I am training a DNN with CNN in Keras. Though, I can write an EarlyStopping criteria based on val_loss but due to minor oscillations in the val_loss, I want to monitor the average validation loss over last n epoches and with n patience. How can I do this in Keras?

I just noticed that in mostly github repositry of research papers they didnt implemented early stopping criteria and they didnt use validation set but whats the reason behind this?

I am trying to implement early stopping to my model where I am performing Machine Translation using Seq2Seq with attention. I am mostly used to writing my own models in steps, something like this: for activation in activations: for layer1 in layers1: for optimizer in optimizers: # define model model_vanilla_lstm = Sequential() model_vanilla_lstm.add(LSTM(layer1, activation=activation, input_shape=(n_step, n_features))) model_vanilla_lstm.add(Dense(1)) #compile model model_vanilla_lstm.compile(optimizer=optimizer, loss='mse') #Early Stopping earlyStop=EarlyStopping(monitor="val_loss",mode='min',patience=5) # fit model history = model_vanilla_lstm.fit(X, y, epochs=epoch, validation_data=(X_test,dataset_test['Close']) , verbose=1, callbacks=[earlyStop]) #Summary of the model …

I often use "early stopping" when I train neural nets, e.g. in Keras: from keras.callbacks import EarlyStopping # Define early stopping as callback early_stopping = EarlyStopping(monitor='loss', patience=5, mode='auto', restore_best_weights=True) # ...THE MODEL HERE... # Call early stopping in .fit history = model.fit_generator(..., callbacks=[early_stopping]) Question: I often wonder if it is better to monitor the loss (monitor='loss') or the validation loss (monitor='val_loss'). Are there some takeaways from the literature? What is best practice? My intuition would be that monitoring the validation …

I'm performing a classification of imbalanced multiclass data using a Neural Network in the TensorFlow framework. Therefore, I'm applying class weights. I would like to apply early stopping to reduce overfitting. My concern is that the cost of the validation set used for early stopping will be calculated differently from the cost of the training set due to the class weights, so the early stopping will not work correctly. That's because the cost of the validation set could be biased …

Using Keras, I setup EarlyStoping like this: EarlyStopping(monitor='val_loss', min_delta=0, patience=100, verbose=0, mode='min', restore_best_weights=True) When I train it behaves almost as advertised. However, I am initializing my model weights before training using weights I know are a good baseline. The problem is when I train, although EarlyStopping kicks in, it ignores my initial model and often picks the best model since training started (excluding initial model). The model that it picks is often worse than my initial one. Is there a …

Let's say I'm participating in a Kaggle image recognition competition. Firstly, I create a train/validation split and find the good hyperparameters for my model. Here the stopping criterion is when the validation loss stops decreasing and starts increasing, something like EarlyStopping Keras callback. When I have found decent hyperparameters, I want to train my model on the full dataset because I want the best performance for the competition. But what is the stopping criterion for the training now? I don't …

The point of EarlyStopping is to stop training at a point where validation loss (or some other metric) does not improve. If I have set EarlyStopping(patience=10, restore_best_weights=False), Keras will return the model trained for 10 extra epochs after val_loss reached a minimum. Why would I ever want this? Has this model not just trained for 10 unnecessary epochs? Wouldn't it make more sense to give me back the model that was trained at the lowest validation loss i.e. with restore_best_weights=True? …

Can we use both cross validation/nested cross validation technique and early stopping with patient at the same time? Using early stopping for each (training, validation) fold and get best result of each (training, validation) fold and finally get average result as usual? I have read an article about this situation Machine Learning mastery But it is not convincing enough for me.

While training an NGBoost model I got: [iter 0] loss=-2.2911 val_loss=-2.3309 scale=2.0000 norm=1.0976 [iter 100] loss=-3.3288 val_loss=-2.8532 scale=2.0000 norm=0.7841 [iter 200] loss=-4.0889 val_loss=-1.5779 scale=2.0000 norm=0.7544 [iter 300] loss=-4.8400 val_loss=8.8107 scale=2.0000 norm=0.6710 [iter 400] loss=-5.4463 val_loss=51.7171 scale=2.0000 norm=0.5999 It looks like overfit occurred between iterations 100 and 200. Is the best (val_loss wise) model saved, or did I get the last one reported (with a massive overfit, -5.4463 in train loss vs 51.7171 in validation loss)? If I really do get …

Keras gives users the option, while fitting a model, to split the data into train/test samples using the parameter "validation_split. Example: model = Sequential() model.add(Dense(3, activation = 'relu')) /// Compile model /// model.fit(X_train, y_train, validation_split = 0.2) However, my intuition suggests that using validation_split (as opposed to creating train, test samples before fitting the model) will cause overfitting, since although validation_split splits the batches into train and test at each epoch, the overall effect is that the entire dataset is …

When training my CNN model, based on the random initialization of weights, i get the prediction results. In other words, with the same training and test data i get different results every time when i run the code. When tracking the loss, i can know if the result would be acceptable or not. Based on this, i want to know if there is a way to stop the training if the loss begins by a value superior to a desired …

I made my neural network, it is pre-trained for 180 days of data. It filters the fraud data of credit cards everyday and 1-days new data is comming in. And I also want after the filtering, I want to re-train my AI-model but I just want to use new 1-day data only(because training neural network is really time-consuming work). My AI model is 0(not fraud)/1(fraud) classification model. I want to change my neural net by 1/181.... because the amount of …

I am not sure what is the proper way to use early stopping with cross-validation for a gradient boosting algorithm. For a simple train/valid split, we can use the valid dataset as the evaluation dataset for the early stopping and when refitting we use the best number of iterations. But in case of cross-validation like k-fold, my intuition would be to use each valid set of each fold as evaluation dataset for the early stopping but that means the best …