Linked Questions

I am training a binary classification algorithm in Keras, the loss is cross-entropy ...

TLDR: My 1D CNN is doing a really bad job classifying graphs. Here's more context: Note: I've tried adopting the advice listed here and here, but my CNN hasn't stopped overfitting. I've already tried ...

My ANN model produces classic overfitting characteristics, producing high R2 values (90-99%) but low accuracy scores (10-40%). I'm currently inputting 28655 data entries, using 8 input features to ...

I try the code at tensorflow in R tutorial (https://tensorflow.rstudio.com/tfestimators/) but I cannot understand the output what the code produces. Code: ...

I created a neural network and I have trouble getting it to train. I followed all advice in this post: What should I do when my neural network doesn't learn? but have not had success yet. Then I ...

I am attempting to create a neural network that can learn to evaluate chess positions. I'm following along with this paper and trying to recreate its results. The general idea is to have the NN ...

Problem discription I have a forth order polynomial function clearly defined. I would sample $n$ number of points and feed them to my function to get the corresponding $y$ values. I then apply a FC ...

I currently have a quite ok model fitting my data with Regression Tree and a Random Forest. However, while trying to move on to a Neural Network (to evaluate other models), I have been struggling to ...

I am training Autoencoder (1 hl) on keras. I have used validation split of 20%. Training seems ok, but for the validation set the MSE error(shown in blue) seems to saturate with whatever no. of hidden ...

While fine-tuning a deep neural network I ran into the following situation: My train- and validation loss are both decreasing and have very similar values throughout training. Especially the train-...

Most learning (training and validation) curves that I see in literature and through online resources depict a steep decrease/increase in loss/accuracy during initial epochs followed by a subsequent ...

My model looks like this ...

I'm trying to create a multi-class classification model using RNNs. The input data has a sequence length of 90 and consists of 5 features, normalized to the [0,1] range. Here's the network ...