NeuralNetwork/NeuralNetwork.py

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+import torch.nn as nn
+import torch.nn.functional as F
+
+class NeuralNetwork(nn.Module):
+    def __init__(self):
+        super(NeuralNetwork, self).__init__()
+        
+        #  Warstwy konwolucyjnej sieci neuronowej, filtr 5×5, 3 kanały dla RGB
+        self.convolutional_nn_1 = nn.Conv2d(3, 6, 5)
+        self.convolutional_nn_2 = nn.Conv2d(6, 16, 5)
+
+        # Wyciaganie "najwazniejszej" informacji z obrazu
+        self.pool = nn.MaxPool2d(2, 2)
+
+        self.full_connection_layer_1 = nn.Linear(16 * 71 * 71, 120)
+        self.full_connection_layer_2 = nn.Linear(120, 84)
+        self.full_connection_layer_3 = nn.Linear(84, 4)
+
+    # Forward określa przepływ inputu przez warstwy
+    def forward(self, x):
+        x = self.pool(F.relu(self.convolutional_nn_1(x)))
+        x = self.pool(F.relu(self.convolutional_nn_2(x)))
+
+        # 16 kanałów o rozmiarach 71x71
+        x = x.view(x.size(0), 16 * 71 * 71)
+        x = F.relu(self.full_connection_layer_1(x))
+        x = F.relu(self.full_connection_layer_2(x))
+        x = self.full_connection_layer_3(x)
+        return x