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2016-05-29 However, some thumb rules are available for calculating the number of hidden neurons. A rough approximation can be obtained by the geometric pyramid rule proposed by Masters (1993). For a three layer network with n input and m output neurons, the hidden … You can also use the geometric pyramid rule (the Masters rule): a) for one hidden layer the number of neurons in the hidden layer is equal to: nbrHID = sqrt(nbrINP * nbrOUT) found a simple network architecture with which the best accuracy can be obtained by increasing the network depth without increasing computational cost by much. We call it deep pyramid CNN. The pro-posed model with 15 weight layers out-performs the previous best models on six benchmark datasets for sentiment classiﬁ-cation and topic categorization. The learning behavior of artificial neural networks is characterized as a process of “gradient descent”, conducted through a back-propagation cycle. Through the iterations of the back-propagation cycle, every element of an artificial neural network moves an “error target” towards an asymptotic value, a process of ever-decreasing increments in learning for each subsequent cycle.

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H=Hidden Layer, I=Input , O=Output. I am going to use the geometric pyramid rule to determine the amount of hidden layers and neurons for each layer. The general rule of thumb is if the data is linearly separable, use one hidden layer and if it is non-linear use two hidden layers. I am going to use two hidden layers as I already know the non-linear svm produced the best model.

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Geometric deep learning builds upon a rich history of machine learning. The first artificial neural network, called "perceptrons," was invented by Frank Rosenblatt in the 1950s. Early "deep" neural networks were trained by Soviet mathematician Alexey Ivakhnenko in the 1960s. • Number of hidden nodes: There is no magic formula for selecting the optimum number of hidden neurons.

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