For questions related to the accuracy metric/measure, which is the number of correct predictions divided by the total number of predictions.
Questions tagged [accuracy]
52 questions
10
votes
3 answers
Should I choose a model with the smallest loss or highest accuracy?
I have two Machine Learning models (I use LSTM) that have a different result on the validation set (~100 samples data):
Model A: Accuracy: ~91%, Loss: ~0.01
Model B: Accuracy: ~83%, Loss: ~0.003
The size and the speed of both models are almost the…
malioboro
- 2,729
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8
votes
2 answers
Effect of batch size and number of GPUs on model accuracy
I have a data set that was split using a fixed random seed and I am going to use 80% of the data for training and the rest for validation.
Here are my GPU and batch size configurations
use 64 batch size with one GTX 1080Ti
use 128 batch size with…
bit_scientist
- 241
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7
votes
4 answers
Why is my test error lower than the training error?
I am trying to train a CNN regression model using the ADAM optimizer, dropout and weight decay.
My test accuracy is better than training accuracy. But, as far as I know, usually, the training accuracy is better than test accuracy.
So I wonder how…
이희준
- 73
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3
votes
1 answer
Accuracy dropped when I ran the program the second time
I was following a tutorial about Feed-Forward Networks and wrote this code for a simple FFN :
class FirstFFNetwork:
#intialize the parameters
def __init__(self):
self.w1 = np.random.randn()
self.w2 = np.random.randn()
self.w3 =…
Eeshaan Jain
- 31
- 2
3
votes
1 answer
Why is there more than one way of calculating the accuracy?
Some sources consider the true negatives (TN) when computing the accuracy, while some don't.
Source 1:
https://medium.com/greyatom/performance-metrics-for-classification-problems-in-machine-learning-part-i-b085d432082b
Source…
Stephen Philip
- 317
- 2
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3
votes
1 answer
What is the relationship between the training accuracy and validation accuracy?
During model training, I noticed various behaviour in between training and validation accuracy. I understand that 'The training set is used to train the model, while the validation set is only used to evaluate the model's performance...', but I'd…
nilsinelabore
- 241
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3
votes
2 answers
How to express accuracy of a regression ANN that uses MSE loss function?
I have a regression MLP network with all input values between 0 and 1, and am using MSE for the loss function. The minimum MSE over the validation sample set comes to 0.019. So how to express the 'accuracy' of this network in 'lay' terms? If RMSE is…
samiant
- 59
- 1
2
votes
1 answer
Should I continue training if the neural network attains 100% training accuracy?
I have a neural network where there are two hidden layers. Each hidden layer has 128 neurons. The input layer has 20 inputs, and the output layer has 3 outputs.
I have 1 million records of data. 80% is used to train the network, 20% is used for…
user366312
- 351
- 1
- 12
2
votes
0 answers
Why do I get higher average dice accuracy for less data
I am working on image segmentation of MRI thigh images with deep learning (Unet). I noticed that I get a higher average dice accuracy over my predicted masks if I have less samples in the test data set.
I am calculating it in tensorflow as
def…
Lis Louise
- 139
- 3
2
votes
1 answer
Why am I getting a difference between training accuracy and accuracy calculated with Keras' predict_classes on a subset of the training data?
I'm trying to solve a binary classification problem with AlexNet. I split the original dataset into training and validation datasets using a 70/30 ratio. I have trained my neural network with a dataset of 11200 images, and I obtained a training…
Saha
- 131
- 1
2
votes
2 answers
Should I choose the model with highest validation accuracy or the model with highest mean of training and validation accuracy?
I'm training a deep network in Keras on some images for a binary classification (I have around 12K images). Once in a while, I collect some false positives and add them to my training sets and re-train for higher accuracy.
I split my training into…
Tina J
- 973
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2
votes
1 answer
What does top N accuracy mean?
Places205-VGG, a CNN trained model for 205 scene categories of Places Database with 2.5 million images Places205 dataset has top1 accuracy = 58.9% and top5 accuracy = 87.7%.
What does top1 and top5 (and, in general, top $N$) accuracy mean in the…
Farid Alijani
- 299
- 3
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1
vote
0 answers
Training loss is decreasing very slowly while learning MNIST database
I am developing my ANN from scratch which is supposed to classify MNIST database of handwritten digits (0-9). My feed-forward fully connected ANN has to be composed of:
One input layer, with 28x28 = 784 nodes (that is, features of each image)
One…
Anna
- 11
- 1
1
vote
1 answer
Test accuracy decreases during my train process
I want to train a neural network model with the arcface loss function and try to combine it with domain adaption. But when the training process continues, I find the test accuracy first increases and then decreases, the model cannot reach…
klayoe
- 11
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1
vote
1 answer
Is it a good practice to pad signal before feature extraction?
Is padding, before feature extraction with VGGish, a good practice?
Our padding technique is to find the longest signal (which is loaded .wav signal), and then, in every shorter signal, put zeros to the size of the longest one. We need to use it…
Dawid_K
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