Meta Learning
MetaLearner
Source code in engines/contentFilterEngine/learning_paradigms/meta_learning.py
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__init__(model, data_loader, criterion, optimizer, num_epochs)
Initializes the MetaLearner with the given model, data loader, criterion, optimizer, and number of epochs.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model
|
Module
|
The model to be trained and used for predictions. |
required |
data_loader
|
DataLoader
|
DataLoader providing the training data. |
required |
criterion
|
Module
|
Loss function used to evaluate the model's performance. |
required |
optimizer
|
Optimizer
|
Optimization algorithm used to update model weights. |
required |
num_epochs
|
int
|
Number of epochs to train the model. |
required |
Source code in engines/contentFilterEngine/learning_paradigms/meta_learning.py
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predict(graph, node_index, top_k=5, threshold=0.5)
Predicts the top-k items for a given node in a graph using the trained model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
graph
|
Tensor
|
The graph data structure containing nodes and edges. |
required |
node_index
|
int
|
The index of the node for which predictions are to be made. |
required |
top_k
|
int
|
The number of top items to return. Defaults to 5. |
5
|
threshold
|
float
|
The threshold for prediction confidence. Defaults to 0.5. |
0.5
|
Returns:
Type | Description |
---|---|
List[int]: A list of indices representing the top-k predicted items. |
Source code in engines/contentFilterEngine/learning_paradigms/meta_learning.py
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train()
Trains the model using the meta-learning approach.
This method iterates over the data provided by the data_loader for a specified number of epochs, updating the model's weights using the optimizer and evaluating its performance using the criterion.
Source code in engines/contentFilterEngine/learning_paradigms/meta_learning.py
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