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: Identifying which specific deep features are most relevant for a particular prediction task, often referred to as Deep Feature Screening (DeepFS) . 3. Implementation Example
: Reusing layers from a deep model to initialize a new task, where the "deep features" serve as the foundation for learning.
import torch import torchvision.models as models # Load a pre-trained model model = models.resnet50(pretrained=True) # Remove the last fully connected layer to get features feature_extractor = torch.nn.Sequential(*(list(model.children())[:-1])) # 'output' will be the deep feature vector for an input image # output = feature_extractor(input_image) Use code with caution. Copied to clipboard
: Identifying which specific deep features are most relevant for a particular prediction task, often referred to as Deep Feature Screening (DeepFS) . 3. Implementation Example
: Reusing layers from a deep model to initialize a new task, where the "deep features" serve as the foundation for learning.
import torch import torchvision.models as models # Load a pre-trained model model = models.resnet50(pretrained=True) # Remove the last fully connected layer to get features feature_extractor = torch.nn.Sequential(*(list(model.children())[:-1])) # 'output' will be the deep feature vector for an input image # output = feature_extractor(input_image) Use code with caution. Copied to clipboard