Cross-entropy

The cross-entropy of the distribution ${\displaystyle q}$ relative to a distribution ${\displaystyle p}$ over a given set is defined as follows:

${\displaystyle H(p,q)=-\operatorname {E} _{p}[\log q]}$,

where ${\displaystyle E_{p}[\cdot ]}$ is the expected value operator with respect to the distribution ${\displaystyle p}$.

The definition may be formulated using the Kullback–Leibler divergence ${\displaystyle D_{\mathrm {KL} }(p\parallel q)}$, divergence of ${\displaystyle p}$ from ${\displaystyle q}$ (also known as the relative entropy of ${\displaystyle p}$ with respect to ${\displaystyle q}$).

${\displaystyle H(p,q)=H(p)+D_{\mathrm {KL} }(p\parallel q),}$

where ${\displaystyle H(p)}$ is the entropy of ${\displaystyle p}$.

For discrete probability distributions ${\displaystyle p}$ and ${\displaystyle q}$ with the same support ${\displaystyle {\mathcal {X}}}$ this means

${\displaystyle H(P,Q)=-\sum _{x\in {\mathcal {X}}}p(x)\,\log q(x)}$

(Eq.1)

The situation for continuous distributions is analogous. We have to assume that ${\displaystyle p}$ and ${\displaystyle q}$ are absolutely continuous with respect to some reference measure ${\displaystyle r}$ (usually ${\displaystyle r}$ is a Lebesgue measure on a Borel σ-algebra). Let ${\displaystyle P}$ and ${\displaystyle Q}$ be probability density functions of ${\displaystyle p}$ and ${\displaystyle q}$ with respect to ${\displaystyle r}$. Then

${\displaystyle -\int _{\mathcal {X}}P(x)\,\log Q(x)\,dr(x)=\operatorname {E} _{p}[-\log Q]}$

and therefore

${\displaystyle H(p,q)=-\int _{\mathcal {X}}P(x)\,\log Q(x)\,dr(x)}$

(Eq.2)

NB: The notation ${\displaystyle H(p,q)}$ is also used for a different concept, the joint entropy of ${\displaystyle p}$ and ${\displaystyle q}$.

In information theory, the Kraft–McMillan theorem establishes that any directly decodable coding scheme for coding a message to identify one value ${\displaystyle x_{i}}$ out of a set of possibilities ${\displaystyle \{x_{1},\ldots ,x_{n}\}}$ can be seen as representing an implicit probability distribution ${\displaystyle q(x_{i})=\left({\frac {1}{2}}\right)^{\ell _{i}}}$ over ${\displaystyle \{x_{1},\ldots ,x_{n}\}}$, where ${\displaystyle \ell _{i}}$ is the length of the code for ${\displaystyle x_{i}}$ in bits. Therefore, cross-entropy can be interpreted as the expected message-length per datum when a wrong distribution ${\displaystyle q}$ is assumed while the data actually follows a distribution ${\displaystyle p}$. That is why the expectation is taken over the true probability distribution ${\displaystyle p}$ and not ${\displaystyle q}$. Indeed the expected message-length under the true distribution ${\displaystyle p}$ is

$${\displaystyle \operatorname {E} _{p}[\ell ]=-\operatorname {E} _{p}\left[{\frac {\ln {q(x)}}{\ln(2)}}\right]=-\operatorname {E} _{p}\left[\log _{2}{q(x)}\right]=-\sum _{x_{i}}p(x_{i})\,\log _{2}q(x_{i})=-\sum _{x}p(x)\,\log _{2}q(x)=H(p,q).}$$

There are many situations where cross-entropy needs to be measured but the distribution of ${\displaystyle p}$ is unknown. An example is language modeling, where a model is created based on a training set ${\displaystyle T}$, and then its cross-entropy is measured on a test set to assess how accurate the model is in predicting the test data. In this example, ${\displaystyle p}$ is the true distribution of words in any corpus, and ${\displaystyle q}$ is the distribution of words as predicted by the model. Since the true distribution is unknown, cross-entropy cannot be directly calculated. In these cases, an estimate of cross-entropy is calculated using the following formula:

${\displaystyle H(T,q)=-\sum _{i=1}^{N}{\frac {1}{N}}\log _{2}q(x_{i})}$

where ${\displaystyle N}$ is the size of the test set, and ${\displaystyle q(x)}$ is the probability of event ${\displaystyle x}$ estimated from the training set. In other words, ${\displaystyle q(x_{i})}$ is the probability estimate of the model that the i-th word of the text is ${\displaystyle x_{i}}$. The sum is averaged over the ${\displaystyle N}$ words of the test. This is a Monte Carlo estimate of the true cross-entropy, where the test set is treated as samples from ${\displaystyle p(x)}$.

In classification problems we want to estimate the probability of different outcomes. Let the estimated probability of outcome ${\displaystyle i}$ be ${\displaystyle q_{\theta }(X=i)}$ with to-be-optimized parameters ${\displaystyle \theta }$ and let the frequency (empirical probability) of outcome ${\displaystyle i}$ in the training set be ${\displaystyle p(X=i)}$. Given N conditionally independent samples in the training set, then the likelihood of the parameters ${\displaystyle \theta }$ of the model ${\displaystyle q_{\theta }(X=x)}$ on the training set is

$${\displaystyle {\mathcal {L}}(\theta )=\prod _{i\in X}({\mbox{est. probability of }}i)^{{\mbox{number of occurrences of }}i}=\prod _{i}q_{\theta }(X=i)^{Np(X=i)}}$$

where the last expression is due to the definition of the multinomial PMF. Therefore, the log-likelihood, divided by ${\displaystyle N}$ is

$${\displaystyle {\frac {1}{N}}\log({\mathcal {L}}(\theta ))={\frac {1}{N}}\log \prod _{i}q_{\theta }(X=i)^{Np(X=i)}=\sum _{i}p(X=i)\log q_{\theta }(X=i)=-H(p,q)}$$

so that maximizing the likelihood with respect to the parameters ${\displaystyle \theta }$ is the same as minimizing the cross-entropy.

Cross-entropy minimization is frequently used in optimization and rare-event probability estimation. When comparing a distribution ${\displaystyle q}$ against a fixed reference distribution ${\displaystyle p}$, cross-entropy and KL divergence are identical up to an additive constant (since ${\displaystyle p}$ is fixed): According to the Gibbs' inequality, both take on their minimal values when ${\displaystyle p=q}$, which is ${\displaystyle 0}$ for KL divergence, and ${\displaystyle \mathrm {H} (p)}$ for cross-entropy. In the engineering literature, the principle of minimizing KL divergence (Kullback's "Principle of Minimum Discrimination Information") is often called the Principle of Minimum Cross-Entropy (MCE), or Minxent.

However, as discussed in the article Kullback–Leibler divergence, sometimes the distribution ${\displaystyle q}$ is the fixed prior reference distribution, and the distribution ${\displaystyle p}$ is optimized to be as close to ${\displaystyle q}$ as possible, subject to some constraint. In this case the two minimizations are not equivalent. This has led to some ambiguity in the literature, with some authors attempting to resolve the inconsistency by restating cross-entropy to be ${\displaystyle D_{\mathrm {KL} }(p\parallel q)}$, rather than ${\displaystyle H(p,q)}$. In fact, cross-entropy is another name for relative entropy, see Cover and Thomas [1] and Good.[2] On the other hand, ${\displaystyle H(p,q)}$ does not agree with the literature and can be misleading.

Cross-entropy can be used to define a loss function in machine learning and optimization. The true probability ${\displaystyle p_{i}}$ is the true label, and the given distribution ${\displaystyle q_{i}}$ is the predicted value of the current model. This is also known as the log loss (or logarithmic loss[3] or logistic loss);[4] the terms "log loss" and "cross-entropy loss" are used interchangeably.[5]

More specifically, consider a binary regression model which can be used to classify observations into two possible classes (often simply labelled ${\displaystyle 0}$ and ${\displaystyle 1}$). The output of the model for a given observation, given a vector of input features ${\displaystyle x}$, can be interpreted as a probability, which serves as the basis for classifying the observation. In logistic regression, the probability is modeled using the logistic function ${\displaystyle g(z)=1/(1+e^{-z})}$ where ${\displaystyle z}$ is some function of the input vector ${\displaystyle x}$, commonly just a linear function. The probability of the output ${\displaystyle y=1}$ is given by

${\displaystyle q_{y=1}={\hat {y}}\equiv g(\mathbf {w} \cdot \mathbf {x} )={\frac {1}{1+e^{-\mathbf {w} \cdot \mathbf {x} }}},}$

where the vector of weights ${\displaystyle \mathbf {w} }$ is optimized through some appropriate algorithm such as gradient descent. Similarly, the complementary probability of finding the output ${\displaystyle y=0}$ is simply given by

${\displaystyle q_{y=0}=1-{\hat {y}}}$

Having set up our notation, ${\displaystyle p\in \{y,1-y\}}$ and ${\displaystyle q\in \{{\hat {y}},1-{\hat {y}}\}}$, we can use cross-entropy to get a measure of dissimilarity between ${\displaystyle p}$ and ${\displaystyle q}$:

${\displaystyle H(p,q)\ =\ -\sum _{i}p_{i}\log q_{i}\ =\ -y\log {\hat {y}}-(1-y)\log(1-{\hat {y}})}$

Logistic regression typically optimizes the log loss for all the observations on which it is trained, which is the same as optimizing the average cross-entropy in the sample. For example, suppose we have ${\displaystyle N}$ samples with each sample indexed by ${\displaystyle n=1,\dots ,N}$. The average of the loss function is then given by:

${\displaystyle {\begin{aligned}J(\mathbf {w} )\ &=\ {\frac {1}{N}}\sum _{n=1}^{N}H(p_{n},q_{n})\ =\ -{\frac {1}{N}}\sum _{n=1}^{N}\ {\bigg [}y_{n}\log {\hat {y}}_{n}+(1-y_{n})\log(1-{\hat {y}}_{n}){\bigg ]}\,,\end{aligned}}}$

where ${\displaystyle {\hat {y}}_{n}\equiv g(\mathbf {w} \cdot \mathbf {x} _{n})=1/(1+e^{-\mathbf {w} \cdot \mathbf {x} _{n}})}$, with ${\displaystyle g(z)}$ the logistic function as before.

The logistic loss is sometimes called cross-entropy loss. It is also known as log loss (In this case, the binary label is often denoted by {−1,+1}).[6]

Remark: The gradient of the cross-entropy loss for logistic regression is the same as the gradient of the squared error loss for linear regression. That is, define

${\displaystyle X^{T}={\begin{pmatrix}1&x_{11}&\dots &x_{1p}\\1&x_{21}&\cdots &x_{2p}\\\vdots &\vdots &&\vdots \\1&x_{n1}&\cdots &x_{np}\\\end{pmatrix}}\in \mathbb {R} ^{n\times (p+1)}}$

${\displaystyle {\hat {y_{i}}}={\hat {f}}(x_{i1},\dots ,x_{ip})={\frac {1}{1+\exp(-\beta _{0}-\beta _{1}x_{i1}-\dots -\beta _{p}x_{ip})}}}$

${\displaystyle L({\overrightarrow {\beta }})=-\sum _{i=1}^{N}[y_{i}\log {\hat {y}}_{i}+(1-y_{i})\log(1-{\hat {y}}_{i})]}$

Then we have the result

${\displaystyle {\frac {\partial }{\partial {\overrightarrow {\beta }}}}L({\overrightarrow {\beta }})=X^{T}({\hat {Y}}-Y)}$

The proof is as follows. For any ${\displaystyle {\hat {y}}_{i}}$, we have

${\displaystyle {\frac {\partial }{\partial \beta _{0}}}\ln {\frac {1}{1+e^{-\beta _{0}+k_{0}}}}={\frac {e^{-\beta _{0}+k_{0}}}{1+e^{-\beta _{0}+k_{0}}}}}$

${\displaystyle {\frac {\partial }{\partial \beta _{0}}}\ln \left(1-{\frac {1}{1+e^{-\beta _{0}+k_{0}}}}\right)={\frac {-1}{1+e^{-\beta _{0}+k_{0}}}}}$

${\displaystyle {\begin{aligned}{\frac {\partial }{\partial \beta _{0}}}L({\overrightarrow {\beta }})&=-\sum _{i=1}^{N}\left[{\frac {y_{i}\cdot e^{-\beta _{0}+k_{0}}}{1+e^{-\beta _{0}+k_{0}}}}-(1-y_{i}){\frac {1}{1+e^{-\beta _{0}+k_{0}}}}\right]\\&=-\sum _{i=1}^{N}[y_{i}-{\hat {y}}_{i}]=\sum _{i=1}^{N}({\hat {y}}_{i}-y_{i})\end{aligned}}}$

${\displaystyle {\frac {\partial }{\partial \beta _{1}}}\ln {\frac {1}{1+e^{-\beta _{1}x_{i1}+k_{1}}}}={\frac {x_{i1}e^{k_{1}}}{e^{\beta _{1}x_{i1}}+e^{k_{1}}}}}$

${\displaystyle {\frac {\partial }{\partial \beta _{1}}}\ln \left[1-{\frac {1}{1+e^{-\beta _{1}x_{i1}+k_{1}}}}\right]={\frac {-x_{i1}e^{\beta _{1}x_{i1}}}{e^{\beta _{1}x_{i1}}+e^{k_{1}}}}}$

${\displaystyle {\frac {\partial }{\partial \beta _{1}}}L({\overrightarrow {\beta }})=-\sum _{i=1}^{N}x_{i1}(y_{i}-{\hat {y}}_{i})=\sum _{i=1}^{N}x_{i1}({\hat {y}}_{i}-y_{i})}$

In a similar way, we eventually obtain the desired result.

• Cross-entropy method
• Logistic regression
• Conditional entropy
• Kullback-Leibler distance
• Maximum likelihood estimation
• Mutual information

• A Tutorial on the Cross-Entropy Method