Category Archives: Neural network

Logistic Regression – from Nspire to R to Theano

Logistic regression is a very powerful tool for classification and prediction. It works very well with linearly separable problem. This installment will attempt to recap on its practical implementation, from traditional perspective by maximum likelihood, to more machine learning approach by neural network, as well as from handheld calculator to GPU cores.

The heart of the logistic regression model is the logistic function. It takes in any real value and return value in the range from 0 to 1. This is ideal for binary classifier system. The following is a graph of this function.

TI Nspire

In the TI Nspire calculator, logistic regression is provided as a built-in function but is limited to single variable. For multi-valued problems, custom programming is required to apply optimization techniques to determine the coefficients of the regression model. One such application as shown below is the Nelder-Mead method in TI Nspire calculator.

Suppose in a data set from university admission records, there are four attributes (independent variables: SAT score, GPA, Interview score, Aptitude score) and one outcome (“Admission“) as the dependent variable.

Through the use of a Nelder-Mead program, the logistic function is first defined as l. It takes all regression coefficients (a1, a2, a3, a4, b), dependent variable (s), independent variables (x1, x2, x3, x4), and then simply return the logistic probability. Next, the function to optimize in the Nelder-Mead program is defined as nmfunc. This is the likelihood function on the logistic function. Since Nelder-Mead is a minimization algorithm the negative of this function is taken. On completion of the program run, the regression coefficients in the result matrix are available for prediction, as in the following case of a sample data with [GPA=1500, SAT=3, Interview=8, Aptitude=60].



In R, as a sophisticated statistical package, the calculation is much simpler. Consider the sample case above, it is just a few lines of commands to invoke its built-in logistic model.



Apart from the traditional methods, modern advances in computing paradigms made possible neural network coupled with specialized hardware, for example GPU, for solving these problem in a manner much more efficiently, especially on huge volume of data. The Python library Theano is a complex library supporting and enriching these calculations through optimization and symbolic expression evaluation. It also features compiler capabilities for CUDA and integrates Computer Algebra System into Python.

One of the examples come with the Theano documentation depicted the application of logistic regression to showcase various Theano features. It first initializes a random set of data as the sample input and outcome using numpy.random. And then the regression model is created by defining expressions required for the logistic model, including the logistic function and likelihood function. Lastly by using the theano.function method, the symbolic expression graph coded for the regression model is finally compiled into callable objects for the training of neural network and subsequent prediction application.


A nice feature from Theano is the pretty printing of the expression model in a tree like text format. This is such a feel-like-home reminiscence of my days reading SQL query plans for tuning database queries.



Exploring Theano with Keras

Theano needs no introduction in the field of deep learning. It is based on Python and supports CUDA. Keras is a libray that wraps the complexity of Theano to provide a high level abstraction for developing deep learning solutions.

Installing Theano and Keras are easy and there are tons of resources available online. However, my primary CUDA platform is on Windows so most standard guides that are based on Linux required some adaptations. Most notably are the proper setting of the PATH variable and the use of the Visual Studio command prompt.

The basic installation steps include setting up of CUDA, a scientific python environment, and then Theano and Keras. CuDNN is optional and required Compute Capability of greater than 3.0 which unfortunately my GPU is a bit old and does not meet this requirement.


Some programs on Windows platform encountered errors and found to be library related issues. Like this one that failed to compile on Spyder can be resolved using the Visual Studio Cross Tool Command Prompt.

The Nvidia profiler checking for the performance of the GPU, running the Keras example of the MNIST digits with MLP.keras2

Training neural network using Nelder-Mead algorithm on TI Nspire

In this installment the Nelder-Mead method is used to train a simple neural network for the XOR problem. The network consisted of 2-input, 1-output, and 2 hidden layers, and is fully connected. In mainstream practical neural network, back propagation and other evolutionary algorithms are much more popular for training neural network for real world problem. Nelder-Mead is used here just out of curiosity to see how this general optimization routine performed under neural network settings on TI Nspire.

The sigmoid function is declared in an TI Nspire function.

For the XOR problem, the inputs are defined as two lists, and the expected output in another.

The activation functions for each neuron are declared.

To train the network, the sum of squared error function is used to feed into the Nelder-Mead algorithm for minimization. Random numbers are used for initial parameters.

Finally the resulting weights and bias are obtained from running the Nelder-Mead program.

The comparison graph of the performance of the Nelder-Mead trained XOR neural network against expected values.