50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
import torch
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import torch.nn as nn
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import torchvision.transforms as transforms
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import torch.optim as optim
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from torch.utils.data import DataLoader
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from torchvision.datasets import ImageFolder
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from NeuralNetwork import NeuralNetwork
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# import matplotlib.pyplot as plt
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# import numpy as np
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# import cv2
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def trainNeuralNetwork():
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neural_net = NeuralNetwork()
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train_set = ImageFolder(root='./resources/trash_dataset/train', transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]))
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trainloader = DataLoader(
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train_set, batch_size=2, shuffle=True, num_workers=2)
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# potrzebne do wyświetlania loss w każdej iteracji
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.SGD(neural_net.parameters(), lr=0.001, momentum=0.9)
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epoch_num = 4 # najlepiej 10, dla lepszej wiarygodności
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for epoch in range(epoch_num):
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measure_loss = 0.0
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for i, data in enumerate(trainloader, 0):
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inputs, labels = data
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# czyszczenie gradientu f-cji
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optimizer.zero_grad()
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outputs = neural_net(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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measure_loss += loss.item()
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if i:
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print('[%d, %5d] loss: %.3f' %
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(epoch + 1, i + 1, measure_loss))
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measure_loss = 0.0
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print('Finished.')
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PATH = './trained_nn.pth'
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torch.save(neural_net.state_dict(), PATH)
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def main():
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trainNeuralNetwork()
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if __name__ == '__main__':
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main()
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