If you want to prevent overfitting you can reduce the complexity of your network. I want to use one hot to represent group and resource, there are 2 group and 4 resouces in training data: group1 (1, 0) can access resource 1 (1, 0, 0, 0) and resource2 (0, 1, 0, 0) group2 (0 . Drawbacks. 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. Keras stateful LSTM returns NaN for validation loss loss_ = PlotLossesKeras () model.fit (X1, y1, batch_size= 128, epochs=500, validation_split = 0.2, steps_per_epoch = 500, shuffle=True, callbacks= [loss_]) The loss plot looks like this: Large non-decreasing LSTM training loss - PyTorch Forums Learning Rate and Decay Rate: Reduce the learning rate, a good starting value is usually between 0.0005 to 0.001. How to interpret the neural network model when validation accuracy ... Data Science: I'm having some trouble interpreting what's going on in the training and validation loss, sensitivity, and specificity for my model. But the validation loss started increasing while the validation accuracy is not improved. The test legend refers to the validation set. 在训练LSTM和LSTM的稳定性时，keras中的验证丢失 - Validation loss in keras while training ... We designed tensors with both the non-overlapping and overlapping time . LSTM training loss does not decrease - nlp - PyTorch Forums In my case when I attempt LSTM time series classification often val_acc starts with a high value and stays the same, even though loss, val_loss and acc change. . Plot by author. This was done by monitoring the validation loss at each epoch and stopping the training if the validation loss did not decrease for several epochs. Validation loss not decreasing. Upd. I am now doubting whether my model is wrongly built. LSTM Network in R - R-bloggers Kindly someone help me with this.
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