Comments (1)
You could plot the training loss with matplotlib to see whether it's converging or not. We didn't add it by default because we don't want to have too many dependencies.
But in general, smaller value of loss means better model.
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Related Issues (20)
- Embedding Extraction Procedure HOT 1
- about model HOT 1
- [Bug] Predict method does not finish HOT 3
- what is train data format? HOT 1
- Question about custom data generator
- uis-rnn gives different result on broken audios and continuous audios HOT 5
- how to control the number of different speaker when predicting? HOT 1
- Unable to convert pytorch model to tensorflow in Diarization on mobile device. HOT 2
- [Question] Are input d-vectors for training assumed L2-normalized? HOT 8
- Change input size HOT 1
- No module named coverage HOT 1
- Is is possible to pre-load the model for multiple request? HOT 1
- [Question] About num_non_zero HOT 1
- [Question] The dimension of toy test data [test_sequence] is (25, 95, 256) what does the first 2 dimension represent? Toy train data [train_sequence] has dimension (4627, 256) which is understandable. HOT 1
- Is there a way to fine tune an already existing pre-trained model? HOT 1
- rnn initial state trainable HOT 1
- Any documentations on training from scratch using custom data in other languages ? HOT 1
- [Bug] Making a prediction on CPU after training on GPU
- Predicted labels doesn't match with Ground truth labels but the accuracy of test results is 0.8% HOT 1
- assign gpu with arguments
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