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'best_model.pth' neural network learning model + fixes

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Woj 2023-06-04 14:41:33 +02:00
parent 23d5dbd614
commit f7cc21a386
5 changed files with 30 additions and 15 deletions
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NeuralNetwork/best_model.pth Normal file
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10
NeuralNetwork/learning_results.txt Normal file
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@ -0,0 +1,10 @@
Epoch: 1 Train Loss: 65 Train Accuracy: 0.5754245754245755
Epoch: 2 Train Loss: 25 Train Accuracy: 0.7457542457542458
Epoch: 3 Train Loss: 8 Train Accuracy: 0.8431568431568431
Epoch: 4 Train Loss: 2 Train Accuracy: 0.9010989010989011
Epoch: 5 Train Loss: 1 Train Accuracy: 0.9335664335664335
Epoch: 6 Train Loss: 0 Train Accuracy: 0.9545454545454546
Epoch: 7 Train Loss: 0 Train Accuracy: 0.972027972027972
Epoch: 8 Train Loss: 0 Train Accuracy: 0.9820179820179821
Epoch: 9 Train Loss: 0 Train Accuracy: 0.994005994005994
Epoch: 10 Train Loss: 0 Train Accuracy: 0.9945054945054945

9
NeuralNetwork/neural_network_learning.py
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@ -22,7 +22,8 @@ optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)
criterion = nn.CrossEntropyLoss() criterion = nn.CrossEntropyLoss()
num_epochs = 10 num_epochs = 10
train_size = len(glob.glob(images_path, '*.jpg')) # train_size = len(glob.glob(images_path+'*.jpg'))
train_size = 2002
go_to_accuracy = 0.0 go_to_accuracy = 0.0
for epoch in range(num_epochs): for epoch in range(num_epochs):
@ -50,10 +51,10 @@ for epoch in range(num_epochs):
train_accuracy = train_accuracy/train_size train_accuracy = train_accuracy/train_size
train_loss = train_loss/train_size train_loss = train_loss/train_size
model.eval()
print('Epoch: '+ str(epoch+1) +' Train Loss: '+ str(int(train_loss)) +' Train Accuracy: '+ str(train_accuracy)) print('Epoch: '+ str(epoch+1) +' Train Loss: '+ str(int(train_loss)) +' Train Accuracy: '+ str(train_accuracy))
if train_accuracy > go_to_accuracy: if train_accuracy > go_to_accuracy:
go_to_accuracy= train_accuracy go_to_accuracy= train_accuracy
torch.save(model.state_dict(), "best_model.pth") torch.save(model.state_dict(), "best_model.pth")

23
NeuralNetwork/src/data_model.py
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@ -9,35 +9,35 @@ class DataModel(nn.Module):
# convolution # convolution
self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1) self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
#shape (256, 12, 244x244) #shape (256, 12, 224x224)
# batch normalization # batch normalization
self.bn1 = nn.BatchNorm2d(num_features=12) self.bn1 = nn.BatchNorm2d(num_features=12)
#shape (256, 12, 244x244) #shape (256, 12, 224x224)
self.reul1 = nn.ReLU() self.reul1 = nn.ReLU()
self.pool=nn.MaxPool2d(kernel_size=2, stride=2) self.pool=nn.MaxPool2d(kernel_size=2, stride=2)
# reduce image size by factor 2 # reduce image size by factor 2
# pooling window moves by 2 pixels at a time instead of 1 # pooling window moves by 2 pixels at a time instead of 1
# shape (256, 12, 122x122) # shape (256, 12, 112x112)
self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(num_features=24) self.bn2 = nn.BatchNorm2d(num_features=24)
self.reul2 = nn.ReLU() self.reul2 = nn.ReLU()
# shape (256, 24, 122x122) # shape (256, 24, 112x112)
self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=1) self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=1)
#shape (256, 48, 122x122) #shape (256, 48, 112x112)
self.bn3 = nn.BatchNorm2d(num_features=48) self.bn3 = nn.BatchNorm2d(num_features=48)
#shape (256, 48, 122x122) #shape (256, 48, 112x112)
self.reul3 = nn.ReLU() self.reul3 = nn.ReLU()
# connected layer # connected layer
self.fc = nn.Linear(in_features=48*122*122, out_features=num_objects) self.fc = nn.Linear(in_features=48*112*112, out_features=num_objects)
def forward(self, input): def forward(self, input):
output = self.conv1(input) output = self.conv1(input)
output = self.bn1(output) output = self.bn1(output)
output = self.reul1(output) output = self.reul1(output)
@ -51,8 +51,11 @@ class DataModel(nn.Module):
output = self.bn3(output) output = self.bn3(output)
output = self.reul3(output) output = self.reul3(output)
# output shape matrix (256, 48, 122x122) # output shape matrix (256, 48, 112x112)
output = output.view(-1, 48*122*122) #print(output.shape)
#print(self.fc.weight.shape)
output = output.view(-1, 48*112*112)
output = self.fc(output) output = self.fc(output)
return output return output

3
NeuralNetwork/src/torchvision_resize_dataset.py
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@ -1,3 +1,4 @@
import glob
import pathlib import pathlib
import torchvision.transforms as transforms import torchvision.transforms as transforms
from torchvision.datasets import ImageFolder from torchvision.datasets import ImageFolder
@ -27,4 +28,4 @@ combined_dataset = ImageFolder(images_path, transform=transform)
path=pathlib.Path(images_path) path=pathlib.Path(images_path)
classes = sorted([i.name.split("/")[-1] for i in path.iterdir()]) classes = sorted([i.name.split("/")[-1] for i in path.iterdir()])
# print(classes) # print(classes)