Tensorflow

This is a problem sheet that was given to me for the module Emerging Technologies by lecture Ian McLoughlin. The course module can be located here.

TensorFlow is a software framework for building and deploying machine learning models. Machine learning is a different approach than traditional programming. With traditional programming, we write the program that tells the computer exactly what to do to complete the task. With machine learning, we don't explicitly tell the computer how to do something. Instead we show a training data and the machine learning algorithm uses the training data that come up with its own rules to complete the task.

To install Tensorflow on your machine, follow the instructions on the TensorFlow Website for your operating system.

Keras

In this problem sheet I also used Keras.

To install Keras you can simply open a command promt and enter the following command.

• pip install keras

Jupyter

Jupyter Notebook The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

Python

Python is developed under an OSI-approved open source license, making it freely usable and distributable, even for commercial use. Python's license is administered by the Python Software Foundation.

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How to run

You can simply go to Tensorflow.ipynb and my answers to the problem sheet will appear in a jupyter notebook.

OR

You can clone the repository, and run the Tensorflow.ipynb on your own machine.

You must make sure you have Jupyter Notebook downloaded and working on your machine.

Then open your command prompt and navigate to this repository.

Run jupyter notebook and go to Tensorflow.ipynb.

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Problem set: Tensorflow

These problems relate to the Python package Tensorflow. We will again use the famous iris data set. Save your work as a single Jupyter notebook file in a GitHub repository. Include any required data files, a README, and a gitignore file in the repository.

1. Use Tensorflow to create model

• Use Tensorflow to create a model to predict the species of Iris from a flower’s sepal width, sepal length, petal width, and petal length.

2. Split the data into training and testing

• Split the data set into a training set and a testing set. You should investigate the best way to do this, and list any online references used in your notebook. If you wish to, you can write some code to randomly separate the data on the fly.

3. Train the model

• Use the testing set to train your model.

4. Test the model

• Use the testing set to test your model, clearly calculating and displaying the error rate.