cs501r_f2018:lab1
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cs501r_f2018:lab1 [2018/09/03 21:05] wingated created |
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| ====Objective:==== | ====Objective:==== | ||
| - | Get started with anaconda, python, ipython notebooks, and pandas. Begin producing simple visualizations of data and images. | + | Get started with colab and python. Begin producing simple visualizations of data and images. |
| ---- | ---- | ||
| ====Deliverable:==== | ====Deliverable:==== | ||
| - | For this lab, you will submit an ipython notebook. This notebook will have two parts: | + | For this lab, you will submit an ipython notebook via learningsuite. This notebook will have two parts: |
| **Part 1:** Your notebook should generate a random image. We will run this | **Part 1:** Your notebook should generate a random image. We will run this | ||
| Line 19: | Line 19: | ||
| [[http://playground.tensorflow.org/|Tensorflow playground]] | [[http://playground.tensorflow.org/|Tensorflow playground]] | ||
| + | |||
| + | [[http://colab.research.google.com/|Google colab]] | ||
| + | |||
| + | [[https://colab.research.google.com/drive/1TzaPS3jvRadN-URLbQ9nD1ZNoZktfNRy|A colab notebook teaching you how to use colab notebooks]] | ||
| + | |||
| + | [[https://sites.google.com/site/artml2018/tutorials|Various colab tutorials]] | ||
| ---- | ---- | ||
| Line 33: | Line 39: | ||
| ====Description:==== | ====Description:==== | ||
| - | Throughout this class, we will be using a combination of ipython | + | Throughout this class, we will be using Google's colab environment to develop and test our deep neural networks. This consists of ipython |
| - | notebooks, Tensorflow and the anaconda python distribution. For this lab, you | + | notebooks and a standardized python distribution. For this lab, you |
| - | must install anaconda, and write a simple python program (using | + | must create a google account (or use one you already have), start up colab, and write a simple python program in it. |
| - | ipython notebooks). | + | |
| As described above, the notebook should do two things: | As described above, the notebook should do two things: | ||
| Line 47: | Line 52: | ||
| type, such as random lines, and can be created in a loop). We won't | 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 | count the number of elements; this is just to encourage you to create | ||
| - | random images with moderate complexity. | + | random images with moderate complexity (ie, you can't just generate randomly colored pixels, as in the example below). |
| 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! | 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. | + | Once you have a working classifier, take a screenshot. Then use your colab notebook to display that image in-line. |
| ---- | ---- | ||
| - | ====Installing anaconda:==== | + | ====Starter code:==== |
| - | + | ||
| - | 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: | Here's some starter code to help you generate an image. The ''nbimage'' function will display the image inline in the notebook: | ||
| <code python> | <code python> | ||
| - | import cairo | + | import IPython.display |
| - | import numpy | + | import PIL.Image |
| + | import numpy as np | ||
| # A simple function to display an image in an ipython notebook | # A simple function to display an image in an ipython notebook | ||
| def nbimage( data ): | def nbimage( data ): | ||
| - | from IPython.display import display, Image | + | IPython.display.display(PIL.Image.fromarray(data)) |
| - | from PIL.Image import fromarray | + | |
| - | from StringIO import StringIO | + | |
| - | s = StringIO() | ||
| - | fromarray( data ).save( s, 'png' ) | ||
| - | display( Image( s.getvalue() ) ) | ||
| - | WIDTH = 512 | + | # create an image consisting of random colors |
| - | HEIGHT = 288 | + | |
| - | # this is a numpy buffer to hold the image data | + | data = np.random.rand(512,512,3) # a 512x512 image, with 3 color channels (R,G,B) |
| - | data = numpy.zeros( (HEIGHT,WIDTH,4), dtype=numpy.uint8 ) | + | |
| + | # by default, rand creates floating point numbers between [0,1]. We need to convert that to 8-bit bytes between [0,255] | ||
| + | data = (255*data).astype('uint8') | ||
| + | |||
| + | # display it! | ||
| + | nbimage( data ) | ||
| - | # 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> | </code> | ||
| Line 126: | Line 102: | ||
| plt.tight_layout | 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> | </code> | ||
cs501r_f2018/lab1.1536008710.txt.gz · Last modified: 2021/06/30 23:40 (external edit)
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