tra-analysis/analysis-master/tra_analysis/titanlearn.py
Dev Singh 5fd5e32cb1
Implement CD with building on tags to PyPI (#34)
* Create python-publish.yml

* populated publish-analysis.yml
moved legacy versions of analysis to seperate subfolder

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* attempt to fix issue with publish action

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* another attempt o fix publish-analysis.yml

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* this should work now

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* pypa can't take just one package so i'm trying all

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* this should totally work now

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* trying removing custom dir

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* rename analysis to tra_analysis, bump version to 2.0.0

* remove old packages which are already on github releases

* remove pycache

* removed ipynb_checkpoints

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* build

* do the dir thing

* trying removing custom dir

Signed-off-by: Arthur Lu <learthurgo@gmail.com>
Signed-off-by: Dev Singh <dev@devksingh.com>

* rename analysis to tra_analysis, bump version to 2.0.0

Signed-off-by: Dev Singh <dev@devksingh.com>

* remove old packages which are already on github releases

Signed-off-by: Dev Singh <dev@devksingh.com>

* remove pycache

Signed-off-by: Dev Singh <dev@devksingh.com>

* build

Signed-off-by: Dev Singh <dev@devksingh.com>

* removed ipynb_checkpoints

Signed-off-by: Arthur Lu <learthurgo@gmail.com>
Signed-off-by: Dev Singh <dev@devksingh.com>

* do the dir thing

Signed-off-by: Dev Singh <dev@devksingh.com>

* Revert "do the dir thing"

This reverts commit 2eb7ffca8d.

* correct dir

* set correct yaml positions

Signed-off-by: Dev Singh <dev@devksingh.com>

* attempt to set correct dir

Signed-off-by: Dev Singh <dev@devksingh.com>

* run on tags only

Signed-off-by: Dev Singh <dev@devksingh.com>

* remove all caches from vcs

Signed-off-by: Dev Singh <dev@devksingh.com>

* bump version for testing

Signed-off-by: Dev Singh <dev@devksingh.com>

* remove broke build

Signed-off-by: Dev Singh <dev@devksingh.com>

* dont upload dists to github

Signed-off-by: Dev Singh <dev@devksingh.com>

* bump to 2.0.2 for testing

Signed-off-by: Dev Singh <dev@devksingh.com>

* fix yaml

Signed-off-by: Dev Singh <dev@devksingh.com>

* update docs

Signed-off-by: Dev Singh <dev@devksingh.com>

* add to readme

Signed-off-by: Dev Singh <dev@devksingh.com>

* run only on master

Signed-off-by: Dev Singh <dev@devksingh.com>

Co-authored-by: Arthur Lu <learthurgo@gmail.com>
Co-authored-by: Dev Singh <dsingh@CentaurusRidge.localdomain>
2020-08-10 14:29:51 -05:00

122 lines
3.0 KiB
Python

# 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__ = "2.0.1.001"
#changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
2.0.1.001:
- removed matplotlib import
- removed graphloss()
2.0.1.000:
- added net, dataset, dataloader, and stdtrain template definitions
- added graphloss function
2.0.0.001:
- added clear functions
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__ = (
"Arthur Lu <arthurlu@ttic.edu>,"
"Jacob Levine <jlevine@ttic.edu>,"
)
__all__ = [
'clear',
'net',
'dataset',
'dataloader',
'train',
'stdtrainer',
]
import torch
from os import system, name
import numpy as np
def clear():
if name == 'nt':
_ = system('cls')
else:
_ = system('clear')
class net(torch.nn.Module): #template for standard neural net
def __init__(self):
super(Net, self).__init__()
def forward(self, input):
pass
class dataset(torch.utils.data.Dataset): #template for standard dataset
def __init__(self):
super(torch.utils.data.Dataset).__init__()
def __getitem__(self, index):
pass
def __len__(self):
pass
def dataloader(dataset, batch_size, num_workers, shuffle = True):
return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
def train(device, net, epochs, trainloader, optimizer, criterion): #expects standard dataloader, whch returns (inputs, labels)
dataset_len = trainloader.dataset.__len__()
iter_count = 0
running_loss = 0
running_loss_list = []
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()
# monitoring steps below
iter_count += 1
running_loss += loss.item()
running_loss_list.append(running_loss)
clear()
print("training on: " + device)
print("iteration: " + str(i) + "/" + str(int(dataset_len / trainloader.batch_size)) + " | " + "epoch: " + str(epoch) + "/" + str(epochs))
print("current batch loss: " + str(loss.item))
print("running loss: " + str(running_loss / iter_count))
return net, running_loss_list
print("finished training")
def stdtrainer(net, criterion, optimizer, dataloader, epochs, batch_size):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = net.to(device)
criterion = criterion.to(device)
optimizer = optimizer.to(device)
trainloader = dataloader
return train(device, net, epochs, trainloader, optimizer, criterion)