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Project 3: Traffic Sign Classifier
I read the dataset using the pickle library in Python. The original dataset features:
The number of examples of each class has been represented in the bar chart below. Many classes have very less data as compared to other classes. Hence, I have used the function Generatedata to make sure that all classes have at least 800 examples. Generate data calls two functions warp and scale which randomly apply transformations on images. A few examples have been shown below. The original dataset comprises of images as shown below. The label of the image has been indicated above them. Figure 1 Bar Chart Figure 2 Examples
Pre-processing the original is a very important technique in Machine Learning. In my model I decided to convert the image into grayscale. On observing the results, I realised that some images were simply too dark. I found it difficult to myself classify these traffic signs. To further process the images, I used an OpenCV function CLAHE (Contrast Limiting Adaptive Histogram Equalisation). It greatly improved the image contrast. I would like to draw your attention to the traffic signs in green box. Figure 5 Original Figure 4 Grayscale Figure 3 CLAHE The example in green box is very faint in the original dataset. After converting it into grayscale, it is barely recognizable. However once ve apply CLAHE, the sign is quite clearly visible. The data is then standardised so that all the values lie between 0 and 1. The image below shows some examples of images generated by the Generatedata function:
The architecture of the model is: o Convolution with input 32x32x1 and output 28x28x o Activation using RELU o Max pool with output 14x14x o Convolution with input 14x14x6 and output 10x10x 16 o Activation using RELU o Max pool with output 5x5x 16 o Convolution with input 5x5x16 and output 400 o Activation using RELU o Probability of .75 produced best results o Outputs of layer 2b and layer 2 were fed into this step o Fully connected with input 800 and output 400 o Activation using RELU o Fully connected with input 400 and output 2 00 o Activation using RELU o Fully connected with input 200 and output 43 o Activation using RELU Figure 6 Generated data
The following parameters were used while training the model:
The result is:
A Python library for working with and training HMMs with Poisson emissions.
There are two classes in this library:
PHMM creates a typical HMM with Poisson emissions, where every sequence is assumed to have been generated with the same Poisson parameters - i.e., if the HMM has three states with Poisson means of 1.0, 3.0, and 4.5, every sequence will be generated using those parameters.
PHMM_d creates a Poisson-emitting HMM where sequences can be generated with different Poisson parameters. Hence, the parameters are formatted as a nested array, where each subarray is the set of emission parameters for a single sequence, and the length of the overall array is the number of observation sequences you'd like to train. This allows for the training of a PHMM such that the state transition matrix is trained over all observation sequneces, but state magnitudes can differ from sequence to sequence.
Python client library for Training instance of the decentriq platform.
By using Confidential Computing, Confidential ML Training enables you to train machine learning models based on data that nobody ever can access; not you, not us, not the infrastructure provider, nobody. This removes the risk for data breaches or data misuse.
Tangent is a new, free, and open-source Python library for automatic differentiation.
Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) or by staging out a dynamic data-flow graph and then differentiating the graph (ahead-of-time, like TensorFlow). In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output. Tangent fills a unique location in the space of machine learning tools. As a result, you can finally read your automatic derivative code just like the rest of your program. Tangent is useful to researchers and students who not only want to write their models in Python, but also read and debug automatically-generated derivative code without sacrificing speed and flexibility. Tangent works on a large and growing subset of Python, provides extra autodiff features other Python ML libraries don't have, has reasonable performance, and is compatible with TensorFlow and NumPy. This project is an experimental release, and is under active development. As we continue to build Tangent, and respond to feedback from the community, there might be API changes.
PyFlux is an open source time series library for Python. The library has a good array of modern time series models, as well as a flexible array of inference options (frequentist and Bayesian) that can be applied to these models. By combining breadth of models with breadth of inference, PyFlux allows for a probabilistic approach to time series modelling. See some examples and documentation below. PyFlux is still only alpha software; this means you use it at your own risk, that test coverage is still in need of expansion, and also that some modules are still in need of being optimized.
anaGo is a Python library for sequence labeling(NER, PoS Tagging,...), implemented in Keras. anaGo can solve sequence labeling tasks such as named entity recognition (NER), part-of-speech tagging (POS tagging), semantic role labeling (SRL) and so on. Unlike traditional sequence labeling solver, anaGo don't need to define any language dependent features. Thus, we can easily use anaGo for any languages. As an example of anaGo, the following image shows named entity recognition in English:
Rigserve implements a human-readable control language for remote or local IP access to amateur radio equipment (rigs). A Python class library allows interfacing with a large number of rig types via serial ports or other methods.
GUI Dialog library for Python with a major goal of simplicity
EasyGUI is a module for very simple, very easy GUI programming in Python. EasyGUI is different from other GUI generators in that EasyGUI is NOT event-driven. Instead, all GUI interactions are invoked by simple function calls.
Python wrapper for Sword library. Helps python users access sword publications (mostly bibles, commentaries, etc). Also will include a supybot biblebot plugin. There is already a swig generated wrapper to pysword and python based bible reader here: http:
A Python library to create score files for CSound. It differs from other libraries that generate CSound scores, since a score is represented by a list that can contain different types of objects. It creates patterns in a flexible and compact way.
A python library and commandline tool for downloading shows from your Tivo. Does not require a hacked tivo. Combined with Tivodecode, it can decrypt the shows as well. I would recommend transcoding them after the fact with something like Handbrake for greater device compatibility.
WebDAV client library for python. The purpose of this project is to make a python WebDAV client library that is easy and convenient to use, while remaining flexible enough for developers who may want a finer grain of control. UPDATE: The test suite passes using Python 3.1 so we will now be supporting Python3 issues. The library will try to continue to work on both Python 2 and Python 3 until such a time that we find something that is not backwards compatible between the Python releases.
Python module to build dialogs for terminal-based applications
This is a Python module for doing terminal-based user interaction. It wraps the dialog/Xdialog program, and provides a nice, object-oriented programming model.
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For now, there are tremendous work opportunities for various IT fields. Most of the courses in Python Libraries is a great source of IT learning with hands-on training and experience which could be a great contribution to your portfolio.
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