This question already has answers here:
What function defines accuracy in Keras when the loss is mean squared error (MSE)?
(3 answers)
Closed 3 years ago.
How is the accuracy calculated when the problem is a regression one?
I'm working on a regression problem to predict how much electricity each user USES each day,I use keras build a LSTM model to do this time series prediction. At the beginning, I use the 'accuracy' as the metrics, and when run
model.fit(...,verbose=2,...)
val_acc has a value after every epoch. And in my result, the value doesn't change, it's always the same value.
Then I realized that the regression problem was that there was no concept of accuracy, and then I started to wonder, how is that accuracy calculated?
I have a guess that when metrics is 'accuracy' in the regression question, accuracy is also calculated in a similar way to the classification problem: the number of predicted values equal to true values divided by the total sample size.
Am I right?
In regression you use metrics that measure how far your prediction is from the actual value, such as Squared Error, Mean Squared Error, etc.
Please see How to determine the accuracy of regression? Which measure should be used?
When building a keras LSTM model, you usually build a "skeleton" first, then you compile, fit and at the end, predict. During the compile step you need to define your loss function (see Keras documentation on Sequential models) and a metric, so you could do e.g.
model.compile(loss='mean_squared_error', optimizer='sgd', metrics=['mean_squared_error'])
(see Keras documentation on metrics). Therefore, if you put accuracy as a metric in a regression setting, you would not get reasonable results, as this metric is designed only for categorical tasks.
Yes, the accuracy is computed in exactly the same way as in classification, keras does not do any kind of adjustment. As you say it makes no sense to use the accuracy (which is a classification metric) for a regression problem.
Related
So i want to evaluate the best values of :
Learning Rate
Batch Size
Number of epoch
Activation function
Optimizers
Loss function
no of Hidden Layers
noof nodes in the hidden layers
to solve a regression problem
I have done this so far using randomized search. I have to do this individually for each parameter- so set the keras model to a standard model then test one parameter, then repeat.
Can anyone advise why my loss is >50,000 despite using the best network? And mean squared error is >40000000000.0000? This is a network fit using the optimal parameters from the randomized search but it isnt performing well at all. The data has been pre-processed, and scaled. I added in L2 regularisation but it doesnt improve it much.
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Cost function training target versus accuracy desired goal
(2 answers)
Closed 1 year ago.
As I know we optimize our model with changing the weight parameters over the iterations.
The aim is to minimize the loss and maximize the accuracy.
I don't understand why we using loss as parameter as well if we have accuracy as parameter.
Can we use only accuracy and drop loss from our model?
With accuracy we can also change the model weights?
In short, perfecting a neural network is all about minimizing the difference between the intended result and given result. The difference is known as the cost/loss. So the smaller the cost/loss, the closer the intended value, so the higher the accuracy
I suggest you watch 3Blue1Brown's video series on neural networks on youtube
I am trying to train a Deep Neural Network (DNN) with labeled data. The labels are encoded in such a way that it only contains values 0 and 1. The shape of the encoded label is 5 x 5 x 232. About 95% of values in the label is 0and rests are 1. Currently, I am using binary_crossentroy loss function to train the network.
What is the best technique to train the DNN in such a scenario? Is the choice of binary_crossentroy
as the loss function is appropriate in this case? Any suggestion to improve the performance of the model.
You can try MSE loss. If you want to stick to binary cross-entropy (used in binary classification), consider using label smoothing.
You may use 2 other alternative loss functions instead of Binary cross-entropy.They are
Hinge Loss
An alternative to cross-entropy for binary classification problems is the hinge loss function, primarily developed for use with Support Vector Machine (SVM) models.
It is intended for use with binary classification where the target values are in the set {-1, 1}.
Squared Hinge Loss
For more Detail on loss function with examples.click here
Hope helpful, happy learning.
binary_crossentroy as loss is fine
Don't use accuracy as your metrics, because model will just predict every thing as label 0 and will still get 95% accuracy. Instead use F1 score (or precision or recall)
Use Weighted loss: I.e penalize class 1 heavily if they are wrong as compared to class 0.
Instead of class weights you can also use methods like oversampling form the minority class. (Techniques like SMOTE)
How to calculate class weight
You can use sklearn.utils.class_weight to calculate weight from your labels. Check this answer
In such scenarios where you have highly imbalanced data, I would suggest going with Random Forest with up-Sampling. This approach will up-sample the minority class and hence improve the model accuracy.
I have a multi-class classification problem and I want to measure AUC on training and test data.
tf.keras has implemented AUC metric (tf.keras.metrics.AUC), but I'm not be able to see whether this metric could safely be used in multi-class problems. Even, the example "Classification on imbalanced data" on the official Web page is dedicated to a binary classification problem.
I have implemented a CNN model that predicts six classes, having a softmax layer that gives the probabilities of all the classes. I used this metric as follows
self.model.compile(loss='categorical_crossentropy',
optimizer=Adam(hp.get("learning_rate")),
metrics=['accuracy', AUC()]),
and the code was executed without any problem. However, sometimes I see some results that are quite strange for me. For example, the model reported an accuracy of 0.78333336 and AUC equal to 0.97327775, Is this possible? Can a model have a low accuracy and an AUC so high?
I wonder that, although the code does not give any error, the AUC metric is computing wrong.
Somebody may confirm me whether or not this metrics support multi-class classification problems?
There is the argument multi_label which is a boolean inside your tf.keras.metrics.AUC call.
If True (not the default), multi-label data will be treated as such, and so AUC is computed separately for each label and then averaged across labels.
When False (the default), the data will be flattened into a single label before AUC computation. In the latter case, when multi-label data is passed to AUC, each label-prediction pair is treated as an individual data point.
The documentation recommends to set it to False for multi-class data.
e.g.: tf.keras.metrics.AUC(multi_label = True)
See the AUC Documentation for more details.
AUC can have a higher score than accuracy.
Additionally, you can use AUC to decide the cutoff threshold for a binary classifier(this cutoff is by default 0.5). Though there are more technical ways to decide this cutoff, you could simply simply increase it from 0 to 1 to find the value which maximizes your accuracy(this is a naive solution and 1 recommend you to read this https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/One_ROC_Curve_and_Cutoff_Analysis.pdf for an in depth explanation on cutoff analysis )
I would like to know how Keras computes the validation and training accuracies for multi-class classification problems (i.e., the function used). I set my model compile as follows:
model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
But I am trying to understand how is the validation accuracy and training accuracy is computed (i.e., explicit formulae).
I know the validation and training loss are determined by the categorical_crossentropy, but I am not sure about the accuracies.
Note: this is NOT a duplicate of this post. My question is looking for an explanation of the Python function used by Keras to compute accuracy, not the theoretical details given in the mentioned post.
You can find the metrics file and their implementation in the Keras github repo. In this case following metric applies:
def categorical_accuracy(y_true, y_pred):
return K.cast(K.equal(K.argmax(y_true, axis=-1),
K.argmax(y_pred, axis=-1)),
K.floatx())
This calculates the accuracy of a single (y_true, y_pred) pair by checking if the predicted class is the same as the true class. It does this so comparing the index of the highest scoring class in y_pred vector and the index of the actual class in the y_true vector. It returns 0 or 1.
It uses this function to calculate the overall accuracy of the data set, by using the conventional accuracy formula, which is defined as
(amount of correct guesses)/(total amount of guesses)