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Fix Tensor documentation page.
The extra [TOC] tag is generating a huge floating duplicated table-of-contents, which obscures the majority of the page (see bottom of https://eigen.tuxfamily.org/dox/unsupported/eigen_tensors.html). Remove it. Also, headers do not support markup (see [doxygen bug](https://github.com/doxygen/doxygen/issues/7467)), so backticks like ``` ``` end up generating titles that looks like ``` Constructor <tt>Tensor<double,2></tt> ``` Removing backticks for now. To generate proper formatted headers, we must directly use html instead of markdown, i.e. ``` <h2>Constructor <code>Tensor<double,2></code></h2> ``` which is ugly. Fixes #2254.
This commit is contained in:
Antonio Sanchez
Antonio Sánchez
parent
7b35638ddb
commit
f5a9873bbb
@ -3,8 +3,6 @@
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Tensors are multidimensional arrays of elements. Elements are typically scalars,
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but more complex types such as strings are also supported.
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[TOC]
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## Tensor Classes
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You can manipulate a tensor with one of the following classes. They all are in
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@ -21,7 +19,7 @@ matrix.
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Tensors of this class are resizable. For example, if you assign a tensor of a
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different size to a Tensor, that tensor is resized to match its new value.
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#### Constructor `Tensor<data_type, rank>(size0, size1, ...)`
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#### Constructor Tensor<data_type, rank>(size0, size1, ...)
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Constructor for a Tensor. The constructor must be passed `rank` integers
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indicating the sizes of the instance along each of the the `rank`
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@ -34,7 +32,7 @@ dimensions.
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// Resize t_3d by assigning a tensor of different sizes, but same rank.
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t_3d = Tensor<float, 3>(3, 4, 3);
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#### Constructor `Tensor<data_type, rank>(size_array)`
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#### Constructor Tensor<data_type, rank>(size_array)
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Constructor where the sizes for the constructor are specified as an array of
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values instead of an explicitly list of parameters. The array type to use is
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@ -45,7 +43,7 @@ from an initializer list.
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Tensor<string, 2> t_2d({5, 7});
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### Class `TensorFixedSize<data_type, Sizes<size0, size1, ...>>`
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### Class TensorFixedSize<data_type, Sizes<size0, size1, ...>>
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Class to use for tensors of fixed size, where the size is known at compile
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time. Fixed sized tensors can provide very fast computations because all their
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@ -57,7 +55,7 @@ tensor data is held onto the stack and does not cause heap allocation and free.
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// Create a 4 x 3 tensor of floats.
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TensorFixedSize<float, Sizes<4, 3>> t_4x3;
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### Class `TensorMap<Tensor<data_type, rank>>`
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### Class TensorMap<Tensor<data_type, rank>>
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This is the class to use to create a tensor on top of memory allocated and
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owned by another part of your code. It allows to view any piece of allocated
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@ -67,7 +65,7 @@ data are stored.
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A TensorMap is not resizable because it does not own the memory where its data
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are stored.
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#### Constructor `TensorMap<Tensor<data_type, rank>>(data, size0, size1, ...)`
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#### Constructor TensorMap<Tensor<data_type, rank>>(data, size0, size1, ...)
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Constructor for a Tensor. The constructor must be passed a pointer to the
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storage for the data, and "rank" size attributes. The storage has to be
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@ -87,13 +85,13 @@ large enough to hold all the data.
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TensorMap<Tensor<float, 1>> t_12(t_4x3.data(), 12);
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#### Class `TensorRef`
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#### Class TensorRef
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See Assigning to a TensorRef below.
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## Accessing Tensor Elements
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#### `<data_type> tensor(index0, index1...)`
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#### <data_type> tensor(index0, index1...)
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Return the element at position `(index0, index1...)` in tensor
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`tensor`. You must pass as many parameters as the rank of `tensor`.
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@ -278,7 +276,7 @@ Simiarly, assigning an expression to a TensorMap causes its evaluation. Like
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tensors of type TensorFixedSize, TensorMaps cannot be resized so they have to
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have the rank and sizes of the expression that are assigned to them.
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#### Calling `eval()`.
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#### Calling eval().
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When you compute large composite expressions, you sometimes want to tell Eigen
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that an intermediate value in the expression tree is worth evaluating ahead of
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@ -355,7 +353,7 @@ call for the right hand side:
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(Y / (Y.sum(depth_dim).eval().reshape(dims2d).broadcast(bcast))).eval();
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#### Assigning to a `TensorRef`.
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#### Assigning to a TensorRef.
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If you need to access only a few elements from the value of an expression you
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can avoid materializing the value in a full tensor by using a TensorRef.
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@ -455,24 +453,24 @@ memory for tensors with cuda.
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In the documentation of the tensor methods and Operation we mention datatypes
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that are tensor-type specific:
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#### `<Tensor-Type>::``Dimensions`
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#### <Tensor-Type>::Dimensions
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Acts like an array of ints. Has an `int size` attribute, and can be
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indexed like an array to access individual values. Used to represent the
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dimensions of a tensor. See `dimensions()`.
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#### `<Tensor-Type>::``Index`
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#### <Tensor-Type>::Index
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Acts like an `int`. Used for indexing tensors along their dimensions. See
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`operator()`, `dimension()`, and `size()`.
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#### `<Tensor-Type>::``Scalar`
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#### <Tensor-Type>::Scalar
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Represents the datatype of individual tensor elements. For example, for a
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`Tensor<float>`, `Scalar` is the type `float`. See
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`setConstant()`.
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#### `<Operation>`
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#### <Operation>
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We use this pseudo type to indicate that a tensor Operation is returned by a
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method. We indicate in the text the type and dimensions of the tensor that the
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@ -492,7 +490,7 @@ Tensor, TensorFixedSize, and TensorMap.
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## Metadata
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### `int NumDimensions`
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### int NumDimensions
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Constant value indicating the number of dimensions of a Tensor. This is also
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known as the tensor "rank".
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@ -501,7 +499,7 @@ known as the tensor "rank".
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cout << "Dims " << a.NumDimensions;
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=> Dims 2
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### `Dimensions dimensions()`
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### Dimensions dimensions()
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Returns an array-like object representing the dimensions of the tensor.
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The actual type of the `dimensions()` result is `<Tensor-Type>::``Dimensions`.
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@ -519,7 +517,7 @@ If you use a C++11 compiler, you can use `auto` to simplify the code:
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<< ", dim 1: " << d[1];
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=> Dim size: 2, dim 0: 3, dim 1: 4
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### `Index dimension(Index n)`
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### Index dimension(Index n)
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Returns the n-th dimension of the tensor. The actual type of the
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`dimension()` result is `<Tensor-Type>::``Index`, but you can
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@ -530,7 +528,7 @@ always use it like an int.
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cout << "Dim 1: " << dim1;
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=> Dim 1: 4
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### `Index size()`
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### Index size()
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Returns the total number of elements in the tensor. This is the product of all
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the tensor dimensions. The actual type of the `size()` result is
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@ -605,7 +603,7 @@ You can use one of the methods below to initialize the tensor memory. These
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have an immediate effect on the tensor and return the tensor itself as a
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result. These are not tensor Operations which delay evaluation.
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### `<Tensor-Type> setConstant(const Scalar& val)`
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### <Tensor-Type> setConstant(const Scalar& val)
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Sets all elements of the tensor to the constant value `val`. `Scalar`
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is the type of data stored in the tensor. You can pass any value that is
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@ -633,7 +631,7 @@ has a copy constructor and an `operator=()`:
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yolo yolo yolo
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### `<Tensor-Type> setZero()`
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### <Tensor-Type> setZero()
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Fills the tensor with zeros. Equivalent to `setConstant(Scalar(0))`.
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Returns the tensor itself in case you want to chain another call.
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@ -647,7 +645,7 @@ Returns the tensor itself in case you want to chain another call.
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0 0 0 0
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### `<Tensor-Type> setValues({..initializer_list})`
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### <Tensor-Type> setValues({..initializer_list})
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Fills the tensor with explicit values specified in a std::initializer_list.
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The type of the initializer list depends on the type and rank of the tensor.
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@ -683,7 +681,7 @@ code only sets the values of the first row of the tensor.
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10 20 30
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1000 1000 1000
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### `<Tensor-Type> setRandom()`
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### <Tensor-Type> setRandom()
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Fills the tensor with random values. Returns the tensor itself in case you
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want to chain another call.
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@ -750,7 +748,7 @@ values of a tensor expression, the expression must either be evaluated or
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wrapped in a TensorRef.
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### `Scalar* data()` and `const Scalar* data() const`
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### Scalar* data() and const Scalar* data() const
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Returns a pointer to the storage for the tensor. The pointer is const if the
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tensor was const. This allows direct access to the data. The layout of the
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@ -778,7 +776,7 @@ The chain of Operation is evaluated lazily, typically when it is assigned to a
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tensor. See "Controlling when Expression are Evaluated" for more details about
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their evaluation.
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### `<Operation> constant(const Scalar& val)`
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### <Operation> constant(const Scalar& val)
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Returns a tensor of the same type and dimensions as the original tensor but
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where all elements have the value `val`.
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@ -806,7 +804,7 @@ tensor, or multiply every element of a tensor by a scalar.
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0.6 0.6 0.6
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0.6 0.6 0.6
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### `<Operation> random()`
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### <Operation> random()
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Returns a tensor of the same type and dimensions as the current tensor
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but where all elements have random values.
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@ -836,7 +834,7 @@ All these operations take a single input tensor as argument and return a tensor
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of the same type and dimensions as the tensor to which they are applied. The
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requested operations are applied to each element independently.
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### `<Operation> operator-()`
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### <Operation> operator-()
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Returns a tensor of the same type and dimensions as the original tensor
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containing the opposite values of the original tensor.
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@ -855,42 +853,42 @@ containing the opposite values of the original tensor.
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-1 -1 -1
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-1 -1 -1
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### `<Operation> sqrt()`
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### <Operation> sqrt()
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Returns a tensor of the same type and dimensions as the original tensor
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containing the square roots of the original tensor.
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### `<Operation> rsqrt()`
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### <Operation> rsqrt()
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Returns a tensor of the same type and dimensions as the original tensor
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containing the inverse square roots of the original tensor.
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### `<Operation> square()`
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### <Operation> square()
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Returns a tensor of the same type and dimensions as the original tensor
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containing the squares of the original tensor values.
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### `<Operation> inverse()`
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### <Operation> inverse()
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Returns a tensor of the same type and dimensions as the original tensor
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containing the inverse of the original tensor values.
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### `<Operation> exp()`
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### <Operation> exp()
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Returns a tensor of the same type and dimensions as the original tensor
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containing the exponential of the original tensor.
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### `<Operation> log()`
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### <Operation> log()
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Returns a tensor of the same type and dimensions as the original tensor
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containing the natural logarithms of the original tensor.
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### `<Operation> abs()`
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### <Operation> abs()
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Returns a tensor of the same type and dimensions as the original tensor
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containing the absolute values of the original tensor.
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### `<Operation> pow(Scalar exponent)`
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### <Operation> pow(Scalar exponent)
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Returns a tensor of the same type and dimensions as the original tensor
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containing the coefficients of the original tensor to the power of the
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@ -917,17 +915,17 @@ cubic roots of an int Tensor:
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0 1 2
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3 4 5
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### `<Operation> operator * (Scalar scale)`
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### <Operation> operator * (Scalar scale)
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Multiplies all the coefficients of the input tensor by the provided scale.
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### `<Operation> cwiseMax(Scalar threshold)`
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### <Operation> cwiseMax(Scalar threshold)
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TODO
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### `<Operation> cwiseMin(Scalar threshold)`
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### <Operation> cwiseMin(Scalar threshold)
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TODO
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### `<Operation> unaryExpr(const CustomUnaryOp& func)`
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### <Operation> unaryExpr(const CustomUnaryOp& func)
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TODO
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@ -939,39 +937,39 @@ dimensions as the tensors to which they are applied, and unless otherwise
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specified it is also of the same type. The requested operations are applied to
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each pair of elements independently.
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### `<Operation> operator+(const OtherDerived& other)`
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### <Operation> operator+(const OtherDerived& other)
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Returns a tensor of the same type and dimensions as the input tensors
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containing the coefficient wise sums of the inputs.
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### `<Operation> operator-(const OtherDerived& other)`
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### <Operation> operator-(const OtherDerived& other)
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Returns a tensor of the same type and dimensions as the input tensors
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containing the coefficient wise differences of the inputs.
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### `<Operation> operator*(const OtherDerived& other)`
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### <Operation> operator*(const OtherDerived& other)
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Returns a tensor of the same type and dimensions as the input tensors
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containing the coefficient wise products of the inputs.
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### `<Operation> operator/(const OtherDerived& other)`
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### <Operation> operator/(const OtherDerived& other)
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Returns a tensor of the same type and dimensions as the input tensors
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containing the coefficient wise quotients of the inputs.
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This operator is not supported for integer types.
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### `<Operation> cwiseMax(const OtherDerived& other)`
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### <Operation> cwiseMax(const OtherDerived& other)
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Returns a tensor of the same type and dimensions as the input tensors
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containing the coefficient wise maximums of the inputs.
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### `<Operation> cwiseMin(const OtherDerived& other)`
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### <Operation> cwiseMin(const OtherDerived& other)
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Returns a tensor of the same type and dimensions as the input tensors
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containing the coefficient wise mimimums of the inputs.
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### `<Operation> Logical operators`
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### <Operation> Logical operators
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The following logical operators are supported as well:
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@ -1129,50 +1127,50 @@ scalar, represented as a zero-dimension tensor.
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276
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### `<Operation> sum(const Dimensions& new_dims)`
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### `<Operation> sum()`
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### <Operation> sum(const Dimensions& new_dims)
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### <Operation> sum()
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Reduce a tensor using the sum() operator. The resulting values
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are the sum of the reduced values.
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### `<Operation> mean(const Dimensions& new_dims)`
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### `<Operation> mean()`
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### <Operation> mean(const Dimensions& new_dims)
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### <Operation> mean()
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Reduce a tensor using the mean() operator. The resulting values
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are the mean of the reduced values.
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### `<Operation> maximum(const Dimensions& new_dims)`
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### `<Operation> maximum()`
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### <Operation> maximum(const Dimensions& new_dims)
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### <Operation> maximum()
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Reduce a tensor using the maximum() operator. The resulting values are the
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largest of the reduced values.
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### `<Operation> minimum(const Dimensions& new_dims)`
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### `<Operation> minimum()`
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### <Operation> minimum(const Dimensions& new_dims)
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### <Operation> minimum()
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Reduce a tensor using the minimum() operator. The resulting values
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are the smallest of the reduced values.
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### `<Operation> prod(const Dimensions& new_dims)`
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### `<Operation> prod()`
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### <Operation> prod(const Dimensions& new_dims)
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### <Operation> prod()
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Reduce a tensor using the prod() operator. The resulting values
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are the product of the reduced values.
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### `<Operation> all(const Dimensions& new_dims)`
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### `<Operation> all()`
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### <Operation> all(const Dimensions& new_dims)
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### <Operation> all()
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Reduce a tensor using the all() operator. Casts tensor to bool and then checks
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whether all elements are true. Runs through all elements rather than
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short-circuiting, so may be significantly inefficient.
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### `<Operation> any(const Dimensions& new_dims)`
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### `<Operation> any()`
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### <Operation> any(const Dimensions& new_dims)
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### <Operation> any()
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Reduce a tensor using the any() operator. Casts tensor to bool and then checks
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whether any element is true. Runs through all elements rather than
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short-circuiting, so may be significantly inefficient.
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### `<Operation> reduce(const Dimensions& new_dims, const Reducer& reducer)`
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### <Operation> reduce(const Dimensions& new_dims, const Reducer& reducer)
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Reduce a tensor using a user-defined reduction operator. See `SumReducer`
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in TensorFunctors.h for information on how to implement a reduction operator.
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@ -1208,8 +1206,8 @@ Example: Trace along 2 dimensions.
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15
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### `<Operation> trace(const Dimensions& new_dims)`
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### `<Operation> trace()`
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### <Operation> trace(const Dimensions& new_dims)
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### <Operation> trace()
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As a special case, if no parameter is passed to the operation, trace is computed
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along *all* dimensions of the input tensor.
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@ -1259,18 +1257,18 @@ dd a comment to this line
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1 3 6
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4 9 15
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### `<Operation> cumsum(const Index& axis)`
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### <Operation> cumsum(const Index& axis)
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Perform a scan by summing consecutive entries.
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### `<Operation> cumprod(const Index& axis)`
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### <Operation> cumprod(const Index& axis)
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Perform a scan by multiplying consecutive entries.
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## Convolutions
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### `<Operation> convolve(const Kernel& kernel, const Dimensions& dims)`
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### <Operation> convolve(const Kernel& kernel, const Dimensions& dims)
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Returns a tensor that is the output of the convolution of the input tensor with the kernel,
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along the specified dimensions of the input tensor. The dimension size for dimensions of the output tensor
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@ -1313,7 +1311,7 @@ These operations return a Tensor with different dimensions than the original
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Tensor. They can be used to access slices of tensors, see them with different
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dimensions, or pad tensors with additional data.
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### `<Operation> reshape(const Dimensions& new_dims)`
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### <Operation> reshape(const Dimensions& new_dims)
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Returns a view of the input tensor that has been reshaped to the specified
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new dimensions. The argument new_dims is an array of Index values. The
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@ -1392,7 +1390,7 @@ Note that "b" itself was not reshaped but that instead the assignment is done to
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||||
the reshape view of b.
|
||||
|
||||
|
||||
### `<Operation> shuffle(const Shuffle& shuffle)`
|
||||
### <Operation> shuffle(const Shuffle& shuffle)
|
||||
|
||||
Returns a copy of the input tensor whose dimensions have been
|
||||
reordered according to the specified permutation. The argument shuffle
|
||||
@ -1433,7 +1431,7 @@ Let's rewrite the previous example to take advantage of this feature:
|
||||
output.shuffle({2, 0, 1}) = input;
|
||||
|
||||
|
||||
### `<Operation> stride(const Strides& strides)`
|
||||
### <Operation> stride(const Strides& strides)
|
||||
|
||||
Returns a view of the input tensor that strides (skips stride-1
|
||||
elements) along each of the dimensions. The argument strides is an
|
||||
@ -1459,7 +1457,7 @@ It is possible to assign a tensor to a stride:
|
||||
output.stride({2, 3, 4}) = input;
|
||||
|
||||
|
||||
### `<Operation> slice(const StartIndices& offsets, const Sizes& extents)`
|
||||
### <Operation> slice(const StartIndices& offsets, const Sizes& extents)
|
||||
|
||||
Returns a sub-tensor of the given tensor. For each dimension i, the slice is
|
||||
made of the coefficients stored between offset[i] and offset[i] + extents[i] in
|
||||
@ -1485,7 +1483,7 @@ the input tensor.
|
||||
600 700
|
||||
|
||||
|
||||
### `<Operation> chip(const Index offset, const Index dim)`
|
||||
### <Operation> chip(const Index offset, const Index dim)
|
||||
|
||||
A chip is a special kind of slice. It is the subtensor at the given offset in
|
||||
the dimension dim. The returned tensor has one fewer dimension than the input
|
||||
@ -1536,7 +1534,7 @@ lvalue. For example:
|
||||
0 0 0
|
||||
|
||||
|
||||
### `<Operation> reverse(const ReverseDimensions& reverse)`
|
||||
### <Operation> reverse(const ReverseDimensions& reverse)
|
||||
|
||||
Returns a view of the input tensor that reverses the order of the coefficients
|
||||
along a subset of the dimensions. The argument reverse is an array of boolean
|
||||
@ -1566,7 +1564,7 @@ of a 2D tensor:
|
||||
0 100 200
|
||||
|
||||
|
||||
### `<Operation> broadcast(const Broadcast& broadcast)`
|
||||
### <Operation> broadcast(const Broadcast& broadcast)
|
||||
|
||||
Returns a view of the input tensor in which the input is replicated one to many
|
||||
times.
|
||||
@ -1590,11 +1588,11 @@ made in each of the dimensions.
|
||||
0 100 200 0 100 200
|
||||
300 400 500 300 400 500
|
||||
|
||||
### `<Operation> concatenate(const OtherDerived& other, Axis axis)`
|
||||
### <Operation> concatenate(const OtherDerived& other, Axis axis)
|
||||
|
||||
TODO
|
||||
|
||||
### `<Operation> pad(const PaddingDimensions& padding)`
|
||||
### <Operation> pad(const PaddingDimensions& padding)
|
||||
|
||||
Returns a view of the input tensor in which the input is padded with zeros.
|
||||
|
||||
@ -1619,7 +1617,7 @@ Returns a view of the input tensor in which the input is padded with zeros.
|
||||
0 0 0 0
|
||||
|
||||
|
||||
### `<Operation> extract_patches(const PatchDims& patch_dims)`
|
||||
### <Operation> extract_patches(const PatchDims& patch_dims)
|
||||
|
||||
Returns a tensor of coefficient patches extracted from the input tensor, where
|
||||
each patch is of dimension specified by 'patch_dims'. The returned tensor has
|
||||
@ -1706,7 +1704,7 @@ This code results in the following output when the data layout is RowMajor:
|
||||
6 7
|
||||
10 11
|
||||
|
||||
### `<Operation> extract_image_patches(const Index patch_rows, const Index patch_cols, const Index row_stride, const Index col_stride, const PaddingType padding_type)`
|
||||
### <Operation> extract_image_patches(const Index patch_rows, const Index patch_cols, const Index row_stride, const Index col_stride, const PaddingType padding_type)
|
||||
|
||||
Returns a tensor of coefficient image patches extracted from the input tensor,
|
||||
which is expected to have dimensions ordered as follows (depending on the data
|
||||
@ -1763,7 +1761,7 @@ sizes:
|
||||
|
||||
## Special Operations
|
||||
|
||||
### `<Operation> cast<T>()`
|
||||
### <Operation> cast<T>()
|
||||
|
||||
Returns a tensor of type T with the same dimensions as the original tensor.
|
||||
The returned tensor contains the values of the original tensor converted to
|
||||
@ -1792,7 +1790,7 @@ but you can easily cast the tensors to floats to do the division:
|
||||
1 2 2
|
||||
|
||||
|
||||
### `<Operation> eval()`
|
||||
### <Operation> eval()
|
||||
|
||||
TODO
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user