Linked Questions

0 votes
1 answer

Keras - text classification, overfitting, and how to improve my model? [duplicate]

i am developing a text classification neural network based on this two articles -
Эдуард Мартыненко's user avatar
1 vote
1 answer

SSD MobileNet v1 loss not converging bounding boxes all over the place [duplicate]

I've trained SSD MobileNet v2 model using Tensorflow API on my own dataset of ~4k dog pictures and it displays bounding boxes all over the place. I've trained with batch size 1. The same dataset ...
Lukas Jakutis's user avatar
1 vote
1 answer

My Neural Net can overfit but not generalize [duplicate]

I have created a Neural network that gets its training data from a complicated physics simulation. I run the simulation by randomizing 7 different inputs. Each input can be 1 of 4 discrete values. I ...
jlapin's user avatar
  • 21
0 votes
0 answers

Keras: validation loss decreases but accuracy does not increase [duplicate]

I am working on the development of a deep learning model for prediction of a disease from medical images. It is a binary classification algorithm. I am currently using a model built from scratch with ...
DeepLearningStef's user avatar
6 votes
1 answer

How to prevent overfitting? [duplicate]

I'm aware of the concept of overfitting in Machine Learning. The main advice for dealing with it, usually is regularization. Is there other practical advice to avoid overfitting?
TmBrdy's user avatar
  • 161
0 votes
0 answers

How can I reduce the noise of prediction graph? [duplicate]

I am trying to use LSTM to predict a time series data as you can see in the following image, the predicted graphs is very noisy: The original data is looking like this: That I normalized it like ...
user3486308's user avatar
0 votes
0 answers

How does ReLU deal with negative inputs? [duplicate]

I'm replicating this paper for my PhD, which says that they are using deep learning to predict stock returns. So the inputs are (mostly) continuous variables that can be negative and positive. Outputs ...
stevew's user avatar
  • 831
3 votes
1 answer

Neural Network - Estimating Non-linear function [duplicate]

I am fairly new to neural networks. I am trying to empirically show that a neural network can work better than logistic regression when the underlying function is non-linear. In my simulation study, ...
Alex's user avatar
  • 497
0 votes
0 answers

My CNN is not learning but just memorizing [duplicate]

So there has been similar posts but none of them solves my problem, so I decided to created a new question. I'm working on a regression project where I intend to use CNN to predict material properties,...
xshang's user avatar
  • 1
1 vote
0 answers

Why is my keras resnet50 model overfitting? [duplicate]

I have applied Keras ResNet-50 on a small x-ray image dataset. I tried making layers both trainable and non-trainable, but my model validation accuracy doesn't improve above 50%. I don't understand ...
Mobassir Hossen's user avatar
0 votes
0 answers

Approximate output of unknown function using neural networks [duplicate]

I have a block that outputs a single value when fed with a time series containing a 1000 points. I also have a dataset of records of about 20000 input time series (each containing a 1000 points) and ...
Likely's user avatar
  • 31
0 votes
0 answers

RNN Model accuracy with Training and test data [duplicate]

I am using RNN package in R to do regression. First created the model with training dataset and checked the model output using the same training dataset. It was fairly accurate with the predicted ...
Mahesh Subramaniam's user avatar
0 votes
1 answer

How to prevent the keras convolutional neural network model to over-fit? [duplicate]

I want to build a convolutional neural network and train it to recognise whether the digit is 0 or 1. Example of my training data is a 800 * 600 gray scale image containing a digit one: I have 22 ...
mercury0114's user avatar
0 votes
0 answers

Why am I obtaining values close to zero when using a NN for regression? [duplicate]

Here is the dataset. I tried converting this implementation into its analog with Keras. Why are my predictions SO bad? They are almost always close to a single number. Doesn't matter if I use more ...
Caterina's user avatar
  • 167
4 votes
0 answers

What are the most common techniqes for preventing overfitting? [duplicate]

How is this issue dealt with, in practice?
Johnson's user avatar
  • 331

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