cs501r_f2016:lab1
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cs501r_f2016:lab1 [2017/09/08 16:08] humphrey |
cs501r_f2016:lab1 [2021/06/30 23:42] |
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| - | ====Objective:==== | ||
| - | |||
| - | Get started with anaconda, python, ipython notebooks, and pandas. Begin producing simple visualizations of data and images. | ||
| - | |||
| - | ---- | ||
| - | ====Deliverable:==== | ||
| - | |||
| - | For this lab, you will submit an ipython notebook. This notebook will have two parts: | ||
| - | |||
| - | **Part 1:** Your notebook should generate a random image. We will run this | ||
| - | notebook 5 times; it should generate 5 different, moderately complex | ||
| - | images. Each image should be 512 x 288. Have fun with it! | ||
| - | |||
| - | The resulting image could, for example, look like this: | ||
| - | |||
| - | {{:cs401r_w2016:lab1.png?nolink|}} | ||
| - | |||
| - | **Part 2:** You must play with the Tensorflow playground neural network, and figure out how to create a classifier that successfully classifies the "spiral" dataset. | ||
| - | |||
| - | [[http://playground.tensorflow.org/|Tensorflow playground]] | ||
| - | |||
| - | ---- | ||
| - | ====Grading standards:==== | ||
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| - | Your notebook will be graded on the following: | ||
| - | |||
| - | * 20% Successfully turned in a notebook with working code | ||
| - | * 35% Random image with 50 random elements | ||
| - | * 35% Image indicating tensorflow success | ||
| - | * 10% Tidy and legible figures, including labeled axes where appropriate | ||
| - | |||
| - | ---- | ||
| - | ====Description:==== | ||
| - | |||
| - | Throughout this class, we will be using a combination of ipython | ||
| - | notebooks, Tensorflow and the anaconda python distribution. For this lab, you | ||
| - | must install anaconda, and write a simple python program (using | ||
| - | ipython notebooks). | ||
| - | |||
| - | As described above, the notebook should do two things: | ||
| - | 1) generate simple random images, and 2) display an image that you generate using the Tensorflow playground. | ||
| - | |||
| - | For part 1, you can generate any sort of random image that you want -- consider | ||
| - | random lines, random curves, random text, etc. Each time the program | ||
| - | is run, it should generate a different random image. Your image | ||
| - | should have at least 50 random elements (they can all be the same | ||
| - | type, such as random lines, and can be created in a loop). We won't | ||
| - | count the number of elements; this is just to encourage you to create | ||
| - | random images with moderate complexity. | ||
| - | |||
| - | For part 2, you should visit the Tensorflow playground (see link above), and play with different settings. Most of it will be unfamiliar, but don't worry -- you can't break it! | ||
| - | |||
| - | Once you have a working classifier, take a screenshot. Then use your ipython notebook to display that image in-line. | ||
| - | |||
| - | ---- | ||
| - | ====Installing anaconda:==== | ||
| - | |||
| - | http://docs.continuum.io/anaconda/install | ||
| - | |||
| - | To generate images, check out PIL. | ||
| - | |||
| - | To generate random numbers, check out the [[http://docs.scipy.org/doc/numpy-1.10.0/reference/routines.random.html|numpy.random]] module. | ||
| - | |||
| - | To create a new notebook, run: | ||
| - | |||
| - | ''jupyter-notebook'' | ||
| - | |||
| - | This should start an ipython kernel in the background, set up a | ||
| - | webserver, and point your browser to the webserver. In the | ||
| - | upper-right corner, you will see a "new" menu; under that menu you | ||
| - | should see "Notebook" and "Python 2". This will create a new | ||
| - | notebook. | ||
| - | |||
| - | **Note:** When you turn in your notebook, you should turn in the ''.ipynb'' file. Do not take a screen shot, or turn in an HTML page. | ||
| - | |||
| - | Here's some starter code to help you generate an image. The ''nbimage'' function will display the image inline in the notebook: | ||
| - | |||
| - | <code python> | ||
| - | import cairo | ||
| - | import numpy | ||
| - | |||
| - | # A simple function to display an image in an ipython notebook | ||
| - | def nbimage( data ): | ||
| - | from IPython.display import display, Image | ||
| - | from PIL.Image import fromarray | ||
| - | from StringIO import StringIO | ||
| - | |||
| - | s = StringIO() | ||
| - | fromarray( data ).save( s, 'png' ) | ||
| - | display( Image( s.getvalue() ) ) | ||
| - | |||
| - | WIDTH = 512 | ||
| - | HEIGHT = 288 | ||
| - | |||
| - | # this is a numpy buffer to hold the image data | ||
| - | data = numpy.zeros( (HEIGHT,WIDTH,4), dtype=numpy.uint8 ) | ||
| - | |||
| - | # this creates a cairo context based on the numpy buffer | ||
| - | ims = cairo.ImageSurface.create_for_data( data, cairo.FORMAT_ARGB32, WIDTH, HEIGHT ) | ||
| - | cr = cairo.Context( ims ) | ||
| - | |||
| - | # draw a blue line | ||
| - | cr.set_source_rgba( 1.0, 0.0, 0.0, 1.0 ) | ||
| - | cr.set_line_width( 2.0 ) | ||
| - | cr.move_to( 0.0, 0.0 ) | ||
| - | cr.line_to( 100.0, 100.0 ) | ||
| - | cr.stroke() | ||
| - | |||
| - | # display the image | ||
| - | nbimage( data ) | ||
| - | </code> | ||
| - | |||
| - | ---- | ||
| - | ====Hints:==== | ||
| - | |||
| - | The following python functions might be helpful: | ||
| - | |||
| - | <code python> | ||
| - | |||
| - | import matplotlib.pyplot as plt | ||
| - | |||
| - | plt.legend | ||
| - | plt.xlabel | ||
| - | plt.ylabel | ||
| - | |||
| - | plt.tight_layout | ||
| - | |||
| - | </code> | ||
| - | |||
| - | Also note that to get plots to show up inline, you may have to add the magic incantation **in the first cell**: | ||
| - | |||
| - | <code python> | ||
| - | |||
| - | %matplotlib inline | ||
| - | |||
| - | import matplotlib | ||
| - | import numpy as np | ||
| - | import matplotlib.pyplot as plt | ||
| - | |||
| - | </code> | ||
| - | |||
| - | Here is a full setup for cairo in linux. | ||
| - | <code> | ||
| - | sudo apt-get install libcairo2-dev | ||
| - | git clone https://github.com/pygobject/pycairo.git | ||
| - | cd pycairo/ | ||
| - | python setup.py build | ||
| - | python setup.py install | ||
| - | </code> | ||
| - | |||
| - | Here is a setup for cairo in mac. | ||
| - | <code> | ||
| - | brew install cairo --use-clang | ||
| - | brew install py2cairo | ||
| - | </code> | ||
cs501r_f2016/lab1.txt ยท Last modified: 2021/06/30 23:42 (external edit)
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