titanlearn v 2.0.1.000

This commit is contained in:
art 2019-10-29 14:21:53 -05:00
parent dacf12f8a4
commit ab9b38da95
2 changed files with 65 additions and 4 deletions

View File

@ -7,10 +7,13 @@
# this module learns from its mistakes far faster than 2022's captains # this module learns from its mistakes far faster than 2022's captains
# setup: # setup:
__version__ = "2.0.0.001" __version__ = "2.0.1.000"
#changelog should be viewed using print(analysis.__changelog__) #changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
2.0.1.000:
- added net, dataset, dataloader, and stdtrain template definitions
- added graphloss function
2.0.0.001: 2.0.0.001:
- added clear functions - added clear functions
2.0.0.000: 2.0.0.000:
@ -33,6 +36,8 @@ __all__ = [
import torch import torch
from os import system, name from os import system, name
import matplotlib.pyplot as plt
import numpy as np
def clear(): def clear():
if name == 'nt': if name == 'nt':
@ -40,7 +45,34 @@ def clear():
else: else:
_ = system('clear') _ = system('clear')
def train(device, net, epochs, trainloader, optimizer, criterion): 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 epoch in range(epochs): # loop over the dataset multiple times
@ -56,6 +88,35 @@ def train(device, net, epochs, trainloader, optimizer, criterion):
loss.backward() loss.backward()
optimizer.step() 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 return net, running_loss_list
print("finished training") 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)
def graphloss(losses):
x = range(0, len(losses))
plt.plot(x, losses)
plt.show()