62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
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import torch
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from torch.utils.data import DataLoader
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from torchvision import datasets
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from torchvision.transforms import Compose, Lambda, ToTensor
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import matplotlib.pyplot as plt
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import dataset_glasses as dg
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# torch.manual_seed(42)
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device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
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trainset = dg.GlassesDataset('classes\\Jimmy_Neuron\\train', 'classes\\Jimmy_Neuron\\sdg.csv',
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transform=Compose([Lambda(lambda x: x.float())]))
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testset = dg.GlassesDataset('classes\\Jimmy_Neuron\\test', 'classes\\Jimmy_Neuron\\set.csv',
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transform=Compose([Lambda(lambda x: x.float())]))
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def train(model, dataset, n_iter=100, batch_size=512):
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optimizer = torch.optim.SGD(model.parameters(), lr=0.0001) #musiałem zmienić learning rate bo inaczej się wektory własne zerowały
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criterion = torch.nn.CrossEntropyLoss() #Przydatne przy binanrym kryterium!
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dl = DataLoader(dataset, batch_size=batch_size)
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model.train()
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for epoch in range(n_iter):
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for images, targets in dl:
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optimizer.zero_grad()
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out = model(images.to(device))
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loss = criterion(out, targets.to(device))
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loss.backward()
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optimizer.step()
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if epoch % 10 == 0:
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print('epoch: %3d loss: %.4f' % (epoch, loss))
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# for name, param in model.named_parameters():
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# print(name, param.grad)
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def accuracy(model, dataset):
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model.eval()
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correct = sum([(model(images.to(device)).argmax(dim=1) == targets.to(device)).sum()
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for images, targets in DataLoader(dataset, batch_size=256)])
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return correct.float() / len(dataset)
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hidden_size = 135*64
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#żeby to skończyć kiedykolwiek i jak uda mi się to CUDA zrobić to zmieniam rozdziałke z 1024 na 128
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#Użycie konwolucyjnej warstwy dobiło mi z 85 do 90
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model = torch.nn.Sequential(
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torch.nn.Conv2d(3, 6, 5),
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torch.nn.MaxPool2d(2, 2),
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torch.nn.Conv2d(6, 16, 5),
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torch.nn.Flatten(),
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torch.nn.Linear(53824, hidden_size),
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torch.nn.ReLU(),
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torch.nn.Linear(hidden_size, 32*32),
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torch.nn.ReLU(),
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torch.nn.Linear(32*32, 10),
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torch.nn.LogSoftmax(dim=-1)
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).to(device)
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train(model, trainset)
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print(accuracy(model, testset))
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# torch.save(model.state_dict(), 'model.pt')
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