Comments (3)
For the batch hard loss, you need to decrease the learning rate.
With the default learning rate of 1e-3
, the embeddings collapse to a single point so the loss is equal to the margin (0.5
).
from tensorflow-triplet-loss.
I'm not sure what you are trying to do and what code you are using so some context would be helpful.
from tensorflow-triplet-loss.
The batch hard triplet loss in this repo works for batches with multiple images for each class.
For instance you would need to have a batch size of 100, which 10 images from each class.
In your case your triplets are already formed so you need to change the triplet selection process, not the triplet loss.
from tensorflow-triplet-loss.
Related Issues (20)
- What does 'embedding_mean_norm' mean? HOT 2
- loss=0 in step=101(after two step) HOT 1
- Saving weights of model and calculation of embeddings HOT 1
- Using batch_all_triplet_loss function HOT 2
- Embeddings Collapse very fast
- fraction_positive increasing HOT 4
- the function of _get_triplet_mask in triplet_loss.py
- base_model
- Multi domain triplet loss
- Why average loss value by `batch_size` when using `batch_hard` method?
- PyTorch Implmentation of triplet loss
- the difference between tf.contrib.losses.metric_learning.triplet_semihard_loss and batch_all_triplet_loss
- Implementation of metrics to monitor training process in tf.keras environment
- Advice on which loss to optimize HOT 2
- Performance issues in the program
- Performance issue in /model/tests (by P3) HOT 1
- Got Nan value when label's id is greater than 159 HOT 1
- hard triplet convergence!
- Some corrections in MNIST_dataset since TF 2.0
- OSS License compatibility question
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from tensorflow-triplet-loss.