objective-function

SCIP Objective Function quicksum over a exponential term

由 送分小仙女□ 提交于 2021-01-29 17:22:03
问题 I am having issues summing over an exponential equation and using this as is the objective function. Am I able to use the exponential equation in the objective function? If not, is it possible to put the exponential function in as a constraint? Any help on this would be appreciated. import pandas as pd from pyscipopt import Model, quicksum, multidict, exp num_fac_to_open = 1 order_to_open = [] opened_fac = [] closed_fac = [0, 1, 2] S = [0, 1, 2] R = [10, 11, 12] distance_dict = {(0, 10): 300

Keras: Why do loss functions have to return one scalar per batch item rather than just one scalar?

由 蓝咒 提交于 2020-01-05 04:05:16
问题 I'm writing a custom loss function in Keras and just tripped over the following: Why do Keras loss functions have to return one scalar per batch item rather than just one scalar? I care about the cumulative loss for the whole batch, not about the loss per item, don't I? 回答1: I think I figured it out: fit() has an argument sample_weight with which you can assign different weights to different samples in the batch. In order for this to work you need the loss function to return the loss per

How to interpret “loss” and “accuracy” for a machine learning model

由 空扰寡人 提交于 2019-11-28 02:36:37
When I trained my neural network with Theano or Tensorflow, they will report a variable called "loss" per epoch. How should I interpret this variable? Higher loss is better or worse, or what does it mean for the final performance (accuracy) of my neural network? Amir The lower the loss, the better a model (unless the model has over-fitted to the training data). The loss is calculated on training and validation and its interperation is how well the model is doing for these two sets. Unlike accuracy, loss is not a percentage. It is a summation of the errors made for each example in training or

How to interpret “loss” and “accuracy” for a machine learning model

由 一世执手 提交于 2019-11-26 22:28:13
问题 When I trained my neural network with Theano or Tensorflow, they will report a variable called "loss" per epoch. How should I interpret this variable? Higher loss is better or worse, or what does it mean for the final performance (accuracy) of my neural network? 回答1: The lower the loss, the better a model (unless the model has over-fitted to the training data). The loss is calculated on training and validation and its interperation is how well the model is doing for these two sets. Unlike
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