how to decrease validation loss in cnn

cat. Dropout from anywhere between 0.5-0.8 after each CNN+dense+pooling layer Heavy data augmentation in "on the fly" in Keras Realising that perhaps I have too many free parameters: decreasing the network to only contain 2 CNN blocks + dense + output. Applying regularization. There is no fixed number of epochs . If I don't use loss_validation = torch.sqrt (F.mse_loss (model (factors_val), product_val)) the code works fine. What does that signify? As part of the optimization algorithm, the error for the current state of the model must be estimated repeatedly. By today's standards, LeNet is a very shallow neural network, consisting of the following layers: (CONV => RELU => POOL) * 2 => FC => RELU => FC => SOFTMAX. After the final iteration it displays a validation accuracy of above 80% but then suddenly it dropped to 73% without an iteration. but the validation accuracy remains 17% and the validation loss becomes 4.5%. Training loss is decreasing while validation loss is NaN Ways to decrease validation loss. Therefore, if you're model is stuck then it's likely that a significant number of your neurons are now dead. Discover how to train a model using an iterative approach. How to build CNN in TensorFlow: examples, code and notebooks Image Classification with Cat and Dog - Chan`s Jupyter Lower the learning rate (0.1 converges too fast and already after the first epoch, there is no change anymore). What does that signify? But the question is after 80 epochs, both training and validation loss stop changing, not decrease and increase. The number of epoch decides the number of times the weights in the neural network will get updated. Getting the validation loss while training - PyTorch Forums Shuffle the dataset. neural networks - How do I interpret my validation and training loss ... acc and val_acc don't change? · Issue #1597 - GitHub As sinjax said, early stopping can be used here. How to use the ModelCheckpoint callback with Keras and TensorFlow

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