That is over-fitting. By today's standards, LeNet is a very shallow neural network, consisting of the following layers: (CONV => RELU => POOL) * 2 => FC => RELU => FC => SOFTMAX. Say you have some complex surface with countless peaks and valleys. It hovers around a value of 0.69xx and accuracy not improving beyond 65%. In the given base model, there are 2 hidden Layers, one with 128 and one with 64 neurons. These steps are known as strides and can be defined when creating the CNN. 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. Handling overfitting in deep learning models | by Bert Carremans ... This is the classic " loss decreases while accuracy increases " behavior that we expect. One reason why your training and validation set behaves so different could be that they are indeed partitioned differently and the base distributions of the two are different. Difference between Loss, Accuracy, Validation loss, Validation accuracy ... It helps to think about it from a geometric perspective. predict the total trading volume of the stock market). It returns a history of the training, useful for debugging & visualization. Validation loss value depends on the scale of the data. Let's add normalization to all the layers to see the results. Why is my validation loss lower than my training loss? As we can see from the validation loss and validation accuracy, the yellow curve does not fluctuate much. The curve of loss are shown in the following figure: It also seems that the validation loss will keep going up if I train the model for more epochs. High, constant training loss with CNN - Data Science Stack Exchange
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