未验证 提交 2c471db2 编写于 作者: C Cao Ying 提交者: GitHub

Merge pull request #5884 from lcy-seso/fix_latex

fix LaTeX syntax in three operators' comments.
......@@ -32,19 +32,19 @@ class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
"[(D + 2) x D]. The learnable parameter for the linear_chain_crf "
"operator. See more details in the operator's comments.");
AddInput("Label",
"(LoDTensor, default LoDTensor<int>) A LoDTensor with shape "
"(LoDTensor, default LoDTensor<int64_t>) A LoDTensor with shape "
"[N x 1], where N is the total element number in a mini-batch. "
"The ground truth.");
AddOutput(
"Alpha",
"(Tensor, default Tensor<float>) A 2-D Tensor with shape [N x D]. "
"The forward vectors for the entire batch. Denote it as \f$\alpha\f$. "
"\f$\alpha$\f is a memo table used to calculate the normalization "
"factor in CRF. \f$\alpha[k, v]$\f stores the unnormalized "
"The forward vectors for the entire batch. Denote it as $\alpha$. "
"$\alpha$ is a memo table used to calculate the normalization "
"factor in CRF. $\alpha[k, v]$ stores the unnormalized "
"probabilites of all possible unfinished sequences of tags that end at "
"position \f$k$\f with tag \f$v$\f. For each \f$k$\f, "
"\f$\alpha[k, v]$\f is a vector of length \f$D$\f with a component for "
"each tag value \f$v$\f. This vector is called a forward vecotr and "
"position $k$ with tag $v$. For each $k$, "
"$\alpha[k, v]$ is a vector of length $D$ with a component for "
"each tag value $v$. This vector is called a forward vecotr and "
"will also be used in backward computations.")
.AsIntermediate();
AddOutput(
......@@ -73,9 +73,9 @@ LinearChainCRF Operator.
Conditional Random Field defines an undirected probabilistic graph with nodes
denoting random variables and edges denoting dependencies between these
variables. CRF learns the conditional probability \f$P(Y|X)\f$, where
\f$X = (x_1, x_2, ... , x_n)\f$ are structured inputs and
\f$Y = (y_1, y_2, ... , y_n)\f$ are labels for the inputs.
variables. CRF learns the conditional probability $P(Y|X)$, where
$X = (x_1, x_2, ... , x_n)$ are structured inputs and
$Y = (y_1, y_2, ... , y_n)$ are labels for the inputs.
Linear chain CRF is a special case of CRF that is useful for sequence labeling
task. Sequence labeling tasks do not assume a lot of conditional
......@@ -88,21 +88,22 @@ CRF. Please refer to http://www.cs.columbia.edu/~mcollins/fb.pdf and
http://cseweb.ucsd.edu/~elkan/250Bwinter2012/loglinearCRFs.pdf for details.
Equation:
1. Denote Input(Emission) to this operator as \f$x\f$ here.
1. Denote Input(Emission) to this operator as $x$ here.
2. The first D values of Input(Transition) to this operator are for starting
weights, denoted as \f$a\f$ here.
weights, denoted as $a$ here.
3. The next D values of Input(Transition) of this operator are for ending
weights, denoted as \f$b\f$ here.
weights, denoted as $b$ here.
4. The remaning values of Input(Transition) are for transition weights,
denoted as \f$w\f$ here.
5. Denote Input(Label) as \f$s\f$ here.
The probability of a sequence \f$s\f$ of length \f$L\f$ is defined as:
\f$P(s) = (1/Z) \exp(a_{s_1} + b_{s_L}
+ \sum_{l=1}^L x_{s_l}
+ \sum_{l=2}^L w_{s_{l-1},s_l})\f$
where \f$Z\f$ is a normalization value so that the sum of \f$P(s)\f$ over
all possible sequences is \f$1\f$, and \f$x\f$ is the emission feature weight
denoted as $w$ here.
5. Denote Input(Label) as $s$ here.
The probability of a sequence $s$ of length $L$ is defined as:
$$P(s) = (1/Z) \exp(a_{s_1} + b_{s_L}
+ \sum_{l=1}^L x_{s_l}
+ \sum_{l=2}^L w_{s_{l-1},s_l})$$
where $Z$ is a normalization value so that the sum of $P(s)$ over
all possible sequences is 1, and $x$ is the emission feature weight
to the linear chain CRF.
Finally, the linear chain CRF operator outputs the logarithm of the conditional
......
......@@ -59,7 +59,7 @@ Then the ratio of the exponential of the given dimension and the sum of
exponential values of all the other dimensions is the output of the softmax
operator.
For each row `i` and each column `j` in input X, we have:
For each row $i$ and each column $j$ in Input(X), we have:
$$Y[i, j] = \frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])}$$
)DOC");
......
......@@ -67,15 +67,15 @@ The equation is as follows:
1) Hard label (one-hot label, so every sample has exactly one class)
$$Loss_j = \f$ -\text{Logit}_{Label_j} +
$$Loss_j = -\text{Logit}_{Label_j} +
\log\left(\sum_{i=0}^{K}\exp(\text{Logit}_i)\right),
j = 1, ..., K $\f$$
j = 1,..., K$$
2) Soft label (each sample can have a distribution over all classes)
$$Loss_j = \f$ -\sum_{i=0}^{K}\text{Label}_i\left(\text{Logit}_i -
$$Loss_j = -\sum_{i=0}^{K}\text{Label}_i \left(\text{Logit}_i -
\log\left(\sum_{i=0}^{K}\exp(\text{Logit}_i)\right)\right),
j = 1,...,K $\f$$
j = 1,...,K$$
)DOC");
}
......
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