Other answers explain well how accuracy and loss are not necessarily exactly (inversely) correlated, as loss measures a dif validation loss increasing Overfitting and Underfitting - Data Science Portfolio Machine Learning Model Performance A notable reason for this occurrence is that the model may be too complex for the data or that, the model … In this lesson, we’re going to learn how to interpret these learning curves and how we can use them to guide model development. You should be able to run again with --load_checkpoint_dir and the export flags, and it’ll pick up the checkpoint saved during training. I have been training a deepspeech model for quite a few epochs now and my validation loss seems to have reached a point where it now has plateaued. You can improve the model … Try pretrained model and learn just last layer (so dont optimize rest of them, just pass model.fc.parameters() to optimizer). We can also see that changes to the learning rate are dependent on the batch size, after which an update is performed. Interesting Machine Learning / Deep Learning Scenarios Keras LSTM - Validation Loss Increasing From Epoch #1. I'm training using Librispeech train-clean-100.tar.gz and validating on dev-clean.tar.gz . Choose a Learning Rate Scheduler for Neural Networks I'm training using Librispeech train-clean-100.tar.gz and validating on dev-clean.tar.gz . Drop out is probably the best answer to DNN regularization and works with all types of network sizes and architectures. Validation loss increases and validation accuracy decreases
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