must be represented as a function of vectorial input, output and hidden layers.
The input is called the set of states and is composed of a vector of input patterns, and a vector of initial states.
The output layer contains only one output, the target.
The number of states can be large, but it should not be smaller than the number of neurons of the hidden layer, otherwise, it would be difficult to estimate probabilities.
The training of the network is done in two stages.
First, the hidden layer neurons are trained by the Back-Propagation algorithm, using the Multiple Back-Propagation algorithm.
At this stage, the network is considered as a binary classifier, with the associated states as the class labels, or targets.
In a second stage, the output layer is trained with the multiple target, by the Multiple Back-Propagation algorithm, using the previously trained hidden layer.
Forward Propagation
This step consists in computing the probability of the network for each input pattern, given its associated state.
Let’s consider the following learning problem.
Given the input vectors
and the associated vector of initial states
compute the associated vector of the output states:
Notice that the target vector is composed of 1’s, 2’s or 0’s, but is not necessarily 0 or 1.
The idea
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The back-propagation algorithm is used for training neural networks.
This is done by manipulating the weight and the threshold of each neuron
through the genetic algorithm.
Multiple Back-Propagation algorithm is used for testing and grading the
accuracy of the neural network.
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An easy to use application specially designed for the training of neural networks.
It is used to train the Levenberg-Marquardt algorithm to converge to a solution.
The training is done in several iterations.
At the end of each iteration the training error is calculated.
An error that is less than a defined threshold is considered as a correct training.
It is convenient to set the threshold at a value between 2e-8 and 1e-5.
This is done in the input parameter “Threshold.
The training iterations are stored in the structure in the text file “episodes.txt”.
> Note: It is recommended to start with a small learning rate and train with a higher learning rate after the training finished.
Example:
> prog_exec(“MNIST_sbc.exe”)
> cd(“MNIST_BackPropagation”)
> sbc_app(“levenberg”,
“episodes”, “mnist_train_episodes.txt”,
“mnist_train_iterations.txt”,
“mnist_train_learning_rate”,
“mnist_train_threshold”,
“mnist_train_start”, “mnist_train_stop”)
> ctrl_c()
“Program executed without errors.”
Application parameters:
> In case of the Levenberg-Marquardt algorithm it is necessary to define
> the loss function and the differentiable functions:
> – loss – loss function
> – fun0 – f0 = 0
> – fun1 – f1 = 0
> – fun2 – f2 = y
> – fun3 – f3 = x^3
> – fun4 – f4 = x^4
> – fun5 – f5 = x^2*y
> In case of the Back-Propagation algorithm it is necessary to define
> the training algorithm:
> – algorithm – the Back-Propagation algorithm
> – algorithm_learning_rate – the learning rate for the Back-Propagation algorithm
> – algorithm_optim_method – the optimisation method for the Back-Propagation algorithm
> –
What’s New in the?
– When the training of an artificial neural network is done, we are using the back-propagation algorithm to train a feedforward neural network.
– In the back-propagation algorithm, an error is introduced to adjust the weights of the network to calculate the error.
– In the Multiple Back-Propagation algorithm, we introduce this error in multiple steps in order to gain more accuracy.
Multiple Back-Propagation algorithm
1. The Artificial Neural Network (ANN) is firstly pre-trained using the Back-Propagation algorithm.
2. Once the network is pre-trained, the Pre-training error is removed from the input.
3. Then an error is introduced in the Back-Propagation algorithm by considering the fact that the training data are incorrect.
4. A new training data is provided which is the combination of the old training data and this new error.
5. The Back-Propagation algorithm is run again with this new data.
6. The result of the last round is a trained network which has the error.
7. The error is reduced every time step.
8. The result of all these iterations is the Multiple Back-Propagation algorithm.
Applications
– The Multiple Back-Propagation algorithm is used for the training of the following:
– Multiple classification: A neural network is used to classify a given set of data in more than one class.
– Regression: The Neural network is used to give a function of one or more independent variables.
– Clustering: A neural network is used to classify the features of the data.
Jaro Distance application
The Jaro Distance application is an application designed by Hand-Eye Interface Software.
The program is used to evaluate similarity between two words and is used in visual programming languages like Visual Basic, Visual C++, Visual J++, Visual C#, Visual J#, Java and others.
The application allows the user to enter the word and to evaluate its similarity to another word.
The two words are placed in the input panel.
The result of the evaluation is a distance between the words or a similarity score.
The following buttons are displayed on the main window of the application:
– Jaro button (Jaro)
– Damerau button (Damerau)
– Jaro-Damerau button (JD)
– Levenshtein button (Levenshtein)
– Edit distance button (Edit)
– Word order button (Word order)
– MD5 (Hash) button (MD5)
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System Requirements For Multiple Back-Propagation:
Windows 10 (64-bit); macOS 10.12.6 or later;
RAM: 4GB;
Processor: Intel Core i3 (2.2GHz or faster) or AMD Phenom II X2 (2.7GHz or faster);
Graphics: Nvidia Geforce GTS 450, AMD Radeon HD 5770 or better;
Hard Disk: 4GB free space;
Sound Card: DirectX 9.0c compatible sound card.
How to Play:
Gameplay:
Suspension:
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