source/NN/neural_network.py

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+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from torchvision import datasets, transforms
+from torchvision.transforms import Compose, Lambda, ToTensor
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+#data transform to tensors:
+data_transformer = transforms.Compose
+([
+    transforms.Resize((150, 150)),  
+    transforms.ToTensor(),  
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  
+    ])
+
+
+#loading data:
+train_set = datasets.ImageFolder(root='source/resources/train', transform=data_transformer)
+test_set = datasets.ImageFolder(root='source/resources/test', transform=data_transformer)
+
+
+#to mozna nawet przerzucic do funkcji train:
+train_loader = DataLoader(train_set, batch_size=32, shuffle=True, num_workers=2)
+test_loader = DataLoader(test_set, batch_size=32, shuffle=True, num_workers=2)
+
+#test if classes work properly:
+print(train_set.classes)  
+print(train_set.class_to_idx)  
+print(train_set.targets[3002])
+
+#loading your own image: <-- zrobię to na koniec - wrzucanie konkretnego obrazka aby uzyskac wynik
+