Digit Characterisation with Neural Networks: Optimisation Algorithms

This post is part of the series Neural Networks.

In my last post I introduced the application of neural networks for handwritten digit recognition. A key part of training such a network is the optimisation algorithm used. As explained in Neural Network Training, training involves the minimisation of a cost function, which is typically achieved through iteratively stepping in the direction of falling cost.

Gradient Descent

One of the simplest algorithms achieving this is the gradient descent algorithm. This algorithm uses the gradient of the cost function at the current position to determine the direction of most rapidly decreasing cost, and takes a step in that direction. This process is repeated until the minimum is found.

Supposing you are at some initial position \(x_n\), and the gradient of the cost function \(C(x)\) is \(\nabla C(x)\), the estimate of the minimum is iteratively improved by the rule:

\[x_{n+1} = x_{n} - \alpha \nabla C(x_n)\]

The coefficient \(\alpha\) is the learning rate of the algorithm. With a large \(\alpha\), larger steps towards the minimum will be taken, meaning that the solution may be found faster, however if the minimum is overshot there may be oscillations or divergence, depending on the shape of the cost function. A smaller \(\alpha\) is safer, but may require an unreasonably large number of iterations to find the minimum.

Which learning rate to use is application-specific, depending as stated on the shape of the cost function being minimised. It is therefore wise to try out a selection of learning rates, selecting the one that most quickly finds the minimum of the cost function without oscillations.

Application to Digit Characterisation

Using the same setup as in my last post, training of the digit characterisation neural network is achieved using the train method:

network.train(train_features, train_labels)

train internally uses the optimisation package of SciPy to perform the optimisation of the cost function. However, while SciPy provides a number of more advanced optimisation algorithms, it does not provide standard gradient descent. I have therefore provided a gradient descent algorithm along with my neural network code.

To use gradient descent with train, first generate the algorithm with the desired learning rate:

from neuralnetwork.gradientdescent import gradient_descent
alpha = 0.1
optimiser = gradient_descent(alpha)

Then pass it as the optimiser optional argument to train:

network.train(train_features, train_labels,

I performed optimisation with gradient descent with a regularisation parameter of \(\lambda=1\) for 50 iterations with learning rates \(\alpha\) of 0.1, 0.2, 0.4, 0.6 and 1.0. The graph below shows how the cost function for each learning rate converged over time:

Learning Rate

Looking at this chart, we see that the largest learning rate of \(\alpha=1.0\) resulted in a lot of oscillations and slow convergence. The smallest learning rate of \(\alpha=0.1\) was better, but also had slower convergence than some of the intermediate values. I selected \(\alpha=0.4\) as the best learning rate here, as it converged quickly without oscillations.

Comparison with other Optimisers

Using the SciPy optimisation package as the framework for our optimisation process was not accidental. While it does not supply standard gradient descent as an optimiser, it does provide a number of other, more advanced optimisers that could be used in training our neural network.

In particular, the Conjugate Gradient and Newton-CG optimisers are relevant to our problem. They are both unconstrained, take advantage of our efficient method for calculation of the cost function gradient, but do not require calculation of the second derivatives. Using the same setup as with the gradient descent training above, I trained using these algorithms for 50 iterations with a regularisation parameter of \(\lambda=1\). The graph below shows the evolution of the cost using each of these methods and with gradient descent with the optimal learning rate discovered above.

Comparison of Optimisers

Here we can see that the Conjugate Gradient method results in significantly better convergence than either the gradient descent or Newton-CG methods. Usefully, these methods also do not require a choice of learning rate.

The implementation of the Conjugate Gradient optimiser is beyond the scope of this blog post, but we have discovered that it offers significant improvements over standard gradient descent when training our neural network to recognise handwritten digits. In future posts we will look at choice of regularisation parameter and network design with the goal of further improving the accuracy of our digit classifier.