Birdsong.report B1 Model Classification Quality

Here are some important notices about the classification quality of B1 model currently deployed at (birdsong.report)

I made per class classification quality analysis (don’t know why I did not do it before!) and the results are both impressive and distressing.
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Anomaly detection system. Case study

The problem

There is a coldproof box with electronics (e.g. smart house control center).
We need to detect any environmental anomalies inside the box-case like overheating, coldproof failure or any other.

The plan

  1. The first thing to do is to gather some measurements data of normal system operation.
  2. Define a probabilistic model of normal operation.
  3. Continue reading Anomaly detection system. Case study

My bird song classifier

In this post I describe the neural network architecture that I try as a bird song classifier.

Intro

When I first came up with an idea of building a bird song classifier I started to google for the training dataset.
I found xeno-canto.org and the first thing that caught my attention was spectrograms.
spectorgram
(Sepctrogram is visual representation of how spectrum evolves through time. The vertical axis reflects frequency, the horizontal represents time. Bright pixels on the spectrogram indicate that for this particular time there is a signal of this particular frequency)

Well, spectrograms are ideal for visual pattern matching!
Why do I need to analyse sound when we have such expressive visual patterns of songs? That was my thoughts.
I decided to train neural net to classify spectrograms.

Continue reading My bird song classifier

Multilayer perceptron learning capacity

In this post I demonstrate how the capacity (e.g. classifier variance) changes in multilayer perceptron classifier with the change of hidden layer units.
As a training data I use MNIST dataset published on Kaggle as a training competition.
The network is multiclass classifier with single hidden layer, sigmoid activation.

Multi-class logarithmic loss

loss function achieved

The plot shows the minimum value of loss function achieved across different training runs.
Each dot in the figure corresponds to a separate training run finished by stucking in some minimum of the loss function.
You may see that the training procedure can stuck in the local minimum regardless of the hidden units number.
This means that one needs to carry out many training runs in order to figure out real network architecture learning capacity.
The lower boundary of the point cloud depicts the learning capacity. We can see that learning capacity slowly rises as we increase the number of hidden layer units.

Classification accuracy

Learning capacity is also reflected into the achieved accuracy.

Accuracy acieved

In this plot, as in the previous one, each of the dot is separate finished training run.
But in this one Y-axis depicts classification accuracy.
Interesting that the network with even 10 units can correctly classify more than a half of the images.
It is also surprising for me that the best models cluster together forming a clear gap between them and others.
Can you see the empty space stride?
Is this some particular image feature that is ether captured or not?

Solar wind simulation: particle bursts engine

Intro

The Solar Wind Particle Burst Engine models the solar wind by simulating bursts of particles emitted by coronal holes. The idea is to simulate continuous flow of particles using discrete representation of the world. We can represent the wind as a finite number of particle bursts. The world space is one dimensional. It is represented with finite number of bins. The bins are enumerated with index. Greater the index, greater the distance of the bin from the Sun. Therefore the bin with index 0 corresponds to the Sun surface. Each bin at every particular time moment can “contain” zero or more particle bursts. At every world time tick (the time is modelled in a discrete way in form of integer ticks) the particle bursts move out of the Sun by leaving one bin and getting into another.
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Solar wind observations by EPAM ACE

This is a part of study described in the dedicated post.

We have per minute solar wind observations (density, temperature and velocity) recorded by EPAM instrument of Advanced Composition Explorer (ACE) spacecraft. (see this link for data files)

This data from ACE can be used for two purposes. First, we can use this particular archive of observations (e.g. 2015 year) to fit the prediction model parameters. Second, we can use very recent measurements coming from ACE as predictors for forecasting the wind velocity for near future.
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