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math formula fixes 2021.1 #3513

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math formula fixes 2021.1 #3513

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10 changes: 6 additions & 4 deletions docs/MO_DG/prepare_model/convert_model/Legacy_IR_Layers_Catalog_Spec.md
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Original file line number Diff line number Diff line change
Expand Up @@ -1582,9 +1582,9 @@ OI, which means that Input changes the fastest, then Output.

**Mathematical Formulation**

\f[
output[:, ... ,:, i, ... , j,:, ... ,:] = input2[:, ... ,:, input1[i, ... ,j],:, ... ,:]
\f]
\f[
output[:, ... ,:, i, ... , j,:, ... ,:] = input2[:, ... ,:, input1[i, ... ,j],:, ... ,:]
\f]


**Inputs**
Expand Down Expand Up @@ -5086,7 +5086,9 @@ t \in \left ( 0, \quad tiles \right )

Output tensor is populated by values computes in the following way:

output[i1, ..., i(axis-1), j, i(axis+1) ..., iN] = top_k(input[i1, ...., i(axis-1), :, i(axis+1), ..., iN]), k, sort, mode)
\f[
output[i1, ..., i(axis-1), j, i(axis+1) ..., iN] = top_k(input[i1, ...., i(axis-1), :, i(axis+1), ..., iN]), k, sort, mode)
\f]

So for each slice `input[i1, ...., i(axis-1), :, i(axis+1), ..., iN]` which represents 1D array, top_k value is computed individually. Sorting and minimum/maximum are controlled by `sort` and `mode` attributes.

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6 changes: 3 additions & 3 deletions docs/ops/activation/Mish_4.md
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Expand Up @@ -26,9 +26,9 @@

For each element from the input tensor calculates corresponding
element in the output tensor with the following formula:
\f[
Mish(x) = x*tanh(ln(1.0+e^{x}))
\f]
\f[
Mish(x) = x*tanh(ln(1.0+e^{x}))
\f]

**Examples**

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7 changes: 4 additions & 3 deletions docs/ops/activation/Sigmoid_1.md
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Expand Up @@ -20,6 +20,7 @@

For each element from the input tensor calculates corresponding
element in the output tensor with the following formula:
\f[
sigmoid( x ) = \frac{1}{1+e^{-x}}
\f]

\f[
sigmoid( x ) = \frac{1}{1+e^{-x}}
\f]
6 changes: 3 additions & 3 deletions docs/ops/activation/Swish_4.md
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Expand Up @@ -9,9 +9,9 @@
**Detailed description**: For each element from the input tensor calculates corresponding
element in the output tensor with the following formula:

\f[
Swish(x) = x / (1.0 + e^{-(beta * x)})
\f]
\f[
Swish(x) = x / (1.0 + e^{-(beta * x)})
\f]

The Swish operation is introduced in the [article](https://arxiv.org/pdf/1710.05941.pdf).

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