Questions tagged [loss]
The loss tag has no usage guidance.
37
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Train neural network to predict multiple distributions
I aim to train a neural network to predict 2 distributions (10 quantiles, i.e. deciles) at 5 time points. So my y is of shape:
...
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Siamese Network in TensorFlow employing Triplet Loss
I am constructing a siamese network using tensorflow which uses triplet loss.
My inputs are of shape (100,100,1) and I have made a CNN embed_model to so that the output is a tensor with 50 points.
Now ...
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flat validation loss (underfitting or goodfitting)
if we have a loss like this plot, is it kind of underfitting or goodfitting?
Error results:
Training Process:
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Interpret loss of WGAN
I have been working with WGAN-GP to produce text.
However, I have some issues interpreting the loss functions.
First of all, the loss seems awfully small. After the first 300 batches (which translate ...
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Combining 2 losses for 2 different tasks and training the networks in Keras
I have to implement a communication model consisting of a mapper , channel , detector and demapper blocks. The mapper , detector and demapper blocks are neural networks and channel is a AWGN channel.
<...
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How Does the Reward Model in ChatGPT Calculate Losses?
Reading the InstructGPT paper(which seems to be what ChatGPT was built off of), I found this equation for the reward function.
However, I'm struggling to understand how this equation is used to ...
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Accuracy is decreased, and loss is not changing for logistic regression of stacking model meta learner
Problem: I would like to improve accuracy of stock price prediction image classification model using candlestick charts.
Base model: VGG16 and EfficientNet.
Base model input: Two models independently ...
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116
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Early stopping on validation loss or macro-F1?
I am working on an extremely imbalanced dataset to build a classification model. The number of classes is 53 classes. I use early stopping on the validation loss to prevent the model from overfitting.
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Calculationg perplexity (in natural language processing) manually
I am trying to understand Perplexity within Natural Language Processing as a metric more fully. And I am doing so by creating manual examples to understand all the component parts. Is the following ...
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What does that mean if the loss looks like this?
I have a problem. I have trained a model. And as you can see, there is a zigzag in the loss. In addition, the validation loss is increasing.
What does this mean if you only look at the training curve? ...
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How to interpret training and validation loss of DeepAR?
Please bear with me. Its a long but complete post.
My questions are:
Why does the training loss start to osccilate wildly after some epochs? It is because it has jumped out of a local minima? I tried ...
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Is there a way I can double the punishment when model mis-classing to a specific class?
As the title I asked.
For example: a model that predicts the probability of a stock price rising/falling. Let's say this is a triple-classification problem.
If it predicts "RISING", while ...
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54
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Val loss initially decreases, then increases
I've created an LSTM model to predict 1 output value from 8 features. My loss constantly decreases and my val loss also decreases from the start, however it begins to increase after so many epochs. ...
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Why is there a 0.5 in this loss?
I'm reading this paper and I don't understand why the squared L2-norm is also multiplied by 0.5 in the loss.
They want a loss that measures the distance between two feature maps. Why don't they use ...
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56
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training loss decreases but training accuracy is also decreasing with epochs
I am working on the classification problem where by I am having a hinge loss function + other loss terms to optimize for which the input is the output from tanh layer at the end. But I can't reveal ...
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The proper loss function for regression that prediction values do not lie on one side of the real values
I'm doing a prediction task using machine learning. First I'm doing a regression task, then I use the values to predict its class.
I used MSE as loss function. However, my prediction values are ...
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599
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Keras loss object and shapes
I'm at a loss. I've been staring at this problem for a while and I'm unsure how to proceed. I've been constructing a script to train a model for object detection based on a dataset I've compiled. I've ...
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Should the model be defined again before training it to new data?
I wanted to fit the LSTM model on new data set in a loop so I have implemented it like this
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Custom loss function for regression
I am trying to write a custom loss function for a machine learning regression task. What I want to accomplish is following:
Reward higher preds, higher targets
Punish higher preds, lower targets
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510
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Logarithmic scale for a learning curve [closed]
I'm plotting the learning curve with Python with the following code:
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58
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Decreasing Learning Rate doesn't improve the results
In theory and what people are doing (e.g. Paper) decreasing the learning rate should help the optimizer to go "deeper into the valley" and thus decrease the loss and increase the metric.
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The case of (1,478) dim and parameters of neural network to find out
colleagues, actually I am kind'a new to NN, but hard trying..
I have data:
Index: 40073 entries (excluded from training, UID)
Columns: 484 entries
dtypes: bool(468), float64(2), int64(13), object(1)
I ...
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70
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why the accuracy result and the loss result of an ANN model is inconsistent?
I trained a model based on an ANN and the accuracy is 94.65% almost every time while the loss result is 12.06%. Now my question is shouldn't the loss of the model be (100-94 = 6%) or near it? Why it ...
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HuggingFace Transformers is giving loss: nan - accuracy: 0.0000e+00
I am a HuggingFace Newbie and I am fine-tuning a BERT model (distilbert-base-cased) using the Transformers library but the training loss is not going down, instead ...
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Is my CNN model overfitting or underfitting?
I would like to be sure of whether the model is overfitting or undercutting. Being new to this, is there any specific point to identify when to stop the training process. Any help in this regard would ...
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86
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Q values loss per episode and mean absolute error
I am new to deep reinforcement learning! I am following this code for my adaptation problem (doing actions)
https://github.com/jaromiru/AI-blog/blob/master/CartPole-DQN.py
I am wondering how I can ...
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344
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Training loss = 0, training accuracy =1, validation and test around 85%
I have created different CNNs for doing image classification.
The dataset is this: https://www.kaggle.com/crowww/a-large-scale-fish-dataset
There are 9 classes, and each class contains 1000 images of ...
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256
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How to calculate MAE and threshold in a multivariate time series
I'm trying to understand how to calculate the MAE in my time series and then the thresholds to understand which of my data in the test set are anomalies. I'm following this tutorial, which is based on ...
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41
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How to interpret high loss value from model.evaluate() on test data
I'm collecting some metrics for my model's performance using:
...
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57
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How to increase accuracy and decrease loss of my model
https://jovian.ai/casella0798/badmodel
I created the model above to predict red wine quality. I have 6 classes, from 3 to 8. Dataset is unbalanced, with a lot of classes 5 and 6.
My model performs ...
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189
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Interpreting Categorical Crossentropy Loss
I would like to ask for clarification about the loss values outputted during training using Categorical Crossentropy as the loss function. If I have 11 categories, and my loss is (for the sake of the ...
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Why are the Loss and Accuracy metrics not suitable?
I have read in a book where it was described that Loss and Accuracy are only conditionally suitable for a statement strength. Other metrics are used like Precision, Recall, ... .
Does anyone know a ...
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134
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Accuracy graph of binary classification by CNN [closed]
Why in binary classification of images with CNN the loss and accuracy graph are so unstable? I mean accuracy of validation test does not increase smoothly, it goes to 80%, then comes to 60%, then ...
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Should Discriminator Loss increase or decrease?
This question is purely based on the theoretical aspect of GANs.
So, when training a GAN how should the discriminator loss look like?
Should the loss of discriminator increase (as the generator is ...
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Why do people prefer $(target-actual)^2$ over $|(target-actual)|$ [duplicate]
When computing loss functions, people use $(target-actual)^2$. They sqaure it to prevent any negative loss. But we can even use $|(target-actual)|$ to prevent any negative loss. So, why do people ...
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3
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Regression problem with Deep Learning
I'm working on the Housing Price dataset, where the target is to predict the housing price.
The price of the house will always be positive and according to me, it's possible that the model can predict ...
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Is it vital to use weighted metrics with weighted loss functions?
When one has an imbalanced machine learning problem, let's say a binary classification problem where class 0 is a majority class and class 1 is a minority class. Here class 1 is the most important ...
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