tra-analysis/data analysis/analysis/titanlearn.py

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# Titan Robotics Team 2022: ML Module
# Written by Arthur Lu & Jacob Levine
# Notes:
# this should be imported as a python module using 'import titanlearn'
# this should be included in the local directory or environment variable
# this module is optimized for multhreaded computing
# this module learns from its mistakes far faster than 2022's captains
# setup:
__version__ = "2.0.0.001"
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#changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
2.0.0.001:
- added clear functions
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2.0.0.000:
- complete rewrite planned
- depreciated 1.0.0.xxx versions
- added simple training loop
1.0.0.xxx:
-added generation of ANNS, basic SGD training
"""
__author__ = (
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"Arthur Lu <arthurlu@ttic.edu>,"
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"Jacob Levine <jlevine@ttic.edu>,"
)
__all__ = [
'clear',
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'train',
]
import torch
import torch.optim as optim
from os import *
def clear():
if os.name == 'nt':
_ = os.system('cls')
else:
_ = os.system('clear')
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def train(device, net, epochs, trainloader, optimizer, criterion):
for epoch in range(epochs): # loop over the dataset multiple times
for i, data in enumerate(trainloader, 0):
inputs = data[0].to(device)
labels = data[1].to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels.to(torch.float))
loss.backward()
optimizer.step()
return net
print("finished training")