![]() ![]() 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%) For instance, take a CNN classifier, you could define a nn.Sequential for the CNN part, then define another nn.Sequential for the fully connected classifier section of the model.A practical theory for designing very deep convolutional neural networksĭEEP CONVOLUTIONAL NEURALNETWORK DESIGN PATTERNS In a more complicated module though, you might need to use multiple sequential submodules. The objective of nn.Sequential is to quickly implement sequential modules such that you are not required to write the forward definition, it being implicitly known because the layers are sequentially called on the outputs. Or a simpler way of putting it is: NN = Sequential( The equivalent here is: class NN(nn.Sequential): As I explained earlier, nn.Sequential is a special kind of nn.Module made for this particular widespread type of neural network. ![]() Then, you can simply use a nn.Sequential. the layers are called sequentially on the input, one by one. If the model you are defining is sequential, i.e. Here is an example of a module: class NN(nn.Module): PyTorch will handle backward pass with Autograd. When creating a new neural network, you would usually go about creating a new class and inheriting from nn.Module, and defining two methods: _init_ (the initializer, where you define your layers) and forward (the inference code of your module, where you use your layers). As such nn.Sequential is actually a direct subclass of nn.Module, you can look for yourself on this line. I should start by mentioning that nn.Module is the base class for all neural network modules in PyTorch. If the layers are sequentially used ( self.layer3(self.layer2(self.layer1(x))), you can leverage nn.Sequential to not have to define the forward function of the model. ![]()
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