Questions tagged [cost-function]

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Chain function in backpropagation

I'm reading a Neural Networks Tutorial. In order to answer my question you might have to take a brief look at it. I understand everything until something they declare as "chain function": It's ...
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1answer
11 views

Norm type in cost function of ANN

I'm reading a tutorial about ANN. They use the following cost function: As you can see this equation includes a norm. I'm new to the concept of norm. My question is what kind of norm they use here (...
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1answer
28 views

Regularization for intercept parameter

Why is the regularization parameter not applied to the intercept parameter? From what I have read about the cost functions for Linear and Logistic regression, the regularization parameter (λ) is ...
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7 views

How to design a two-factor optimization score function?

What if I have two parameters as optimization target and want to maximize one and minimize the other one? Example: food products dataset, look for strawberries and artichokes in USDA FDC. Task: find ...
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1answer
44 views

Reason for capping Learning Rate (alpha) up to 1 for Gradient Descent

I am learning to implement Gradient Descent algorithm in Python and came across the problem of selecting the right learning rate. I have learned that learning rates are usually selected up to 1 (...
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1answer
27 views

Andrew Ngs Class - Why Did He Change up the Cost Function?

I am taking Andrew Ng's Machine Learning Intro class. Looks like he changed the cost function without any explanation in the second week. Specifically: He no longer squares each deviation between the ...
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0answers
17 views

Theano gradient descent and cost function issue

Sorry but can anyone point out whats wrong with this code? ...
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1answer
63 views

How do the policy gradient's cost function and gradients work?

I am not a math expert but have a basic understanding of linear algebra, calculus and probability and I understand the math behind back propagation. Currently I am trying to learn about policy ...
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1answer
201 views

Policy gradient vs cost function

I was working with continuous system RL and obviously stumbled across this Policy Gradient. I want to know is this something like cost function for RL? It kinda gives that impression considering we ...
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0answers
58 views

Neural network cost is constant never changing during training

I am trying to build a binary classifier to predict a pulsar star with Single Hidden layer Neural Network. But the cost on training dataset after almost 100 iterations has no change, following is the ...
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2answers
49 views

Question of using gradient descent instead of calculus. I checked previous questions there are still points to clarify

First of all I checked http://stats.stackexchange.com/questions/23128/solving-for-regression-parameters-in-closed-form-vs-gradient-descent, http://stackoverflow.com/questions/26804656/why-do-we-use-...
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1answer
30 views

Reason behind the sum of rate factors for calculating cost function derivative

Suppose we have a network of neurons like below: We make a little change in weight w[l][j][k] on our network, and it can make change on our cost function from ...
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0answers
93 views

Minimum cost is not zero when calculating cross-entropy on soft labels

I am training a neural network using batches of soft labels, e.g. y = [[0.00, 0.25, 0.25, 0.50], ... [0.75, 0.00, 0.20, 0.05]] However, as ...
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Understanding about RNN loss

Problem I am now taking Andrew Ng's deep learning course on Coursera. Everything is great but when it comes to RNN, I sometimes feel confused. Here is a question about RNN (or more specifically, the ...
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2answers
78 views

Two different cost functions for neural networks, how they can give the same result?

One is: $$J=-\frac{1}{m}\sum_{i=1}^{m}\sum_{k=1}^{K}\Big[y_{k}^{i}\log\big((h_{\theta}(x^{i}))_k\big)+(1-y_{k}^{i})\log\big(1-(h_{\theta}(x^{i}))_k\big)\Big]$$ The other one is: $$J=-\frac{1}{m}\sum_{...
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2answers
38 views

Cost function - ideas

I build xgboost model for regression problem. By the default xgboost optimize $(y - y_{pred})^2$, so the RMSE will be the best eval metric to measure performance. But my task is to build the best ...
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3answers
158 views

How does cost function change by choice of activation function (ReLU, Sigmoid, Softmax)?

I am new to ML and as I take courses for the area DL, I am wondering, by our choice of activation function for the last layer, whether we take sigmoid, relu or softmax, would the formula for ...
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2answers
3k views

Why do we have to divide by 2 in the ML squared error cost function? [duplicate]

I'm not sure why you need to multiply by $\frac1{2m}$ in the beginning. I understand that you would have to divide the whole sum by $\frac1{m}$, but why do we have to multiply $m$ by two? Is it ...
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1answer
20 views

Decision Tree Optimize Deviation From Objective

I have the following problem: I have three classes/modes, let's call them car, bike, and walking. For any given test data instance with some environmental variables such as distance, road quality etc, ...
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1answer
34 views

How is the linear regression cost function evolved?

A couple of weeks ago I joined the Standford University machine learning course on Coursera. In that course, they directly gave the cost function formula without telling how this formula was evolved. ...
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1answer
45 views

How to get a rebalance strategy with a cost matrix?

In the case of a classification problem where a cost matrix is used to maximize the model performance, it is common to do a rebalance technique. Let's say for example that I have the following costs ...
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1answer
1k views

Cost sensitive classification with individual cost

I'm currently sitting on a problem, where i'm uncertain if there is not a much simpler solution. I'm trying to train a DNN with a dataset for a classification task that should be cost sensitive. ...
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1answer
149 views

Logitic Regression cost function - what if ln(0)?

I am building logistic regression from scrap. The simplified cost function I am using is (from machine learning course on coursera): in specific case during learning, one observation in training ...
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1answer
63 views

Understanding minimizing cost correctly

I cannot wrap my head around this simple concept. Suppose we have a linear regression, and there is a single parameter theta to be optimized (for simplicity purposes): $h(x) = \theta \cdot x$ The ...
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1answer
8k views

What do “compile” , “fit” and “predict” do in Keras sequential models?

I am a little confused between these two parts of Keras sequential models functions. May someone explains what is exactly the job of each one? I mean ...
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4answers
162 views

Weights not converging while cost function has converged in neural networks

My cost/loss function drops drastically and approaches 0, which looks a sign of convergence. But the weights are still changing in a visible way, a lot faster than the cost function. Should I ensure ...
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1answer
5k views

How does Gradient Descent and Backpropagation work together?

Please forgive me as I am new to this. I have attached a diagram trying to model my understanding of neural network and Back-propagation? From videos on Coursera and resources online I formed the ...
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2answers
185 views

What is an intuitive explanation for the log loss cost function?

I would really appreciate if someone could explain the log loss cost function And the use of it in measuring a classification model performance. I have read a few articles but most of them ...
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2answers
367 views

Logistic regression cost function

In Aurelien Geron's book I found this line ...
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1answer
2k views

Should the minimum value of a cost (loss) function be equal to zero?

We know optimization techniques search in the space of all the possible parameters for a parameter set that minimizes the cost function of the model. The most well-known loss functions, like MSE or ...
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1answer
92 views

How to Define a Cost Fucntion?

I want to define a cost function in python to identify optimum value in days when i should end a marketing campaign to save spend on campaigns not generating traffic good traffic. Problem is I dont ...
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1answer
383 views

boosting an xgboost classifier with another xgboost classifier using different sets of features

What I would like to do, is train a first model $f_{1}(\underline{x})$, where $\underline{x}$ is a set of features, fix what model 1 has learned, and then train a second model $f_{2}(\underline{y})$ ...
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0answers
143 views

How to resolve the instability of average reward per episode in training of DQN (Deep Q-Network)?

what is shown when average reward per episode in training is unstable? If there is big difference between average reward per episode and final reward by test section, what we can say? For ...
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1answer
174 views

Policy Gradient Methods - ScoreFunction & Log(policy)

In Policy Gradient Methods, Lecture 7 (34:15), David describes a Score Function as being the Gradient of the Log of the policy Question: If we have a Neural ...
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2answers
461 views

Should the cost function be zero using TensorFlow's sigmoid_cross_entropy_with_logits?

I'm building a CNN to make a binary classification (1 or zero). For this, I'm using the cost function sigmoid_cross_entropy_with_logits. But for some reason, the ...
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1answer
5k views

What is the Time Complexity of Linear Regression?

I am working with linear regression and I would like to know the Time complexity in big-O notation. The cost function of linear regression without an optimisation algorithm (such as Gradient descent) ...
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2answers
108 views

Why do we double the number in a quadratic cost function or MSE?

$$ C(w,b) = \frac{1}{2n}\sum_{x}||y(x)-a||^2 $$ Where y is a 10-dimensional vector, a is the output, w is the weight and b is the bias and n is the number of inputs. If this is the MSE, shouldn't it ...
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2answers
3k views

What are the cases where it is fine to initialize all weights to zero

I've taken a few online courses in machine learning, and in general, the advice has been to choose random weights for a neural network to ensure that your neurons don't all learn the same thing, ...
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0answers
355 views

Cost/loss functions for multi-tasking regression neural networks

The mean square loss function is the standard for regression neural networks. However, if I have a neural network learning two tasks (two outputs) at once, is it more advisable to train on the sum of ...
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1answer
491 views

asymmetric cost function for deep neural network binary classifier

I am building a deep neural network based binary classifier, with single output. The loss function I actually want to minimize is $$ \mathcal L(\hat y,y) = \begin{cases} 0, & \text{if $\hat y$ = ...
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2answers
892 views

logistic regression algorithm fails to work

I'm trying to code my own logistic regression algorithm using Andrew NG's machine learning using Octave. lectures. So what I did was make a csv file, the first row being some parameter and the second ...
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1answer
202 views

Derivation of the cross-entropy equation in Michael Nielsen's book

I am reading the book http://neuralnetworksanddeeplearning.com/chap3.html by Michael Nielsen. So this is a question mostly for the people familiar with the book and understanding the material. In the ...
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1answer
1k views

Gradient Descent in logistic regression

Logistic and Linear Regression have different cost functions. But I don't get how the gradient descent in logistic regression is the same as Linear Regression. We get the Gradient Descent formula by ...
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2answers
2k views

Softmax classifier never allows for 100% probability in LSTM?

When working with LSTM I am using a softmax classifier and a one-hot encoded vector approach. The softmax looks like this: $$S(h_i) = \frac{e^{h_i}}{\sum e^{h_{total}}}$$ notice, LSTM's result is a $...
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1answer
162 views

My LSTM can't reduce error down to zero, when overfitting

I have implemented LSTM in c++ which steadily decreases in error, but slows down at the certain error value. It also seems to predict most of the characters, but gets stuck and not able to correct ...
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0answers
103 views

How to decrease the learning rate only when cost function is stagnant?

I'm training a very complex function in tensorflow. Is there a way to decrease the learning rate only when the cost isin't decreasing very much?
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1answer
703 views

Logistic Regression : Solving the cross-entropy cost function analytically

Logistic regression cost function is cross-entropy. It is defined as below: This is a convex function. To reach the minimum, scikit-learn provides multiple types of solvers such as : ‘liblinear’ ...
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1answer
3k views

Cost function for Ordinal Regression using neural networks

What is the best cost function to train a neural network to perform ordinal regression, i.e. to predict a result whose value exists on an arbitrary scale where only the relative ordering between ...
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1answer
1k views

Understanding Logistic Regression Cost function

Linear Regression cost function: $$J(\theta) = \frac{1}{2 m} \sum_{i=1}^m (h_{\theta}(x^{(i)}) - y^{(i)})^2$$ where: $$h_{\theta}(x) = \theta_0 + \theta_1 x_1$$ Logistic Regression cost function $...
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24k views

Python implementation of cost function in logistic regression: why dot multiplication in one expression but element-wise multiplication in another

I have a very basic question which relates to Python, numpy and multiplication of matrices in the setting of logistic regression. First, let me apologise for not using math notation. I am confused ...