I'm working with time series data, outputting 60 predicted days ahead.I'm currently using mean squared error as my loss function and the results are badI ...
Testing a loss function with weights as Keras tensors def custom_loss_2(y_true, y_pred): return K.mean(K.abs(y_true-y_pred)*K.ones_like(y_true)) This function seems to do the work. So, probably suggests that a Keras tensor as a weight matrix would work. So, I created another version of the loss function. Loss function try 3
01.12.2021 · Use of Keras loss weights During the training process, one can weigh the loss function by observations or samples. The weights can be arbitrary but a typical choice are class weights (distribution of labels).
Given a matrix containing weights for pairs of classes, returns a loss function that computes the categorical cross entropy loss for each sample and scales each loss value by the entry in the weight matrix corresponding to that (true_class, pred_class) pair. For example, if computer work and lying rest are meant to receive
Dec 01, 2021 · Use of Keras loss weights During the training process, one can weigh the loss function by observations or samples. The weights can be arbitrary but a typical choice are class weights (distribution of labels).
class_weight: Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only).
Testing a loss function with weights as Keras tensors def custom_loss_2 (y_true, y_pred): return K.mean (K.abs (y_true-y_pred)*K.ones_like (y_true)) This function seems to do the work. So, probably suggests that a Keras tensor as a weight matrix would work. So, I created another version of the loss function. Loss function try 3
04.09.2019 · To address this issue, I coded a simple weighted binary cross entropy loss function in Keras with Tensorflow as the backend. def weighted_bce(y_true, y_pred): weights = (y_true * 59.) + 1. bce = K.binary_crossentropy(y_true, y_pred) weighted_bce = K.mean(bce * weights) return weighted_bce
Sep 05, 2019 · To address this issue, I coded a simple weighted binary cross entropy loss function in Keras with Tensorflow as the backend. def weighted_bce(y_true, y_pred): weights = (y_true * 59.) + 1. bce = K.binary_crossentropy(y_true, y_pred) weighted_bce = K.mean(bce * weights) return weighted_bce
weight: array of size (num_classes, num_classes) giving the pairwise: penalty weights: Returns-----weighted_categorical_crossentropy: a function that complies with Keras' loss function api and returns the categorical crossentropy weighted : as specified """ def w_categorical_crossentropy (y_true, y_pred, weights): # Scalar; number of classes: nb_cl = len (weights)
It works by including the loss weights into the definition of the loss function itself. code by author: weight adjuster callback and how to include it in your ...
The purpose of loss functions is to compute the quantity that a model should seek to ... acts as reduction weighting coefficient for the per-sample losses.
27.09.2019 · You can set the class weight for every class when the dataset is unbalanced. Let’s say you have 5000 samples of class dog and 45000 samples of class not-dog than you feed in class_weight = {0: 5, 1: 0.5}. That gives class “dog” 10 times the weight of class “not-dog” means that in your loss function you assign a higher value to these ...