source/NN/neural_network.py

@@ -6,6 +6,7 @@ from torchvision.transforms import Compose, Lambda, ToTensor
 import matplotlib.pyplot as plt
 import numpy as np
 from model import *
+from PIL import Image
 
 device = torch.device('cuda')
 
@@ -32,10 +33,9 @@ test_set = datasets.ImageFolder(root='resources/test', transform=data_transforme
 #print(train_set.targets[3002])
 
 
-#loading your own image: <-- zrobię to na koniec - wrzucanie konkretnego obrazka aby uzyskac wynik
 #function for training model
 def train(model, dataset, iter=100, batch_size=64):
-    optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
     criterion = nn.NLLLoss()
     train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
     model.train()
@@ -53,6 +53,7 @@ def train(model, dataset, iter=100, batch_size=64):
 #function for getting accuracy
 def accuracy(model, dataset):
     model.eval()
+    with torch.no_grad():
         correct = sum([
             (model(inputs.to(device)).argmax(dim=1) == labels.to(device)).sum()
             for inputs, labels in DataLoader(dataset, batch_size=64, shuffle=True)
@@ -63,7 +64,32 @@ def accuracy(model, dataset):
 
 
 
+
 model = Neural_Network_Model()
 model.to(device)
-train(model, train_set)
+
-print(accuracy(model, test_set))
+model.load_state_dict(torch.load('model.pth'))
+model.eval()
+
+#training the model:
+# train(model, train_set)
+# print(f"Accuracy of the network is: {100*accuracy(model, test_set)}%")
+# torch.save(model.state_dict(), 'model.pth')
+
+
+#TEST - loading the image and getting results:
+testImage_path = 'resources/images/plant_photos/pexels-polina-tankilevitch-4110456.jpg'
+testImage = Image.open(testImage_path)
+testImage = data_transformer(testImage)
+testImage = testImage.unsqueeze(0)
+testImage = testImage.to(device)
+
+model.load_state_dict(torch.load('model.pth'))
+model.to(device)
+model.eval()
+
+testOutput = model(testImage)
+_, predicted = torch.max(testOutput, 1)
+predicted_class = train_set.classes[predicted.item()]
+print(f'The predicted class is: {predicted_class}')
+