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visualization v 1.0.0.000, titanlearn v 2.0.1.001
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@ -7,10 +7,13 @@
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# this module learns from its mistakes far faster than 2022's captains
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# this module learns from its mistakes far faster than 2022's captains
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# setup:
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# setup:
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__version__ = "2.0.1.000"
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__version__ = "2.0.1.001"
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#changelog should be viewed using print(analysis.__changelog__)
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#changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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__changelog__ = """changelog:
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2.0.1.001:
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- removed matplotlib import
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- removed graphloss()
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2.0.1.000:
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2.0.1.000:
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- added net, dataset, dataloader, and stdtrain template definitions
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- added net, dataset, dataloader, and stdtrain template definitions
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- added graphloss function
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- added graphloss function
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@ -36,7 +39,6 @@ __all__ = [
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import torch
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import torch
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from os import system, name
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from os import system, name
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import matplotlib.pyplot as plt
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import numpy as np
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import numpy as np
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def clear():
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def clear():
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@ -114,9 +116,3 @@ def stdtrainer(net, criterion, optimizer, dataloader, epochs, batch_size):
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trainloader = dataloader
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trainloader = dataloader
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return train(device, net, epochs, trainloader, optimizer, criterion)
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return train(device, net, epochs, trainloader, optimizer, criterion)
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def graphloss(losses):
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x = range(0, len(losses))
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plt.plot(x, losses)
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plt.show()
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34
data analysis/analysis/visualization.py
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34
data analysis/analysis/visualization.py
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# Titan Robotics Team 2022: Visualization Module
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# Written by Arthur Lu & Jacob Levine
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# Notes:
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# this should be imported as a python module using 'import visualization'
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# this should be included in the local directory or environment variable
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# fancy
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# setup:
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__version__ = "1.0.0.000"
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#changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.0.0.000:
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- created visualization.py
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- added graphloss()
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- added imports
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"""
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__author__ = (
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"Arthur Lu <arthurlu@ttic.edu>,"
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"Jacob Levine <jlevine@ttic.edu>,"
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)
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__all__ = [
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'graphloss',
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]
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import matplotlib.pyplot as plt
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def graphloss(losses):
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x = range(0, len(losses))
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plt.plot(x, losses)
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plt.show()
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