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code/Reconnaissance Image/Fingers5.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import datasets, transforms
+import matplotlib.pyplot as plt
+
+trainSet = datasets.ImageFolder(r'C:\Users\kesha\Desktop\TelecomParis\PACT\DownloadedDataset\train',
+                                transform = transforms.ToTensor())
+valSet = datasets.ImageFolder(r'C:\Users\kesha\Desktop\TelecomParis\PACT\DownloadedDataset\val',
+                              transform = transforms.ToTensor())
+
+trainloader = torch.utils.data.DataLoader(trainSet,
+                                          batch_size = 50,
+                                          shuffle = True)
+
+valloader = torch.utils.data.DataLoader(valSet,
+                                        batch_size = 50,
+                                        shuffle = True)
+
+class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        #nn.Conv2d(channels_in, out_channels/number of filters, kernel size)
+        self.conv1 = nn.Conv2d(3, 16, 3)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(16, 32, 3)
+        self.conv3 = nn.Conv2d(32, 64, 3)
+        self.fc1 = nn.Linear(64*14*14, 16)
+        self.fc2 = nn.Linear(16, 6)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        #size = 16*126*126 then 16*63*63
+        x = self.pool(F.relu(self.conv2(x)))
+        #size = 32*61*61 then 32*30*30
+        x = self.pool(F.relu(self.conv3(x)))
+        #size = 64*28*28 then 64*14*14
+        x = torch.flatten(x, 1)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+net = Net()
+print(net)
+
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.RMSprop(net.parameters(), lr=0.001)
+
+device = torch.device('cuda')
+for epoch in range(1, 7):
+    print('Starting epoch ' + str(epoch))
+    current_loss = 0
+    Epoch = []
+    Loss = []
+    for i, data in enumerate(trainloader, 0):
+        inputs, labels = data
+
+        #très important
+        optimizer.zero_grad()
+
+        output = net(inputs)
+        loss = criterion(output, labels)
+        loss.backward()
+        optimizer.step()
+
+        current_loss += loss.item()
+    print('epoch: ', epoch, " loss: ", current_loss)
+    Loss.append(current_loss)
+    Epoch.append(epoch)
+    
+plt.plot(Epoch, Loss)
+plt.title('Valeur de la fonction cost en fonction de l\'epoch')
+plt.show()
+#to save a model: torch.save(net.state_dict(), file_location)