Losses of keras CNN model is not decreasing - Data Science Stack Exchange Automatic Detection and Segmentation of Thrombi in Abdominal Aortic ... Splitting Machine Learning Data: Train, Validation, Test Set Split The validation loss stays lower much longer than the baseline model. 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. I tried different setups from LR, optimizer, number of . I have queries regarding why loss of network is not decreasing, I have doubt whether I am using correct loss function or not. Merge two datasets into one. Due to the way backpropagation works and a simple application of the chain rule, once a gradient is 0, it ceases to contribute to the model. The training loss will always tend to improve as training continues up until the model's capacity to learn has been saturated. Let's add normalization to all the layers to see the results. The test size has 250000 inputs and the validation set has 20000. sadeghmir commented on Jul 27, 2016. but the val_loss start to increase when the train_loss is relatively low. The green curve and red curve fluctuate suddenly to higher validation loss and lower validation accuracy, then goes to the lower validation loss and the higher validation accuracy, especially for the green curve. PyTorch: Training your first Convolutional Neural Network (CNN) Training loss is decreasing while validation loss is NaN Architecture of fine-tuned CNN model. I could notice that the training and validation accuracy started to converge towards . Hi, I recently had the same experience of training a CNN while my validation accuracy doesn't change. In other words, your model would overfit to the . These steps are known as strides and can be defined when creating the CNN. 1- the percentage of train, validation and test data is not set properly. This leads to a less classic " loss increases while accuracy stays the same ". Here is a snippet of training and validation, I'm using a combined CNN+RNN network, model 1,2,3 are encoder, RNN, decoder respectively. I don't understand that. To train a model, we need a good way to reduce the model's loss. We can add weight regularization to the hidden layer to reduce the overfitting of the model to the training dataset and improve the performance on the holdout set. As always, the code in this example will use the tf.keras API, which you can learn more about in the TensorFlow Keras guide.. 200 epochs are scheduled but learning will stops if there is no improvement on validation set for 10 epochs.