From de53c405783a207a912aa3be46b4881432aa1c19 Mon Sep 17 00:00:00 2001 From: Patryk Elszkowski Date: Fri, 2 Jul 2021 12:51:00 +0200 Subject: [PATCH] bulk change `type T` to `type *T*` in spec (#6486) * bulk change `type T` to `type *T*` in spec * update all `T` which referee to type to use *T* pattern * get back with `T` where T is dimension * fix *T*1 -> *T1* * Make italic types where was no formating --- docs/ops/activation/Clamp_1.md | 6 +- docs/ops/activation/Elu_1.md | 6 +- docs/ops/activation/Exp_1.md | 6 +- docs/ops/activation/GELU_2.md | 4 +- docs/ops/activation/HardSigmoid_1.md | 10 +-- docs/ops/activation/LogSoftmax_5.md | 6 +- docs/ops/activation/Mish_4.md | 4 +- docs/ops/activation/Selu_1.md | 8 +- docs/ops/activation/SoftPlus_4.md | 6 +- docs/ops/activation/Swish_4.md | 6 +- docs/ops/arithmetic/Abs_1.md | 7 +- docs/ops/arithmetic/Acos_1.md | 6 +- docs/ops/arithmetic/Acosh_3.md | 6 +- docs/ops/arithmetic/Add_1.md | 6 +- docs/ops/arithmetic/Asin_1.md | 6 +- docs/ops/arithmetic/Asinh_3.md | 6 +- docs/ops/arithmetic/Atan_1.md | 6 +- docs/ops/arithmetic/Atanh_3.md | 6 +- docs/ops/arithmetic/Cosh_1.md | 6 +- docs/ops/arithmetic/CumSum_3.md | 10 +-- docs/ops/arithmetic/Divide_1.md | 6 +- docs/ops/arithmetic/Erf_1.md | 4 +- docs/ops/arithmetic/FloorMod_1.md | 10 +-- docs/ops/arithmetic/Log_1.md | 8 +- docs/ops/arithmetic/Maximum_1.md | 10 +-- docs/ops/arithmetic/Minimum_1.md | 10 +-- docs/ops/arithmetic/Mod_1.md | 10 +-- docs/ops/arithmetic/Multiply_1.md | 8 +- docs/ops/arithmetic/Power_1.md | 10 +-- docs/ops/arithmetic/Sign_1.md | 8 +- docs/ops/arithmetic/Sin_1.md | 6 +- docs/ops/arithmetic/Sinh_1.md | 4 +- docs/ops/arithmetic/SquaredDifference_1.md | 10 +-- docs/ops/arithmetic/Subtract_1.md | 8 +- docs/ops/arithmetic/Tan_1.md | 7 +- docs/ops/comparison/Equal_1.md | 6 +- docs/ops/comparison/GreaterEqual_1.md | 4 +- docs/ops/comparison/Greater_1.md | 4 +- docs/ops/comparison/LessEqual_1.md | 6 +- docs/ops/comparison/Less_1.md | 6 +- docs/ops/comparison/NotEqual_1.md | 8 +- docs/ops/detection/RegionYolo_1.md | 10 +-- docs/ops/generation/Range_1.md | 9 +-- docs/ops/generation/Range_4.md | 7 +- docs/ops/image/Interpolate_4.md | 8 +- docs/ops/infrastructure/Result_1.md | 6 +- docs/ops/logical/LogicalAnd_1.md | 6 +- docs/ops/logical/LogicalNot_1.md | 8 +- docs/ops/logical/LogicalOr_1.md | 6 +- docs/ops/logical/LogicalXor_1.md | 8 +- docs/ops/movement/DepthToSpace_1.md | 4 +- docs/ops/movement/GatherTree_1.md | 14 ++-- docs/ops/movement/Split_1.md | 8 +- docs/ops/movement/StridedSlice_1.md | 16 ++-- docs/ops/movement/Tile_1.md | 14 ++-- docs/ops/movement/Transpose_1.md | 12 +-- docs/ops/normalization/LRN_1.md | 6 +- docs/ops/pooling/MaxPool_1.md | 74 +++++++++---------- docs/ops/sequence/CTCGreedyDecoderSeqLen_6.md | 4 +- docs/ops/sequence/CTCGreedyDecoder_1.md | 8 +- docs/ops/shape/Reshape_1.md | 2 +- 61 files changed, 253 insertions(+), 257 deletions(-) diff --git a/docs/ops/activation/Clamp_1.md b/docs/ops/activation/Clamp_1.md index 91dba4b0ab2..22d956cfc10 100644 --- a/docs/ops/activation/Clamp_1.md +++ b/docs/ops/activation/Clamp_1.md @@ -38,16 +38,16 @@ clamp( x_{i} )=\min\big( \max\left( x_{i}, min\_value \right), max\_value \big) **Inputs**: -* **1**: A tensor of type `T` and arbitrary shape. **Required**. +* **1**: A tensor of type *T* and arbitrary shape. **Required**. **Outputs**: -* **1**: A tensor of type `T` with same shape as input tensor. +* **1**: A tensor of type *T* with same shape as input tensor. **Types** * *T*: any numeric type. -* **Note**: In case of integral numeric type, ceil is used to convert *min* from `float` to `T` and floor is used to convert *max* from `float` to `T`. +* **Note**: In case of integral numeric type, ceil is used to convert *min* from `float` to *T* and floor is used to convert *max* from `float` to *T*. **Example** diff --git a/docs/ops/activation/Elu_1.md b/docs/ops/activation/Elu_1.md index 60a093c506f..ad09c00556e 100644 --- a/docs/ops/activation/Elu_1.md +++ b/docs/ops/activation/Elu_1.md @@ -20,7 +20,7 @@ Elu(x) = \left\{\begin{array}{r} where α corresponds to *alpha* attribute. -*Elu* is equivalent to *ReLU* operation when *alpha* is equal to zero. +*Elu* is equivalent to *ReLU* operation when *alpha* is equal to zero. **Attributes** @@ -34,11 +34,11 @@ where α corresponds to *alpha* attribute. **Inputs**: -* **1**: A tensor of type `T` and arbitrary shape. **Required**. +* **1**: A tensor of type *T* and arbitrary shape. **Required**. **Outputs**: -* **1**: The result of element-wise *Elu* function applied to the input tensor. A tensor of type `T` and the same shape as input tensor. +* **1**: The result of element-wise *Elu* function applied to the input tensor. A tensor of type *T* and the same shape as input tensor. **Types** diff --git a/docs/ops/activation/Exp_1.md b/docs/ops/activation/Exp_1.md index fad5c24c3bd..bb62a675a9b 100644 --- a/docs/ops/activation/Exp_1.md +++ b/docs/ops/activation/Exp_1.md @@ -18,11 +18,11 @@ exp(x) = e^{x} **Inputs** -* **1**: A tensor of type `T` and arbitrary shape. **Required**. +* **1**: A tensor of type *T* and arbitrary shape. **Required**. **Outputs** -* **1**: The result of element-wise *Exp* function applied to the input tensor. A tensor of type `T` and the same shape as input tensor. +* **1**: The result of element-wise *Exp* function applied to the input tensor. A tensor of type *T* and the same shape as input tensor. **Types** @@ -45,4 +45,4 @@ exp(x) = e^{x} -``` \ No newline at end of file +``` diff --git a/docs/ops/activation/GELU_2.md b/docs/ops/activation/GELU_2.md index 9f48eba8791..ef2921a6c7c 100644 --- a/docs/ops/activation/GELU_2.md +++ b/docs/ops/activation/GELU_2.md @@ -27,11 +27,11 @@ Additionally, *Gelu* function may be approximated as follows: **Inputs**: -* **1**: A tensor of type `T` and arbitrary shape. **Required**. +* **1**: A tensor of type *T* and arbitrary shape. **Required**. **Outputs**: -* **1**: The result of element-wise *Gelu* function applied to the input tensor. A tensor of type `T` and the same shape as input tensor. +* **1**: The result of element-wise *Gelu* function applied to the input tensor. A tensor of type *T* and the same shape as input tensor. **Types** diff --git a/docs/ops/activation/HardSigmoid_1.md b/docs/ops/activation/HardSigmoid_1.md index 629548322f7..04c09dae06e 100644 --- a/docs/ops/activation/HardSigmoid_1.md +++ b/docs/ops/activation/HardSigmoid_1.md @@ -18,15 +18,15 @@ For each element from the input tensor calculates corresponding **Inputs** -* **1**: An tensor of type T. **Required.** +* **1**: An tensor of type *T*. **Required.** -* **2**: `alpha` 0D tensor (scalar) of type T. **Required.** +* **2**: `alpha` 0D tensor (scalar) of type *T*. **Required.** -* **3**: `beta` 0D tensor (scalar) of type T. **Required.** +* **3**: `beta` 0D tensor (scalar) of type *T*. **Required.** **Outputs** -* **1**: The result of the hard sigmoid operation. A tensor of type T. +* **1**: The result of the hard sigmoid operation. A tensor of type *T*. **Types** @@ -51,4 +51,4 @@ For each element from the input tensor calculates corresponding -``` \ No newline at end of file +``` diff --git a/docs/ops/activation/LogSoftmax_5.md b/docs/ops/activation/LogSoftmax_5.md index b25bf8f9e91..c6096423032 100644 --- a/docs/ops/activation/LogSoftmax_5.md +++ b/docs/ops/activation/LogSoftmax_5.md @@ -24,11 +24,11 @@ LogSoftmax(x, axis) = t - Log(ReduceSum(Exp(t), axis)) **Inputs**: -* **1**: Input tensor *x* of type T with enough number of dimension to be compatible with *axis* attribute. Required. +* **1**: Input tensor *x* of type *T* with enough number of dimension to be compatible with *axis* attribute. Required. **Outputs**: -* **1**: The resulting tensor of the same shape and of type T. +* **1**: The resulting tensor of the same shape and of type *T*. **Types** @@ -59,4 +59,4 @@ where \f$C\f$ is a size of tensor along *axis* dimension. -``` \ No newline at end of file +``` diff --git a/docs/ops/activation/Mish_4.md b/docs/ops/activation/Mish_4.md index 30eebc71c64..e5eeba75396 100644 --- a/docs/ops/activation/Mish_4.md +++ b/docs/ops/activation/Mish_4.md @@ -20,11 +20,11 @@ Mish(x) = x\cdot\tanh\big(SoftPlus(x)\big) = x\cdot\tanh\big(\ln(1+e^{x})\big) **Inputs**: -* **1**: A tensor of type `T` and arbitrary shape. **Required**. +* **1**: A tensor of type *T* and arbitrary shape. **Required**. **Outputs**: -* **1**: The result of element-wise *Mish* function applied to the input tensor. A tensor of type `T` and the same shape as input tensor. +* **1**: The result of element-wise *Mish* function applied to the input tensor. A tensor of type *T* and the same shape as input tensor. **Types** diff --git a/docs/ops/activation/Selu_1.md b/docs/ops/activation/Selu_1.md index 0af534b8f56..4be64167157 100644 --- a/docs/ops/activation/Selu_1.md +++ b/docs/ops/activation/Selu_1.md @@ -31,15 +31,15 @@ Selu(x) = \lambda\cdot\big(\max(0, x) + \min(0, \alpha(e^{x}-1))\big) **Inputs** -* **1**: `data`. A tensor of type `T` and arbitrary shape. **Required.** +* **1**: `data`. A tensor of type *T* and arbitrary shape. **Required.** -* **2**: `alpha`. 1D tensor with one element of type `T`. **Required.** +* **2**: `alpha`. 1D tensor with one element of type *T*. **Required.** -* **3**: `lambda`. 1D tensor with one element of type `T`. **Required.** +* **3**: `lambda`. 1D tensor with one element of type *T*. **Required.** **Outputs** -* **1**: The result of element-wise *Selu* function applied to `data` input tensor. A tensor of type `T` and the same shape as `data` input tensor. +* **1**: The result of element-wise *Selu* function applied to `data` input tensor. A tensor of type *T* and the same shape as `data` input tensor. **Types** diff --git a/docs/ops/activation/SoftPlus_4.md b/docs/ops/activation/SoftPlus_4.md index e0e625ce1b4..cfb02e7335f 100644 --- a/docs/ops/activation/SoftPlus_4.md +++ b/docs/ops/activation/SoftPlus_4.md @@ -8,7 +8,7 @@ **Detailed description** -*SoftPlus* operation is introduced in this [article](https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.6419). +*SoftPlus* operation is introduced in this [article](https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.6419). *SoftPlus* performs element-wise activation function on a given input tensor, based on the following mathematical formula: @@ -35,11 +35,11 @@ For example, if *T* is `fp32`, `threshold` should be `20` or if *T* is `fp16`, ` **Inputs**: -* **1**: A tensor of type `T` and arbitrary shape. **Required**. +* **1**: A tensor of type *T* and arbitrary shape. **Required**. **Outputs**: -* **1**: The result of element-wise *SoftPlus* function applied to the input tensor. A tensor of type `T` and the same shape as input tensor. +* **1**: The result of element-wise *SoftPlus* function applied to the input tensor. A tensor of type *T* and the same shape as input tensor. **Types** diff --git a/docs/ops/activation/Swish_4.md b/docs/ops/activation/Swish_4.md index 04f17390a16..c4714bf581d 100644 --- a/docs/ops/activation/Swish_4.md +++ b/docs/ops/activation/Swish_4.md @@ -22,13 +22,13 @@ where β corresponds to `beta` scalar input. **Inputs**: -* **1**: `data`. A tensor of type `T` and arbitrary shape. **Required**. +* **1**: `data`. A tensor of type *T* and arbitrary shape. **Required**. -* **2**: `beta`. A non-negative scalar value of type `T`. Multiplication parameter for the sigmoid. Default value 1.0 is used. **Optional**. +* **2**: `beta`. A non-negative scalar value of type *T*. Multiplication parameter for the sigmoid. Default value 1.0 is used. **Optional**. **Outputs**: -* **1**: The result of element-wise *Swish* function applied to the input tensor `data`. A tensor of type `T` and the same shape as `data` input tensor. +* **1**: The result of element-wise *Swish* function applied to the input tensor `data`. A tensor of type *T* and the same shape as `data` input tensor. **Types** diff --git a/docs/ops/arithmetic/Abs_1.md b/docs/ops/arithmetic/Abs_1.md index 91b296381a7..426daee3806 100644 --- a/docs/ops/arithmetic/Abs_1.md +++ b/docs/ops/arithmetic/Abs_1.md @@ -2,7 +2,7 @@ **Versioned name**: *Abs-1* -**Category**: Arithmetic unary operation +**Category**: Arithmetic unary operation **Short description**: *Abs* performs element-wise the absolute value with given tensor. @@ -12,11 +12,11 @@ **Inputs** -* **1**: An tensor of type T. **Required.** +* **1**: An tensor of type *T*. **Required.** **Outputs** -* **1**: The result of element-wise abs operation. A tensor of type T. +* **1**: The result of element-wise abs operation. A tensor of type *T*. **Types** @@ -48,4 +48,3 @@ a_{i} = abs(a_{i}) ``` - diff --git a/docs/ops/arithmetic/Acos_1.md b/docs/ops/arithmetic/Acos_1.md index 384f033ac25..2c274b01c8a 100644 --- a/docs/ops/arithmetic/Acos_1.md +++ b/docs/ops/arithmetic/Acos_1.md @@ -2,7 +2,7 @@ **Versioned name**: *Acos-1* -**Category**: Arithmetic unary operation +**Category**: Arithmetic unary operation **Short description**: *Acos* performs element-wise inverse cosine (arccos) operation with given tensor. @@ -12,11 +12,11 @@ **Inputs** -* **1**: An tensor of type T. **Required.** +* **1**: An tensor of type *T*. **Required.** **Outputs** -* **1**: The result of element-wise acos operation. A tensor of type T. +* **1**: The result of element-wise acos operation. A tensor of type *T*. **Types** diff --git a/docs/ops/arithmetic/Acosh_3.md b/docs/ops/arithmetic/Acosh_3.md index 23f6b64385f..4c6d7b6f615 100644 --- a/docs/ops/arithmetic/Acosh_3.md +++ b/docs/ops/arithmetic/Acosh_3.md @@ -2,7 +2,7 @@ **Versioned name**: *Acosh-3* -**Category**: Arithmetic unary operation +**Category**: Arithmetic unary operation **Short description**: *Acosh* performs element-wise hyperbolic inverse cosine (arccosh) operation with given tensor. @@ -12,11 +12,11 @@ **Inputs** -* **1**: A tensor of type T. **Required.** +* **1**: A tensor of type *T*. **Required.** **Outputs** -* **1**: The result of element-wise acosh operation. A tensor of type T. +* **1**: The result of element-wise acosh operation. A tensor of type *T*. **Types** diff --git a/docs/ops/arithmetic/Add_1.md b/docs/ops/arithmetic/Add_1.md index 85a431117b1..d73bdaaea85 100644 --- a/docs/ops/arithmetic/Add_1.md +++ b/docs/ops/arithmetic/Add_1.md @@ -29,12 +29,12 @@ o_{i} = a_{i} + b_{i} **Inputs** -* **1**: A tensor of type T and arbitrary shape and rank. **Required.** -* **2**: A tensor of type T and arbitrary shape and rank. **Required.** +* **1**: A tensor of type *T* and arbitrary shape and rank. **Required.** +* **2**: A tensor of type *T* and arbitrary shape and rank. **Required.** **Outputs** -* **1**: The result of element-wise addition operation. A tensor of type T with shape equal to broadcasted shape of the two inputs. +* **1**: The result of element-wise addition operation. A tensor of type *T* with shape equal to broadcasted shape of the two inputs. **Types** diff --git a/docs/ops/arithmetic/Asin_1.md b/docs/ops/arithmetic/Asin_1.md index db6ea5074bf..5e7fe068ba7 100644 --- a/docs/ops/arithmetic/Asin_1.md +++ b/docs/ops/arithmetic/Asin_1.md @@ -2,7 +2,7 @@ **Versioned name**: *Asin-1* -**Category**: Arithmetic unary operation +**Category**: Arithmetic unary operation **Short description**: *Asin* performs element-wise inverse sine (arcsin) operation with given tensor. @@ -12,11 +12,11 @@ **Inputs** -* **1**: An tensor of type T. **Required.** +* **1**: An tensor of type *T*. **Required.** **Outputs** -* **1**: The result of element-wise asin operation. A tensor of type T. +* **1**: The result of element-wise asin operation. A tensor of type *T*. **Types** diff --git a/docs/ops/arithmetic/Asinh_3.md b/docs/ops/arithmetic/Asinh_3.md index 9c2e3096d89..9db15defa47 100644 --- a/docs/ops/arithmetic/Asinh_3.md +++ b/docs/ops/arithmetic/Asinh_3.md @@ -2,7 +2,7 @@ **Versioned name**: *Asinh-3* -**Category**: Arithmetic unary operation +**Category**: Arithmetic unary operation **Short description**: *Asinh* performs element-wise hyperbolic inverse sine (arcsinh) operation with given tensor. @@ -12,11 +12,11 @@ **Inputs** -* **1**: A tensor of type T. **Required.** +* **1**: A tensor of type *T*. **Required.** **Outputs** -* **1**: The result of element-wise asinh operation. A tensor of type T. +* **1**: The result of element-wise asinh operation. A tensor of type *T*. **Types** diff --git a/docs/ops/arithmetic/Atan_1.md b/docs/ops/arithmetic/Atan_1.md index 43f9a1c4213..dc4c3b6d824 100644 --- a/docs/ops/arithmetic/Atan_1.md +++ b/docs/ops/arithmetic/Atan_1.md @@ -2,7 +2,7 @@ **Versioned name**: *Atan-1* -**Category**: Arithmetic unary operation +**Category**: Arithmetic unary operation **Short description**: *Atan* performs element-wise inverse tangent (arctangent) operation with given tensor. @@ -12,11 +12,11 @@ **Inputs** -* **1**: An tensor of type T. **Required.** +* **1**: An tensor of type *T*. **Required.** **Outputs** -* **1**: The result of element-wise atan operation. A tensor of type T. +* **1**: The result of element-wise atan operation. A tensor of type *T*. **Types** diff --git a/docs/ops/arithmetic/Atanh_3.md b/docs/ops/arithmetic/Atanh_3.md index e2d5dfa36eb..10ec3ea7713 100644 --- a/docs/ops/arithmetic/Atanh_3.md +++ b/docs/ops/arithmetic/Atanh_3.md @@ -2,7 +2,7 @@ **Versioned name**: *Atanh-3* -**Category**: Arithmetic unary operation +**Category**: Arithmetic unary operation **Short description**: *Atanh* performs element-wise hyperbolic inverse tangent (arctangenth) operation with given tensor. @@ -12,11 +12,11 @@ **Inputs** -* **1**: A tensor of type T. **Required.** +* **1**: A tensor of type *T*. **Required.** **Outputs** -* **1**: The result of element-wise atanh operation. A tensor of type T. +* **1**: The result of element-wise atanh operation. A tensor of type *T*. **Types** diff --git a/docs/ops/arithmetic/Cosh_1.md b/docs/ops/arithmetic/Cosh_1.md index d90f6a182fe..08b3be9f421 100644 --- a/docs/ops/arithmetic/Cosh_1.md +++ b/docs/ops/arithmetic/Cosh_1.md @@ -2,7 +2,7 @@ **Versioned name**: *Cosh-1* -**Category**: Arithmetic unary operation +**Category**: Arithmetic unary operation **Short description**: *Cosh* performs element-wise hyperbolic cosine operation with given tensor. @@ -12,11 +12,11 @@ **Inputs** -* **1**: An tensor of type T. **Required.** +* **1**: An tensor of type *T*. **Required.** **Outputs** -* **1**: The result of element-wise cosh operation. A tensor of type T. +* **1**: The result of element-wise cosh operation. A tensor of type *T*. **Types** diff --git a/docs/ops/arithmetic/CumSum_3.md b/docs/ops/arithmetic/CumSum_3.md index 952998069a1..66897acdba8 100644 --- a/docs/ops/arithmetic/CumSum_3.md +++ b/docs/ops/arithmetic/CumSum_3.md @@ -2,10 +2,10 @@ **Versioned name**: *CumSum-3* -**Category**: Arithmetic unary operation +**Category**: Arithmetic unary operation **Short description**: *CumSum* performs cumulative summation of the input elements along the given axis. - + **Detailed description**: By default, it will do the sum inclusively meaning the first element is copied as is. Through an "exclusive" attribute, this behavior can change to exclude the first element. It can also perform summation in the opposite direction of the axis. For that, set reverse attribute to `true`. **Attributes**: @@ -32,13 +32,13 @@ **Inputs** -* **1**: An tensor of type T. **Required.** +* **1**: An tensor of type *T*. **Required.** -* **2**: Scalar axis of type T_AXIS. Negative value means counting dimensions from the back. Default value is 0. **Optional.** +* **2**: Scalar axis of type *T_AXIS*. Negative value means counting dimensions from the back. Default value is 0. **Optional.** **Outputs** -* **1**: Output tensor with cumulative sums of the input's elements. A tensor of type T of the same shape as 1st input. +* **1**: Output tensor with cumulative sums of the input's elements. A tensor of type *T* of the same shape as 1st input. **Types** diff --git a/docs/ops/arithmetic/Divide_1.md b/docs/ops/arithmetic/Divide_1.md index 694ed91ce8d..b16198a05ad 100644 --- a/docs/ops/arithmetic/Divide_1.md +++ b/docs/ops/arithmetic/Divide_1.md @@ -41,12 +41,12 @@ The result of division by zero is undefined. **Inputs** -* **1**: A tensor of type T and arbitrary shape and rank. **Required.** -* **2**: A tensor of type T and arbitrary shape and rank. **Required.** +* **1**: A tensor of type *T* and arbitrary shape and rank. **Required.** +* **2**: A tensor of type *T* and arbitrary shape and rank. **Required.** **Outputs** -* **1**: The result of element-wise division operation. A tensor of type T with shape equal to broadcasted shape of the two inputs. +* **1**: The result of element-wise division operation. A tensor of type *T* with shape equal to broadcasted shape of the two inputs. **Types** diff --git a/docs/ops/arithmetic/Erf_1.md b/docs/ops/arithmetic/Erf_1.md index a672ab6431d..9dcb8038114 100644 --- a/docs/ops/arithmetic/Erf_1.md +++ b/docs/ops/arithmetic/Erf_1.md @@ -19,11 +19,11 @@ erf(x) = \pi^{-1} \int_{-x}^{x} e^{-t^2} dt **Inputs** -* **1**: A tensor of type T. **Required.** +* **1**: A tensor of type *T*. **Required.** **Outputs** -* **1**: The result of element-wise operation. A tensor of type T. +* **1**: The result of element-wise operation. A tensor of type *T*. **Types** diff --git a/docs/ops/arithmetic/FloorMod_1.md b/docs/ops/arithmetic/FloorMod_1.md index 042ffb7f428..90483e612e9 100644 --- a/docs/ops/arithmetic/FloorMod_1.md +++ b/docs/ops/arithmetic/FloorMod_1.md @@ -4,14 +4,14 @@ **Category**: Arithmetic binary operation -**Short description**: *FloorMod* performs an element-wise floor modulo operation with two given tensors applying broadcasting rule specified in the *auto_broadcast* attribute. +**Short description**: *FloorMod* performs an element-wise floor modulo operation with two given tensors applying broadcasting rule specified in the *auto_broadcast* attribute. **Detailed description** As a first step input tensors *a* and *b* are broadcasted if their shapes differ. Broadcasting is performed according to `auto_broadcast` attribute specification. As a second step *FloorMod* operation is computed element-wise on the input tensors *a* and *b* according to the formula below: \f[ o_{i} = a_{i} % b_{i} -\f] +\f] *FloorMod* operation computes a reminder of a floored division. It is the same behaviour like in Python programming language: `floor(x / y) * y + floor_mod(x, y) = x`. The sign of the result is equal to a sign of a divisor. The result of division by zero is undefined. @@ -29,12 +29,12 @@ o_{i} = a_{i} % b_{i} **Inputs** -* **1**: A tensor of type T and arbitrary shape. Required. -* **2**: A tensor of type T and arbitrary shape. Required. +* **1**: A tensor of type *T* and arbitrary shape. Required. +* **2**: A tensor of type *T* and arbitrary shape. Required. **Outputs** -* **1**: The result of element-wise floor modulo operation. A tensor of type T with shape equal to broadcasted shape of two inputs. +* **1**: The result of element-wise floor modulo operation. A tensor of type *T* with shape equal to broadcasted shape of two inputs. **Types** diff --git a/docs/ops/arithmetic/Log_1.md b/docs/ops/arithmetic/Log_1.md index f1314919821..b8074316e79 100644 --- a/docs/ops/arithmetic/Log_1.md +++ b/docs/ops/arithmetic/Log_1.md @@ -2,7 +2,7 @@ **Versioned name**: *Log-1* -**Category**: Arithmetic unary operation +**Category**: Arithmetic unary operation **Short description**: *Log* performs element-wise natural logarithm operation with given tensor. @@ -18,11 +18,11 @@ a_{i} = log(a_{i}) **Inputs** -* **1**: An tensor of type T and arbitrary shape. **Required.** +* **1**: An tensor of type *T* and arbitrary shape. **Required.** **Outputs** -* **1**: The result of element-wise log operation. A tensor of type T and the same shape as input. +* **1**: The result of element-wise log operation. A tensor of type *T* and the same shape as input. **Types** @@ -47,4 +47,4 @@ a_{i} = log(a_{i}) -``` \ No newline at end of file +``` diff --git a/docs/ops/arithmetic/Maximum_1.md b/docs/ops/arithmetic/Maximum_1.md index b456761d9a3..478febfbc9e 100644 --- a/docs/ops/arithmetic/Maximum_1.md +++ b/docs/ops/arithmetic/Maximum_1.md @@ -13,7 +13,7 @@ After broadcasting *Maximum* does the following with the input tensors *a* and * \f[ o_{i} = max(a_{i}, b_{i}) -\f] +\f] **Attributes**: @@ -29,12 +29,12 @@ o_{i} = max(a_{i}, b_{i}) **Inputs** -* **1**: A tensor of type T and arbitrary shape. Required. -* **2**: A tensor of type T and arbitrary shape. Required. +* **1**: A tensor of type *T* and arbitrary shape. Required. +* **2**: A tensor of type *T* and arbitrary shape. Required. **Outputs** -* **1**: The result of element-wise maximum operation. A tensor of type T with shape equal to broadcasted shape of two inputs. +* **1**: The result of element-wise maximum operation. A tensor of type *T* with shape equal to broadcasted shape of two inputs. **Types** @@ -92,4 +92,4 @@ o_{i} = max(a_{i}, b_{i}) -``` \ No newline at end of file +``` diff --git a/docs/ops/arithmetic/Minimum_1.md b/docs/ops/arithmetic/Minimum_1.md index e9d5b2c47c9..61c24dc8c78 100644 --- a/docs/ops/arithmetic/Minimum_1.md +++ b/docs/ops/arithmetic/Minimum_1.md @@ -11,7 +11,7 @@ As a first step input tensors *a* and *b* are broadcasted if their shapes differ \f[ o_{i} = min(a_{i}, b_{i}) -\f] +\f] **Attributes**: @@ -27,12 +27,12 @@ o_{i} = min(a_{i}, b_{i}) **Inputs** -* **1**: A tensor of type T and arbitrary shape. Required. -* **2**: A tensor of type T and arbitrary shape. Required. +* **1**: A tensor of type *T* and arbitrary shape. Required. +* **2**: A tensor of type *T* and arbitrary shape. Required. **Outputs** -* **1**: The result of element-wise minimum operation. A tensor of type T with shape equal to broadcasted shape of two inputs. +* **1**: The result of element-wise minimum operation. A tensor of type *T* with shape equal to broadcasted shape of two inputs. **Types** @@ -90,4 +90,4 @@ o_{i} = min(a_{i}, b_{i}) -``` \ No newline at end of file +``` diff --git a/docs/ops/arithmetic/Mod_1.md b/docs/ops/arithmetic/Mod_1.md index f55a1efa05b..03aa430eae6 100644 --- a/docs/ops/arithmetic/Mod_1.md +++ b/docs/ops/arithmetic/Mod_1.md @@ -4,7 +4,7 @@ **Category**: Arithmetic binary operation -**Short description**: *Mod* performs an element-wise modulo operation with two given tensors applying broadcasting rule specified in the *auto_broadcast* attribute. +**Short description**: *Mod* performs an element-wise modulo operation with two given tensors applying broadcasting rule specified in the *auto_broadcast* attribute. **Detailed description** As a first step input tensors *a* and *b* are broadcasted if their shapes differ. Broadcasting is performed according to `auto_broadcast` attribute specification. As a second step *Mod* operation is computed element-wise on the input tensors *a* and *b* according to the formula below: @@ -30,12 +30,12 @@ o_{i} = a_{i} % b_{i} **Inputs** -* **1**: A tensor of type T and arbitrary shape. Required. -* **2**: A tensor of type T and arbitrary shape. Required. +* **1**: A tensor of type *T* and arbitrary shape. Required. +* **2**: A tensor of type *T* and arbitrary shape. Required. **Outputs** -* **1**: The result of element-wise modulo operation. A tensor of type T with shape equal to broadcasted shape of two inputs. +* **1**: The result of element-wise modulo operation. A tensor of type *T* with shape equal to broadcasted shape of two inputs. **Types** @@ -93,4 +93,4 @@ o_{i} = a_{i} % b_{i} -``` \ No newline at end of file +``` diff --git a/docs/ops/arithmetic/Multiply_1.md b/docs/ops/arithmetic/Multiply_1.md index eff8b30d568..6b8273922f5 100644 --- a/docs/ops/arithmetic/Multiply_1.md +++ b/docs/ops/arithmetic/Multiply_1.md @@ -29,12 +29,12 @@ o_{i} = a_{i} * b_{i} **Inputs** -* **1**: A tensor of type T and arbitrary shape and rank. **Required.** -* **2**: A tensor of type T and arbitrary shape and rank. **Required.** +* **1**: A tensor of type *T* and arbitrary shape and rank. **Required.** +* **2**: A tensor of type *T* and arbitrary shape and rank. **Required.** **Outputs** -* **1**: The result of element-wise multiplication operation. A tensor of type T with shape equal to broadcasted shape of the two inputs. +* **1**: The result of element-wise multiplication operation. A tensor of type *T* with shape equal to broadcasted shape of the two inputs. **Types** @@ -93,4 +93,4 @@ o_{i} = a_{i} * b_{i} -``` \ No newline at end of file +``` diff --git a/docs/ops/arithmetic/Power_1.md b/docs/ops/arithmetic/Power_1.md index 81be1dd80ea..4e585cd5bfb 100644 --- a/docs/ops/arithmetic/Power_1.md +++ b/docs/ops/arithmetic/Power_1.md @@ -11,7 +11,7 @@ As a first step input tensors *a* and *b* are broadcasted if their shapes differ \f[ o_{i} = {a_{i} ^ b_{i}} -\f] +\f] **Attributes**: @@ -27,12 +27,12 @@ o_{i} = {a_{i} ^ b_{i}} **Inputs** -* **1**: A tensor of type T and arbitrary shape. Required. -* **2**: A tensor of type T and arbitrary shape. Required. +* **1**: A tensor of type *T* and arbitrary shape. Required. +* **2**: A tensor of type *T* and arbitrary shape. Required. **Outputs** -* **1**: The result of element-wise power operation. A tensor of type T with shape equal to broadcasted shape of two inputs. +* **1**: The result of element-wise power operation. A tensor of type *T* with shape equal to broadcasted shape of two inputs. **Types** @@ -91,4 +91,4 @@ o_{i} = {a_{i} ^ b_{i}} -``` \ No newline at end of file +``` diff --git a/docs/ops/arithmetic/Sign_1.md b/docs/ops/arithmetic/Sign_1.md index 8a42258e0da..e68cc51f97f 100644 --- a/docs/ops/arithmetic/Sign_1.md +++ b/docs/ops/arithmetic/Sign_1.md @@ -2,7 +2,7 @@ **Versioned name**: *Sign-1* -**Category**: Arithmetic unary operation +**Category**: Arithmetic unary operation **Short description**: *Sign* performs element-wise sign operation with given tensor. @@ -12,11 +12,11 @@ **Inputs** -* **1**: An tensor of type T. **Required.** +* **1**: An tensor of type *T*. **Required.** **Outputs** -* **1**: The result of element-wise sign operation. A tensor of type T with mapped elements of the input tensor to -1 (if it is negative), 0 (if it is zero), or 1 (if it is positive). +* **1**: The result of element-wise sign operation. A tensor of type *T* with mapped elements of the input tensor to -1 (if it is negative), 0 (if it is zero), or 1 (if it is positive). **Types** @@ -47,4 +47,4 @@ a_{i} = sign(a_{i}) -``` \ No newline at end of file +``` diff --git a/docs/ops/arithmetic/Sin_1.md b/docs/ops/arithmetic/Sin_1.md index 3f785e4442c..24b43d2e39d 100644 --- a/docs/ops/arithmetic/Sin_1.md +++ b/docs/ops/arithmetic/Sin_1.md @@ -2,7 +2,7 @@ **Versioned name**: *Sin-1* -**Category**: Arithmetic unary operation +**Category**: Arithmetic unary operation **Short description**: *Sin* performs element-wise sine operation with given tensor. @@ -19,11 +19,11 @@ a - value representing angle in radians. **Inputs** -* **1**: An tensor of type T and arbitrary rank. **Required.** +* **1**: An tensor of type *T* and arbitrary rank. **Required.** **Outputs** -* **1**: The result of element-wise sin operation. A tensor of type T. +* **1**: The result of element-wise sin operation. A tensor of type *T*. **Types** diff --git a/docs/ops/arithmetic/Sinh_1.md b/docs/ops/arithmetic/Sinh_1.md index 7bac2216a42..94a724acfe9 100644 --- a/docs/ops/arithmetic/Sinh_1.md +++ b/docs/ops/arithmetic/Sinh_1.md @@ -2,7 +2,7 @@ **Versioned name**: *Sinh-1* -**Category**: Arithmetic unary operation +**Category**: Arithmetic unary operation **Short description**: *Sinh* performs element-wise hyperbolic sine (sinh) operation with given tensor. @@ -12,7 +12,7 @@ **Inputs** -* **1**: An tensor of type T. **Required.** +* **1**: An tensor of type *T*. **Required.** **Outputs** diff --git a/docs/ops/arithmetic/SquaredDifference_1.md b/docs/ops/arithmetic/SquaredDifference_1.md index 5e5b89b5727..20e22e3b4de 100644 --- a/docs/ops/arithmetic/SquaredDifference_1.md +++ b/docs/ops/arithmetic/SquaredDifference_1.md @@ -11,7 +11,7 @@ As a first step input tensors *a* and *b* are broadcasted if their shapes differ \f[ o_{i} = (a_{i} - b_{i})^2 -\f] +\f] **Attributes**: @@ -27,12 +27,12 @@ o_{i} = (a_{i} - b_{i})^2 **Inputs** -* **1**: A tensor of type T and arbitrary shape. Required. -* **2**: A tensor of type T and arbitrary shape. Required. +* **1**: A tensor of type *T* and arbitrary shape. Required. +* **2**: A tensor of type *T* and arbitrary shape. Required. **Outputs** -* **1**: The result of element-wise subtract and square the result operation. A tensor of type T with shape equal to broadcasted shape of two inputs. +* **1**: The result of element-wise subtract and square the result operation. A tensor of type *T* with shape equal to broadcasted shape of two inputs. **Types** @@ -89,4 +89,4 @@ o_{i} = (a_{i} - b_{i})^2 -``` \ No newline at end of file +``` diff --git a/docs/ops/arithmetic/Subtract_1.md b/docs/ops/arithmetic/Subtract_1.md index 347fbbd0b34..0720bd3415c 100644 --- a/docs/ops/arithmetic/Subtract_1.md +++ b/docs/ops/arithmetic/Subtract_1.md @@ -29,12 +29,12 @@ o_{i} = a_{i} - b_{i} **Inputs** -* **1**: A tensor of type T and arbitrary shape and rank. **Required.** -* **2**: A tensor of type T and arbitrary shape and rank. **Required.** +* **1**: A tensor of type *T* and arbitrary shape and rank. **Required.** +* **2**: A tensor of type *T* and arbitrary shape and rank. **Required.** **Outputs** -* **1**: The result of element-wise subtraction operation. A tensor of type T with shape equal to broadcasted shape of the two inputs. +* **1**: The result of element-wise subtraction operation. A tensor of type *T* with shape equal to broadcasted shape of the two inputs. **Types** @@ -91,4 +91,4 @@ o_{i} = a_{i} - b_{i} -``` \ No newline at end of file +``` diff --git a/docs/ops/arithmetic/Tan_1.md b/docs/ops/arithmetic/Tan_1.md index 8a4640262cc..6ea7d1e9a6b 100644 --- a/docs/ops/arithmetic/Tan_1.md +++ b/docs/ops/arithmetic/Tan_1.md @@ -2,7 +2,7 @@ **Versioned name**: *Tan-1* -**Category**: Arithmetic unary operation +**Category**: Arithmetic unary operation **Short description**: *Tan* performs element-wise tangent operation with given tensor. @@ -12,11 +12,11 @@ **Inputs** -* **1**: An tensor of type T. **Required.** +* **1**: An tensor of type *T*. **Required.** **Outputs** -* **1**: The result of element-wise tan operation. A tensor of type T. +* **1**: The result of element-wise tan operation. A tensor of type *T*. **Types** @@ -48,4 +48,3 @@ a_{i} = tan(a_{i}) ``` - diff --git a/docs/ops/comparison/Equal_1.md b/docs/ops/comparison/Equal_1.md index 22d75588da6..f72d3302665 100644 --- a/docs/ops/comparison/Equal_1.md +++ b/docs/ops/comparison/Equal_1.md @@ -20,8 +20,8 @@ **Inputs** -* **1**: A tensor of type T. **Required.** -* **2**: A tensor of type T. **Required.** +* **1**: A tensor of type *T*. **Required.** +* **2**: A tensor of type *T*. **Required.** **Outputs** @@ -40,7 +40,7 @@ After broadcasting *Equal* does the following with the input tensors *a* and *b* o_{i} = a_{i} == b_{i} \f] -**Examples** +**Examples** *Example 1* diff --git a/docs/ops/comparison/GreaterEqual_1.md b/docs/ops/comparison/GreaterEqual_1.md index 8c0b22d6c32..5acf4cbe6d6 100644 --- a/docs/ops/comparison/GreaterEqual_1.md +++ b/docs/ops/comparison/GreaterEqual_1.md @@ -20,8 +20,8 @@ **Inputs** -* **1**: A tensor of type T. **Required.** -* **2**: A tensor of type T. **Required.** +* **1**: A tensor of type *T*. **Required.** +* **2**: A tensor of type *T*. **Required.** **Outputs** diff --git a/docs/ops/comparison/Greater_1.md b/docs/ops/comparison/Greater_1.md index 0e286b51762..928eca83878 100644 --- a/docs/ops/comparison/Greater_1.md +++ b/docs/ops/comparison/Greater_1.md @@ -20,8 +20,8 @@ **Inputs** -* **1**: A tensor of type T. **Required.** -* **2**: A tensor of type T. **Required.** +* **1**: A tensor of type *T*. **Required.** +* **2**: A tensor of type *T*. **Required.** **Outputs** diff --git a/docs/ops/comparison/LessEqual_1.md b/docs/ops/comparison/LessEqual_1.md index 34d66e6592a..4144095bed4 100644 --- a/docs/ops/comparison/LessEqual_1.md +++ b/docs/ops/comparison/LessEqual_1.md @@ -20,8 +20,8 @@ **Inputs** -* **1**: A tensor of type T. **Required.** -* **2**: A tensor of type T. **Required.** +* **1**: A tensor of type *T*. **Required.** +* **2**: A tensor of type *T*. **Required.** **Outputs** @@ -90,4 +90,4 @@ o_{i} = a_{i} <= b_{i} -``` \ No newline at end of file +``` diff --git a/docs/ops/comparison/Less_1.md b/docs/ops/comparison/Less_1.md index 90113253fca..79a154a6c57 100644 --- a/docs/ops/comparison/Less_1.md +++ b/docs/ops/comparison/Less_1.md @@ -20,8 +20,8 @@ **Inputs** -* **1**: A tensor of type T. **Required.** -* **2**: A tensor of type T. **Required.** +* **1**: A tensor of type *T*. **Required.** +* **2**: A tensor of type *T*. **Required.** **Outputs** @@ -90,4 +90,4 @@ o_{i} = a_{i} < b_{i} -``` \ No newline at end of file +``` diff --git a/docs/ops/comparison/NotEqual_1.md b/docs/ops/comparison/NotEqual_1.md index c55b14be61a..456aeb7a785 100644 --- a/docs/ops/comparison/NotEqual_1.md +++ b/docs/ops/comparison/NotEqual_1.md @@ -20,8 +20,8 @@ **Inputs** -* **1**: A tensor of type T. **Required.** -* **2**: A tensor of type T. **Required.** +* **1**: A tensor of type *T*. **Required.** +* **2**: A tensor of type *T*. **Required.** **Outputs** @@ -40,7 +40,7 @@ After broadcasting *NotEqual* does the following with the input tensors *a* and o_{i} = a_{i} != b_{i} \f] -**Examples** +**Examples** *Example 1* @@ -90,4 +90,4 @@ o_{i} = a_{i} != b_{i} -``` \ No newline at end of file +``` diff --git a/docs/ops/detection/RegionYolo_1.md b/docs/ops/detection/RegionYolo_1.md index ebcc79c2377..68826178b47 100644 --- a/docs/ops/detection/RegionYolo_1.md +++ b/docs/ops/detection/RegionYolo_1.md @@ -6,7 +6,7 @@ **Short description**: *RegionYolo* computes the coordinates of regions with probability for each class. -**Detailed description**: This operation is directly mapped to the [YOLO9000: Better, Faster, Stronger](https://arxiv.org/pdf/1612.08242.pdf) paper. +**Detailed description**: This operation is directly mapped to the [YOLO9000: Better, Faster, Stronger](https://arxiv.org/pdf/1612.08242.pdf) paper. **Attributes**: @@ -78,13 +78,13 @@ **Inputs**: -* **1**: `data` - 4D tensor of type `T` and shape `[N, C, H, W]`. **Required.** +* **1**: `data` - 4D tensor of type *T* and shape `[N, C, H, W]`. **Required.** **Outputs**: -* **1**: tensor of type `T` and rank 4 or less that codes detected regions. Refer to the [YOLO9000: Better, Faster, Stronger](https://arxiv.org/pdf/1612.08242.pdf) paper to decode the output as boxes. `anchors` should be used to decode real box coordinates. If `do_softmax` is set to `0`, then the output shape is `[N, (classes + coords + 1) * len(mask), H, W]`. If `do_softmax` is set to `1`, then output shape is partially flattened and defined in the following way: +* **1**: tensor of type *T* and rank 4 or less that codes detected regions. Refer to the [YOLO9000: Better, Faster, Stronger](https://arxiv.org/pdf/1612.08242.pdf) paper to decode the output as boxes. `anchors` should be used to decode real box coordinates. If `do_softmax` is set to `0`, then the output shape is `[N, (classes + coords + 1) * len(mask), H, W]`. If `do_softmax` is set to `1`, then output shape is partially flattened and defined in the following way: - `flat_dim = data.shape[axis] * data.shape[axis+1] * ... * data.shape[end_axis]` + `flat_dim = data.shape[axis] * data.shape[axis+1] * ... * data.shape[end_axis]` `output.shape = [data.shape[0], ..., data.shape[axis-1], flat_dim, data.shape[end_axis + 1], ...]` **Types** @@ -133,4 +133,4 @@ -``` \ No newline at end of file +``` diff --git a/docs/ops/generation/Range_1.md b/docs/ops/generation/Range_1.md index 06a16cc1899..3e2f1d37eef 100644 --- a/docs/ops/generation/Range_1.md +++ b/docs/ops/generation/Range_1.md @@ -12,13 +12,13 @@ No attributes available. **Inputs**: -* **1**: "start" - A scalar of type T. **Required.** -* **2**: "stop" - A scalar of type T. **Required.** -* **3**: "step" - A scalar of type T. **Required.** +* **1**: "start" - A scalar of type *T*. **Required.** +* **2**: "stop" - A scalar of type *T*. **Required.** +* **3**: "step" - A scalar of type *T*. **Required.** **Outputs**: -* **1**: A tensor of type T. +* **1**: A tensor of type *T*. **Types** @@ -87,4 +87,3 @@ val[i]=start+i*step ``` - diff --git a/docs/ops/generation/Range_4.md b/docs/ops/generation/Range_4.md index 3314ca4246f..037784b8576 100644 --- a/docs/ops/generation/Range_4.md +++ b/docs/ops/generation/Range_4.md @@ -18,9 +18,9 @@ **Inputs**: -* **1**: "start" - A scalar of type T1. **Required.** -* **2**: "stop" - A scalar of type T2. **Required.** -* **3**: "step" - A scalar of type T3. If `step` is equal to zero after casting to `output_type`, behavior is undefined. **Required.** +* **1**: "start" - A scalar of type *T1*. **Required.** +* **2**: "stop" - A scalar of type *T2*. **Required.** +* **3**: "step" - A scalar of type *T3*. If `step` is equal to zero after casting to `output_type`, behavior is undefined. **Required.** **Outputs**: @@ -124,4 +124,3 @@ This is aligned with PyTorch's operation `torch.arange`, to align with tensorflo ``` - diff --git a/docs/ops/image/Interpolate_4.md b/docs/ops/image/Interpolate_4.md index 773802dab93..b0c9c846f53 100644 --- a/docs/ops/image/Interpolate_4.md +++ b/docs/ops/image/Interpolate_4.md @@ -89,13 +89,13 @@ **Inputs** -* **1**: `data` - tensor of type `T` with data for interpolation. **Required.** +* **1**: `data` - tensor of type *T* with data for interpolation. **Required.** -* **2**: `sizes` - 1D tensor of type `T_SIZE` describing output shape for spatial axes. Number of elements matches the number of indices in `axes` input, the order matches as well. **Required.** +* **2**: `sizes` - 1D tensor of type *T_SIZE* describing output shape for spatial axes. Number of elements matches the number of indices in `axes` input, the order matches as well. **Required.** -* **3**: `scales` - 1D tensor of type `T_SCALES` describing scales for spatial axes. Number and order of elements match the number and order of indices in `axes` input. **Required.** +* **3**: `scales` - 1D tensor of type *T_SCALES* describing scales for spatial axes. Number and order of elements match the number and order of indices in `axes` input. **Required.** -* **4**: `axes` - 1D tensor of type `T_AXES` specifying dimension indices where interpolation is applied, and `axes` is any unordered list of indices of different dimensions of input tensor, e.g. `[0, 4]`, `[4, 0]`, `[4, 2, 1]`, `[1, 2, 3]`. These indices should be non-negative integers from `0` to `rank(data) - 1` inclusively. Other dimensions do not change. The order of elements in `axes` attribute matters, and mapped directly to elements in the 2nd input `sizes`. **Optional** with default value `[0,...,rank(data) - 1]`. +* **4**: `axes` - 1D tensor of type *T_AXES* specifying dimension indices where interpolation is applied, and `axes` is any unordered list of indices of different dimensions of input tensor, e.g. `[0, 4]`, `[4, 0]`, `[4, 2, 1]`, `[1, 2, 3]`. These indices should be non-negative integers from `0` to `rank(data) - 1` inclusively. Other dimensions do not change. The order of elements in `axes` attribute matters, and mapped directly to elements in the 2nd input `sizes`. **Optional** with default value `[0,...,rank(data) - 1]`. **Outputs** diff --git a/docs/ops/infrastructure/Result_1.md b/docs/ops/infrastructure/Result_1.md index f2afa552fdd..b34d4a4436c 100644 --- a/docs/ops/infrastructure/Result_1.md +++ b/docs/ops/infrastructure/Result_1.md @@ -6,13 +6,13 @@ **Short description**: *Result* layer specifies output of the model. -**Attributes**: +**Attributes**: No attributes available. **Inputs** -* **1**: A tensor of type T. **Required.** +* **1**: A tensor of type *T*. **Required.** **Types** @@ -31,4 +31,4 @@ -``` \ No newline at end of file +``` diff --git a/docs/ops/logical/LogicalAnd_1.md b/docs/ops/logical/LogicalAnd_1.md index 54a04881fbf..9312f3871a6 100644 --- a/docs/ops/logical/LogicalAnd_1.md +++ b/docs/ops/logical/LogicalAnd_1.md @@ -20,8 +20,8 @@ **Inputs** -* **1**: A tensor of type T. **Required**. -* **2**: A tensor of type T. **Required**. +* **1**: A tensor of type *T*. **Required**. +* **2**: A tensor of type *T*. **Required**. **Outputs** @@ -90,4 +90,4 @@ o_{i} = a_{i} and b_{i} -``` \ No newline at end of file +``` diff --git a/docs/ops/logical/LogicalNot_1.md b/docs/ops/logical/LogicalNot_1.md index f31eab7ddbc..9dd9132383f 100644 --- a/docs/ops/logical/LogicalNot_1.md +++ b/docs/ops/logical/LogicalNot_1.md @@ -2,7 +2,7 @@ **Versioned name**: *LogicalNot-1* -**Category**: Logical unary operation +**Category**: Logical unary operation **Short description**: *LogicalNot* performs element-wise logical negation operation with given tensor. @@ -12,11 +12,11 @@ **Inputs** -* **1**: An tensor of type T. **Required.** +* **1**: An tensor of type *T*. **Required.** **Outputs** -* **1**: The result of element-wise logical negation operation. A tensor of type T. +* **1**: The result of element-wise logical negation operation. A tensor of type *T*. **Types** @@ -47,4 +47,4 @@ a_{i} = not(a_{i}) -``` \ No newline at end of file +``` diff --git a/docs/ops/logical/LogicalOr_1.md b/docs/ops/logical/LogicalOr_1.md index b900f9c151a..0e11560cc4b 100644 --- a/docs/ops/logical/LogicalOr_1.md +++ b/docs/ops/logical/LogicalOr_1.md @@ -20,8 +20,8 @@ **Inputs** -* **1**: A tensor of type T. **Required**. -* **2**: A tensor of type T. **Required**. +* **1**: A tensor of type *T*. **Required**. +* **2**: A tensor of type *T*. **Required**. **Outputs** @@ -90,4 +90,4 @@ o_{i} = a_{i} or b_{i} -``` \ No newline at end of file +``` diff --git a/docs/ops/logical/LogicalXor_1.md b/docs/ops/logical/LogicalXor_1.md index bc0b197a2b3..d6b1e53fb8d 100644 --- a/docs/ops/logical/LogicalXor_1.md +++ b/docs/ops/logical/LogicalXor_1.md @@ -20,12 +20,12 @@ **Inputs** -* **1**: A tensor of type T. **Required**. -* **2**: A tensor of type T. **Required**. +* **1**: A tensor of type *T*. **Required**. +* **2**: A tensor of type *T*. **Required**. **Outputs** -* **1**: The result of element-wise logical XOR operation. A tensor of type T. +* **1**: The result of element-wise logical XOR operation. A tensor of type *T*. **Types** @@ -90,4 +90,4 @@ o_{i} = a_{i} xor b_{i} -``` \ No newline at end of file +``` diff --git a/docs/ops/movement/DepthToSpace_1.md b/docs/ops/movement/DepthToSpace_1.md index a5fa1d23ee3..3fcb223c47f 100644 --- a/docs/ops/movement/DepthToSpace_1.md +++ b/docs/ops/movement/DepthToSpace_1.md @@ -48,11 +48,11 @@ If `mode = depth_first`: **Inputs** -* **1**: `data` - input tensor of type T with rank >= 3. **Required**. +* **1**: `data` - input tensor of type *T* with rank >= 3. **Required**. **Outputs** -* **1**: permuted tensor of type T and shape `[N, C / block_size ^ K, D1 * block_size, D2 * block_size, ..., DK * block_size]`. +* **1**: permuted tensor of type *T* and shape `[N, C / block_size ^ K, D1 * block_size, D2 * block_size, ..., DK * block_size]`. **Types** diff --git a/docs/ops/movement/GatherTree_1.md b/docs/ops/movement/GatherTree_1.md index 0b81bea248e..de7d90eae20 100644 --- a/docs/ops/movement/GatherTree_1.md +++ b/docs/ops/movement/GatherTree_1.md @@ -21,13 +21,13 @@ for batch in range(BATCH_SIZE): parent = parent_idx[max_sequence_in_beam - 1, batch, beam] final_idx[max_sequence_in_beam - 1, batch, beam] = step_idx[max_sequence_in_beam - 1, batch, beam] - + for level in reversed(range(max_sequence_in_beam - 1)): final_idx[level, batch, beam] = step_idx[level, batch, parent] parent = parent_idx[level, batch, parent] - # For a given beam, past the time step containing the first decoded end_token + # For a given beam, past the time step containing the first decoded end_token # all values are filled in with end_token. finished = False for time in range(max_sequence_in_beam): @@ -43,18 +43,18 @@ Element data types for all input tensors should match each other. **Inputs** -* **1**: `step_ids` -- a tensor of shape `[MAX_TIME, BATCH_SIZE, BEAM_WIDTH]` of type `T` with indices from per each step. Required. +* **1**: `step_ids` -- a tensor of shape `[MAX_TIME, BATCH_SIZE, BEAM_WIDTH]` of type *T* with indices from per each step. Required. -* **2**: `parent_idx` -- a tensor of shape `[MAX_TIME, BATCH_SIZE, BEAM_WIDTH]` of type `T` with parent beam indices. Required. +* **2**: `parent_idx` -- a tensor of shape `[MAX_TIME, BATCH_SIZE, BEAM_WIDTH]` of type *T* with parent beam indices. Required. -* **3**: `max_seq_len` -- a tensor of shape `[BATCH_SIZE]` of type `T` with maximum lengths for each sequence in the batch. Required. +* **3**: `max_seq_len` -- a tensor of shape `[BATCH_SIZE]` of type *T* with maximum lengths for each sequence in the batch. Required. -* **4**: `end_token` -- a scalar tensor of type `T` with value of the end marker in a sequence. Required. +* **4**: `end_token` -- a scalar tensor of type *T* with value of the end marker in a sequence. Required. **Outputs** -* **1**: `final_idx` -- a tensor of shape `[MAX_TIME, BATCH_SIZE, BEAM_WIDTH]` of type `T`. +* **1**: `final_idx` -- a tensor of shape `[MAX_TIME, BATCH_SIZE, BEAM_WIDTH]` of type *T*. **Types** diff --git a/docs/ops/movement/Split_1.md b/docs/ops/movement/Split_1.md index 1dfa5f10c6f..6664393d7e0 100644 --- a/docs/ops/movement/Split_1.md +++ b/docs/ops/movement/Split_1.md @@ -29,13 +29,13 @@ Where D is the rank of input tensor `data`. The axis being split must be evenly **Inputs** -* **1**: `data`. A tensor of type `T` and arbitrary shape. **Required.** -* **2**: `axis`. Axis along `data` to split. A scalar of type `T_AXIS` within the range `[-rank(data), rank(data) - 1]`. Negative values address dimensions from the end. **Required.** +* **1**: `data`. A tensor of type *T* and arbitrary shape. **Required.** +* **2**: `axis`. Axis along `data` to split. A scalar of type *T_AXIS* within the range `[-rank(data), rank(data) - 1]`. Negative values address dimensions from the end. **Required.** * **Note**: The dimension of input tensor `data` shape along `axis` must be evenly divisible by *num_splits* attribute. **Outputs** -* **Multiple outputs**: Tensors of type `T`. The i-th output has the same shape as `data` input tensor except for dimension along `axis` which is `data.shape[axis]/num_splits`. +* **Multiple outputs**: Tensors of type *T*. The i-th output has the same shape as `data` input tensor except for dimension along `axis` which is `data.shape[axis]/num_splits`. **Types** @@ -78,4 +78,4 @@ Where D is the rank of input tensor `data`. The axis being split must be evenly -``` \ No newline at end of file +``` diff --git a/docs/ops/movement/StridedSlice_1.md b/docs/ops/movement/StridedSlice_1.md index 41742e20652..da3a580e0ec 100644 --- a/docs/ops/movement/StridedSlice_1.md +++ b/docs/ops/movement/StridedSlice_1.md @@ -4,7 +4,7 @@ **Category**: Data movement operation -**Short description**: *StridedSlice* extracts a strided slice of a tensor. +**Short description**: *StridedSlice* extracts a strided slice of a tensor. **Attributes** @@ -50,22 +50,22 @@ **Inputs**: -* **1**: `data` - input tensor to be sliced of type `T` and arbitrary shape. **Required.** +* **1**: `data` - input tensor to be sliced of type *T* and arbitrary shape. **Required.** -* **2**: `begin` - 1D tensor of type `T_IND` with begin indexes for input tensor slicing. **Required.** +* **2**: `begin` - 1D tensor of type *T_IND* with begin indexes for input tensor slicing. **Required.** Out-of-bounds values are silently clamped. If `begin_mask[i]` is `1`, the value of `begin[i]` is ignored and the range of the appropriate dimension starts from `0`. Negative values mean indexing starts from the end. For example, if `data=[1,2,3]`, `begin[0]=-1` means `begin[0]=3`. -* **3**: `end` - 1D tensor of type `T_IND` with end indexes for input tensor slicing. **Required.** +* **3**: `end` - 1D tensor of type *T_IND* with end indexes for input tensor slicing. **Required.** Out-of-bounds values will be silently clamped. If `end_mask[i]` is `1`, the value of `end[i]` is ignored and the full range of the appropriate dimension is used instead. Negative values mean indexing starts from the end. For example, if `data=[1,2,3]`, `end[0]=-1` means `end[0]=3`. -* **4**: `stride` - 1D tensor of type `T_IND` with strides. **Optional.** +* **4**: `stride` - 1D tensor of type *T_IND* with strides. **Optional.** **Types** * *T*: any supported type. * *T_IND*: any supported integer type. -**Example** -Example of `begin_mask` & `end_mask` usage. +**Example** +Example of `begin_mask` & `end_mask` usage. ```xml @@ -86,7 +86,7 @@ Example of `begin_mask` & `end_mask` usage. - + 1 3 2 diff --git a/docs/ops/movement/Tile_1.md b/docs/ops/movement/Tile_1.md index d71f009f40e..b0d17a3fb47 100644 --- a/docs/ops/movement/Tile_1.md +++ b/docs/ops/movement/Tile_1.md @@ -4,7 +4,7 @@ **Category**: Data movement -**Short description**: *Tile* operation repeats an input tensor *"data"* the number of times given by *"repeats"* input tensor along each dimension. +**Short description**: *Tile* operation repeats an input tensor *"data"* the number of times given by *"repeats"* input tensor along each dimension. * If number of elements in *"repeats"* is more than shape of *"data"*, then *"data"* will be promoted to "*repeats*" by prepending new axes, e.g. let's shape of *"data"* is equal to (2, 3) and *"repeats"* is equal to [2, 2, 2], then shape of *"data"* will be promoted to (1, 2, 3) and result shape will be (2, 4, 6). * If number of elements in *"repeats"* is less than shape of *"data"*, then *"repeats"* will be promoted to "*data*" by prepending 1's to it, e.g. let's shape of *"data"* is equal to (4, 2, 3) and *"repeats"* is equal to [2, 2], then *"repeats"* will be promoted to [1, 2, 2] and result shape will be (4, 4, 6) @@ -14,12 +14,12 @@ No attributes available. **Inputs**: -* **1**: "data" - an input tensor to be padded. A tensor of type T1. **Required.** -* **2**: "repeats" - a per-dimension replication factor. For example, *repeats* equal to 88 means that the output tensor gets 88 copies of data from the specified axis. A tensor of type T2. **Required.** +* **1**: "data" - an input tensor to be padded. A tensor of type *T1*. **Required.** +* **2**: "repeats" - a per-dimension replication factor. For example, *repeats* equal to 88 means that the output tensor gets 88 copies of data from the specified axis. A tensor of type *T2*. **Required.** **Outputs**: -* **1**: The count of dimensions in result shape will be equal to the maximum from count of dimensions in "data" shape and number of elements in "repeats". A tensor with type matching 1st tensor. +* **1**: The count of dimensions in result shape will be equal to the maximum from count of dimensions in "data" shape and number of elements in "repeats". A tensor with type matching 1st tensor. **Types** @@ -89,7 +89,7 @@ No attributes available. - 5 + 5 2 3 4 @@ -100,11 +100,11 @@ No attributes available. - 5 + 5 2 6 12 -``` \ No newline at end of file +``` diff --git a/docs/ops/movement/Transpose_1.md b/docs/ops/movement/Transpose_1.md index 604dc3258d3..5c38171035e 100644 --- a/docs/ops/movement/Transpose_1.md +++ b/docs/ops/movement/Transpose_1.md @@ -6,7 +6,7 @@ **Short description**: *Transpose* operation reorders the input tensor dimensions. -**Detailed description**: *Transpose* operation reorders the input tensor dimensions. Source indexes and destination indexes are bound by the formula: +**Detailed description**: *Transpose* operation reorders the input tensor dimensions. Source indexes and destination indexes are bound by the formula: \f[output[i(order[0]), i(order[1]), ..., i(order[N-1])] = input[i(0), i(1), ..., i(N-1)]\\ \quad \textrm{where} \quad i(j) \quad\textrm{is in the range} \quad [0, (input.shape[j]-1)]\f] @@ -14,12 +14,12 @@ **Inputs**: -* **1**: `arg` - the tensor to be transposed. A tensor of type `T` and arbitrary shape. **Required.** -* **2**: `input_order` - the permutation to apply to the axes of the first input shape. A 1D tensor of `n` elements `T_AXIS` type and shape `[n]`, where `n` is the rank of the first input or `0`. The tensor's value must contain every integer in the range `[0, n-1]`, but if an empty tensor is specified (shape `[0]`), then the axes will be inverted. **Required.** +* **1**: `arg` - the tensor to be transposed. A tensor of type *T* and arbitrary shape. **Required.** +* **2**: `input_order` - the permutation to apply to the axes of the first input shape. A 1D tensor of `n` elements *T_AXIS* type and shape `[n]`, where `n` is the rank of the first input or `0`. The tensor's value must contain every integer in the range `[0, n-1]`, but if an empty tensor is specified (shape `[0]`), then the axes will be inverted. **Required.** **Outputs**: -* **1**: A tensor of type `T` and transposed shape according to the rules specified above. +* **1**: A tensor of type *T* and transposed shape according to the rules specified above. **Types** @@ -67,7 +67,7 @@ 0 - + 4 3 @@ -75,4 +75,4 @@ -``` \ No newline at end of file +``` diff --git a/docs/ops/normalization/LRN_1.md b/docs/ops/normalization/LRN_1.md index 9a73f37f590..2e7f049dbfe 100644 --- a/docs/ops/normalization/LRN_1.md +++ b/docs/ops/normalization/LRN_1.md @@ -63,13 +63,13 @@ output = data / (bias + (alpha / size ** len(axes)) * sqr_sum) ** beta **Inputs** -* **1**: `data` - tensor of type `T` and arbitrary shape. **Required.** +* **1**: `data` - tensor of type *T* and arbitrary shape. **Required.** -* **2**: `axes` - 1D tensor of type `T_IND` which specifies indices of dimensions in `data` which define normalization slices. **Required.** +* **2**: `axes` - 1D tensor of type *T_IND* which specifies indices of dimensions in `data` which define normalization slices. **Required.** **Outputs** -* **1**: Output tensor of type `T` and the same shape as the `data` input tensor. +* **1**: Output tensor of type *T* and the same shape as the `data` input tensor. **Types** * *T*: any supported floating point type. diff --git a/docs/ops/pooling/MaxPool_1.md b/docs/ops/pooling/MaxPool_1.md index 5b73c9b21c5..8ebc653c18d 100644 --- a/docs/ops/pooling/MaxPool_1.md +++ b/docs/ops/pooling/MaxPool_1.md @@ -6,7 +6,7 @@ **Short description**: Performs max pooling operation on input. -**Detailed description**: Input shape can be either 3D, 4D or 5D. Max Pooling operation is performed with the respect to input shape from the third dimension to the last dimension. If paddings are used then during the pooling calculation their value are `-inf`. The Max Pooling operation involves sliding a filter over each channel of feature map and downsampling by choosing the biggest value within the region covered by the filter. [Article about max pooling in Convolutional Networks](https://deeplizard.com/learn/video/ZjM_XQa5s6s). +**Detailed description**: Input shape can be either 3D, 4D or 5D. Max Pooling operation is performed with the respect to input shape from the third dimension to the last dimension. If paddings are used then during the pooling calculation their value are `-inf`. The Max Pooling operation involves sliding a filter over each channel of feature map and downsampling by choosing the biggest value within the region covered by the filter. [Article about max pooling in Convolutional Networks](https://deeplizard.com/learn/video/ZjM_XQa5s6s). **Attributes**: *Pooling* attributes are specified in the `data` node, which is a child of the layer node. @@ -67,7 +67,7 @@ **Inputs**: -* **1**: 3D, 4D or 5D input tensor of type T. Required. +* **1**: 3D, 4D or 5D input tensor of type *T*. Required. **Outputs**: * **1**: Input shape can be either `[N, C, H]`, `[N, C, H, W]` or `[N, C, H, W, D]`. Then the corresponding output shape will be `[N, C, H_out]`, `[N, C, H_out, W_out]` or `[N, C, H_out, W_out, D_out]`. Output tensor has the same data type as input tensor. @@ -77,38 +77,38 @@ * *T*: floating point or integer type. **Mathematical Formulation** -Output shape calculation based on `auto_pad` and `rounding_type`: - * `auto_pad = explicit` and `rounding_type = floor` - `H_out = floor(H + pads_begin[0] + pads_end[0] - kernel[0] / strides[0]) + 1` - `W_out = floor(W + pads_begin[1] + pads_end[1] - kernel[1] / strides[1]) + 1` - `D_out = floor(D + pads_begin[2] + pads_end[2] - kernel[2] / strides[2]) + 1` - - * `auto_pad = valid` and `rounding_type = floor` - `H_out = floor(H - kernel[0] / strides[0]) + 1` - `W_out = floor(W - kernel[1] / strides[1]) + 1` - `D_out = floor(D - kernel[2] / strides[2]) + 1` - - * `auto_pad = same_upper/same_lower` and `rounding_type = floor` - `H_out = H` - `W_out = W` - `D_out = D` - - * `auto_pad = explicit` and `rounding_type = ceil` - `H_out = ceil(H + pads_begin[0] + pads_end[0] - kernel[0] / strides[0]) + 1` - `W_out = ceil(W + pads_begin[1] + pads_end[1] - kernel[1] / strides[1]) + 1` - `D_out = ceil(D + pads_begin[2] + pads_end[2] - kernel[2] / strides[2]) + 1` - - * `auto_pad = valid` and `rounding_type = ceil` - `H_out = ceil(H - kernel[0] / strides[0]) + 1` - `W_out = ceil(W - kernel[1] / strides[1]) + 1` - `D_out = ceil(D - kernel[2] / strides[2]) + 1` - - * `auto_pad = same_upper/same_lower` and `rounding_type = ceil` - `H_out = H` - `W_out = W` - `D_out = D` +Output shape calculation based on `auto_pad` and `rounding_type`: + * `auto_pad = explicit` and `rounding_type = floor` + `H_out = floor(H + pads_begin[0] + pads_end[0] - kernel[0] / strides[0]) + 1` + `W_out = floor(W + pads_begin[1] + pads_end[1] - kernel[1] / strides[1]) + 1` + `D_out = floor(D + pads_begin[2] + pads_end[2] - kernel[2] / strides[2]) + 1` -If `H + pads_begin[i] + pads_end[i] - kernel[i]` is not divided by `strides[i]` evenly then the result is rounded with the respect to `rounding_type` attribute. + * `auto_pad = valid` and `rounding_type = floor` + `H_out = floor(H - kernel[0] / strides[0]) + 1` + `W_out = floor(W - kernel[1] / strides[1]) + 1` + `D_out = floor(D - kernel[2] / strides[2]) + 1` + + * `auto_pad = same_upper/same_lower` and `rounding_type = floor` + `H_out = H` + `W_out = W` + `D_out = D` + + * `auto_pad = explicit` and `rounding_type = ceil` + `H_out = ceil(H + pads_begin[0] + pads_end[0] - kernel[0] / strides[0]) + 1` + `W_out = ceil(W + pads_begin[1] + pads_end[1] - kernel[1] / strides[1]) + 1` + `D_out = ceil(D + pads_begin[2] + pads_end[2] - kernel[2] / strides[2]) + 1` + + * `auto_pad = valid` and `rounding_type = ceil` + `H_out = ceil(H - kernel[0] / strides[0]) + 1` + `W_out = ceil(W - kernel[1] / strides[1]) + 1` + `D_out = ceil(D - kernel[2] / strides[2]) + 1` + + * `auto_pad = same_upper/same_lower` and `rounding_type = ceil` + `H_out = H` + `W_out = W` + `D_out = D` + +If `H + pads_begin[i] + pads_end[i] - kernel[i]` is not divided by `strides[i]` evenly then the result is rounded with the respect to `rounding_type` attribute. Example 1 shows how *MaxPool* operates with 4D input using 2D kernel and `auto_pad = explicit` @@ -194,7 +194,7 @@ output = [[[[5, 3], ```xml - + 1 3 @@ -214,7 +214,7 @@ output = [[[[5, 3], - + 1 3 @@ -234,7 +234,7 @@ output = [[[[5, 3], - + 1 3 @@ -251,4 +251,4 @@ output = [[[[5, 3], -``` \ No newline at end of file +``` diff --git a/docs/ops/sequence/CTCGreedyDecoderSeqLen_6.md b/docs/ops/sequence/CTCGreedyDecoderSeqLen_6.md index adc04ac0ebc..c676af4dac0 100644 --- a/docs/ops/sequence/CTCGreedyDecoderSeqLen_6.md +++ b/docs/ops/sequence/CTCGreedyDecoderSeqLen_6.md @@ -30,7 +30,7 @@ The main difference between [CTCGreedyDecoder](CTCGreedyDecoder_1.md) and CTCGre * **Type**: `boolean` * **Default value**: true * **Required**: *No* - + * *classes_index_type* * **Description**: the type of output tensor with classes indices @@ -38,7 +38,7 @@ The main difference between [CTCGreedyDecoder](CTCGreedyDecoder_1.md) and CTCGre * **Type**: string * **Default value**: "i32" * **Required**: *No* - + * *sequence_length_type* * **Description**: the type of output tensor with sequence length diff --git a/docs/ops/sequence/CTCGreedyDecoder_1.md b/docs/ops/sequence/CTCGreedyDecoder_1.md index 35414602cde..20bcc449a56 100644 --- a/docs/ops/sequence/CTCGreedyDecoder_1.md +++ b/docs/ops/sequence/CTCGreedyDecoder_1.md @@ -28,13 +28,13 @@ Sequences in the batch can have different length. The lengths of sequences are c **Inputs** -* **1**: `data` - input tensor with batch of sequences of type `T_F` and shape `[T, N, C]`, where `T` is the maximum sequence length, `N` is the batch size and `C` is the number of classes. **Required.** +* **1**: `data` - input tensor with batch of sequences of type *T_F* and shape `[T, N, C]`, where `T` is the maximum sequence length, `N` is the batch size and `C` is the number of classes. **Required.** -* **2**: `sequence_mask` - input tensor with sequence masks for each sequence in the batch of type `T_F` populated with values `0` and `1` and shape `[T, N]`. **Required.** +* **2**: `sequence_mask` - input tensor with sequence masks for each sequence in the batch of type *T_F* populated with values `0` and `1` and shape `[T, N]`. **Required.** **Output** -* **1**: Output tensor of type `T_F` and shape `[N, T, 1, 1]` which is filled with integer elements containing final sequence class indices. A final sequence can be shorter that the size `T` of the tensor, all elements that do not code sequence classes are filled with `-1`. +* **1**: Output tensor of type *T_F* and shape `[N, T, 1, 1]` which is filled with integer elements containing final sequence class indices. A final sequence can be shorter that the size `T` of the tensor, all elements that do not code sequence classes are filled with `-1`. **Types** * *T_F*: any supported floating point type. @@ -64,4 +64,4 @@ Sequences in the batch can have different length. The lengths of sequences are c -``` \ No newline at end of file +``` diff --git a/docs/ops/shape/Reshape_1.md b/docs/ops/shape/Reshape_1.md index 10145480ccb..83433f53185 100644 --- a/docs/ops/shape/Reshape_1.md +++ b/docs/ops/shape/Reshape_1.md @@ -26,7 +26,7 @@ If `special_zero` is set to `true` index of `0` cannot be larger than the rank o **Inputs**: -* **1**: `data` a tensor of type T and arbitrary shape. **Required**. +* **1**: `data` a tensor of type *T* and arbitrary shape. **Required**. * **2**: `shape` 1D tensor of type *T_SHAPE* describing output shape. **Required**.