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Questions tagged [loss-function]

If Y_pred is very far off from Y, the Loss value will be very high. However, if both values are almost similar, the Loss value will be very low. Hence we need to keep a loss function which can penalize a model effectively while it is training on a dataset. When a neural network is trying to predict a discrete value, we can consider it to be a classification model. This could be a network trying to predict what kind of animal is present in an image, or whether an email is a spam or not.

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Need help implementing a custom loss function in lightGBM (Zero-inflated Log Normal Loss)

Im trying to implement this zero-inflated log normal loss function based on this paper in lightGBM (https://arxiv.org/pdf/1912.07753.pdf) (page 5). But, admittedly, I just don’t know how. I don’t understand how to get the gradient and hessian of…
Negative Correlation
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Loss with custom backward function in PyTorch - exploding loss in simple MSE example

Before working on something more complex, where I knew I would have to implement my own backward pass, I wanted to try something nice and simple. So, I tried to do linear regression with mean squared error loss using PyTorch. This went wrong (see…
Björn
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Is there a version of sparse categorical cross entropy in pytorch?

I saw a sudoku solver CNN uses a sparse categorical cross-entropy as a loss function using the TensorFlow framework, I am wondering if there is a similar function for Pytorch? if not could how could I potentially calculate the loss of a 2d array…
Shivam Bhatt
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Tensorflow Keras RMSE metric returns different results than my own built RMSE loss function

This is a regression problem My custom RMSE loss: def root_mean_squared_error_loss(y_true, y_pred): return tf.keras.backend.sqrt(tf.keras.losses.MSE(y_true, y_pred)) Training code sample, where create_model returns a dense fully connected…
ma7555
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RMSE loss for multi output regression problem in PyTorch

I'm training a CNN architecture to solve a regression problem using PyTorch where my output is a tensor of 20 values. I planned to use RMSE as my loss function for the model and tried to use PyTorch's nn.MSELoss() and took the square root for it…
cronin
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How to access sample weights in a Keras custom loss function supplied by a generator?

I have a generator function that infinitely cycles over some directories of images and outputs 3-tuples of batches the form [img1, img2], label, weight where img1 and img2 are batch_size x M x N x 3 tensors, and label and weight are each batch_size…
ely
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Higher loss penalty for true non-zero predictions

I am building a deep regression network (CNN) to predict a (1000,1) target vector from images (7,11). The target usually consists of about 90 % zeros and only 10 % non-zero values. The distribution of (non-) zero values in the targets vary from…
Lukas Hecker
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Keras custom loss function (elastic net)

I'm try to code Elastic-Net. It's look likes: And I want to use this loss function into Keras: def nn_weather_model(): ip_weather = Input(shape = (30, 38, 5)) x_weather = BatchNormalization(name='weather1')(ip_weather) x_weather =…
陳建勤
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Why there is sudden drop in loss after every epoch?

I am using custom loss function(triplet loss) with mini-batch, during epoch the loss is gradually decreasing but just after the every epoch there is sudden drop in loss(appx. 10% of fall) and then gradually decreasing during that epoch(ignore…
Rahul Anand
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Keras apply different weight to different misclassification

I am trying to implement a classification problem with three classes: 'A','B' and 'C', where I would like to incorporate penalty for different type of misclassification in my model loss function (kind of like weighted cross entropy). Class weight is…
bambi
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Use TensorFlow loss Global Objectives (recall_at_precision_loss) with Keras (not metrics)

Background I have a multi-label classification problem with 5 labels (e.g. [1 0 1 1 0]). Therefore, I want my model to improve at metrics such as fixed recall, precision-recall AUC or ROC AUC. It doesn't make sense to use a loss function (e.g.…
NumesSanguis
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Hausdorff distance loss in tensorflow

What is the most efficient way to implement a loss function that minimizes the pairwise Hausdorff distance between two batches of tensors in Tensorflow?.
HuckleberryFinn
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Implementing a batch dependent loss in Keras

I have an autoencoder set up in Keras. I want to be able to weight the features of the input vector according to a predetermined 'precision' vector. This continuous valued vector has the same length as the input, and each element lies in the range…
duncster94
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How to conditionally assign values to tensor [masking for loss function]?

I want to create a L2 loss function that ignores values (=> pixels) where the label has the value 0. The tensor batch[1] contains the labels while output is a tensor for the net output, both have a shape of (None,300,300,1). labels_mask =…
ScientiaEtVeritas
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Tensorflow - Total Variation Loss - reduce_sum vs reduce_mean?

Why does the Total Variation Loss in Tensorflow suggest to use reduce_sum instead of reduce_mean as a loss function? This can be used as a loss-function during optimization so as to suppress noise in images. If you have a batch of images, then…
Cypher
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