![]() ![]() We then defined the different components of our network in our initializer function and connected the network together by chaining functions in our forward() function. In Part 3 of this series we built a convultional neural network to classify MNIST digits by defining a new class, that extended nn.Module, called Net. Please also see the other parts ( Part 1, Part 2, Part 3). Out = out.view(out.This is Part 3.5 of the tutorial series. ![]() Self.fc2 = torch.nn.Linear( 625, 10, bias= True) ![]() Torch.nn.MaxPool2d(kernel_size= 2, stride= 2, padding= 1)) # CNN Model class CNN (torch.nn.Module): def _init_ (self): Y_test = mnist_test.test_labels.to(device)Ĭorrect_prediction = torch.argmax(prediction, 1) = Y_testĪccuracy = correct_prediction.float().mean() X_test = mnist_test.test_data.view(len(mnist_test), 1, 28, 28).float().to(device) Test # Test model and check accuracy with torch.no_grad(): #테스트 용이니 학습을 안함 명시 Print( '[Epoch: '.format(epoch + 1, avg_cost))Ħ. # image is already size of (28x28), no reshape # label is not one-hot encoded 학습 (Gradient Descent) optimizer = (model.parameters(), lr=learning_rate) Loss(=Cost) 정의 하기 criterion = torch.nn.CrossEntropyLoss().to(device) # Softmax is internally computed. ![]() Out = out.view(out.size( 0), -1) # Flatten them for FCĤ. Torch.nn.init.xavier_uniform_(self.fc.weight) Self.fc = torch.nn.Linear( 7 * 7 * 64, 10, bias= True) Torch.nn.MaxPool2d(kernel_size= 2, stride= 2)) Pool = torch.nn.MaxPool2d(kernel_size= 2, stride= 2))Ĭonv2 = torch.nn.Conv2d( 32, 64, kernel_size= 3, stride= 1, padding= 1)įc = fc = torch.nn.Linear( 7 * 7 * 64, 10, bias= True) #Final FC 7x7圆4 inputs -> 10 outputs # CNN Model (2 conv layers) class CNN (torch.nn.Module): def _init_ (self): Neural Architecture Search for Object Detection in Point CloudĢ016-Neural Architecture Search with Reinforcement LearningĢ018-Learning Transferable Architectures for Scalable Image Recognition Rethinking the Inception Architecture for Computer VisionĪn Analysis of Deep Neural Network Models for Practical ApplicationsĢ017-Lifelong Learning with Dynamically Expandable Networks (30%)Ģ017-PathNet: Evolution Channels Gradient Descent in Super Neural NetworksĢ017-Measuring Catastrophic Forgetting in Neural Networks (80%)Ģ017-Overcoming catastrophic forgetting in neural networksĢ017-Continual Learning with Deep Generative ReplayĢ018-Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networks (30%)Ģ018-A study on sequential iterative learning for overcoming catastrophic forgetting phenomenon of artificial neural network (100%)Ģ019-Continual Lifelong Learning with Neural Networks: A Review (10%) A practical theory for designing very deep convolutional neural networksĭEEP CONVOLUTIONAL NEURALNETWORK DESIGN PATTERNS ![]()
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