Transfer Weights Between Models
Problem Statement
Transfer matching weights from a pretrained model to a new model with a different final layer.
Background
In transfer learning, you load weights for layers that match in shape and skip incompatible ones.
Your Task
The starter code creates a pretrained model (3 output classes) and a new model (5 output classes). Transfer only the parameters that match in both name and shape between the two models. fc1 should transfer (same shape), fc2 should not (different output size).
Output Format
Returns a dictionary with "fc1_weights_match", "fc1_bias_match", "fc2_weights_match", and "transferred_keys".
Example:
None
{'fc1_weights_match': True, 'fc1_bias_match': True, 'fc2_weights_match': False, 'transferred_keys': ['fc1.weight', 'fc1.bias']}- The function
transfer_weights_test()initializes two models: a pretrained model with a final layer of 3 classes and a new model with a final layer of 5 classes. - It transfers the weights of
fc1from the pretrained model to the new model, which includes both the weights and bias offc1, resulting in transferred keys['fc1.weight', 'fc1.bias']. - Since the shapes of
fc2differ between the two models, its weights are not transferred, sofc2_weights_matchisFalse. - The function then verifies that the weights and bias of
fc1match between the two models, resulting infc1_weights_matchandfc1_bias_matchboth beingTrue.
Constraints:
- Only transfer matching layers
- fc2 has different shapes so cannot be transferred
- Verify with torch.equal
Background Knowledge
Introduction to Model Serialization
Model serialization is the process of saving and loading neural network models. This is crucial in deep learning as it allows us to save trained models and load them later for inference or further training. In PyTorch, models can be serialized using the torch.save() and torch.load() functions. However, when the architecture of the model changes, we need to transfer the weights from the pretrained model to the new model.
Understanding Model Architecture
In this problem, we have two models: a pretrained model and a new model. Both models have a similar architecture, with the exception of the final layer. The pretrained model has a final layer with 3 outputs, while the new model has a final layer with 5 outputs. This means that the weights of the final layer cannot be transferred directly. However, the weights of the earlier layers can be transferred, which is the goal of this problem.
Weight Transfer
Weight transfer is a technique used in transfer learning, where the weights of a pretrained model are used to initialize the weights of a new model. This is useful when the new model has a similar architecture to the pretrained model, but with some changes. In this problem, we need to transfer the weights of the fc1 layer from the pretrained model to the new model. This requires careful handling of the model's state dictionary, which contains the weights and biases of the model.
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