tensorflow::Tensor

#include <tensor.h>

Represents an n-dimensional array of values.

Summary

Constructors and Destructors

Tensor()
Creates a 1-dimensional, 0-element float tensor.
Tensor(DataType type, const TensorShape & shape)
Creates a Tensor of the given type and shape.
Tensor(Allocator *a, DataType type, const TensorShape & shape)
Creates a tensor with the input type and shape, using the allocator a to allocate the underlying buffer.
Tensor(Allocator *a, DataType type, const TensorShape & shape, const AllocationAttributes & allocation_attr)
Creates a tensor with the input type and shape, using the allocator a and the specified "allocation_attr" to allocate the underlying buffer.
Tensor(DataType type)
Creates an empty Tensor of the given data type.
Tensor(const Tensor & other)
Copy constructor.
Tensor(Tensor && other)
Move constructor.
~Tensor()

Public functions

AsProtoField(TensorProto *proto) const
void
Fills in proto with *this tensor's content.
AsProtoTensorContent(TensorProto *proto) const
void
CopyFrom(const Tensor & other, const TensorShape & shape) TF_MUST_USE_RESULT
bool
Copy the other tensor into this tensor and reshape it.
DebugString() const
string
A human-readable summary of the tensor suitable for debugging.
FillDescription(TensorDescription *description) const
void
Fill in the TensorDescription proto with metadata about the tensor that is useful for monitoring and debugging.
FromProto(const TensorProto & other) TF_MUST_USE_RESULT
bool
Parse other and construct the tensor.
FromProto(Allocator *a, const TensorProto & other) TF_MUST_USE_RESULT
bool
IsAligned() const
bool
Returns true iff this tensor is aligned.
IsInitialized() const
bool
If necessary, has this Tensor been initialized?
IsSameSize(const Tensor & b) const
bool
NumElements() const
int64
Convenience accessor for the tensor shape.
SharesBufferWith(const Tensor & b) const
bool
Slice(int64 dim0_start, int64 dim0_limit) const
Slice this tensor along the 1st dimension.
SummarizeValue(int64 max_entries) const
string
Render the first max_entries values in *this into a string.
TotalBytes() const
size_t
Returns the estimated memory usage of this tensor.
UnsafeCopyFromInternal(const Tensor &, DataType dtype, const TensorShape &)
void
Copy the other tensor into this tensor and reshape it and reinterpret the buffer's datatype.
bit_casted_shaped(gtl::ArraySlice< int64 > new_sizes)
TTypes< T, NDIMS >::Tensor
Return the tensor data to an Eigen::Tensor with the new shape specified in new_sizes and cast to a new dtype T.
bit_casted_shaped(gtl::ArraySlice< int64 > new_sizes) const
TTypes< T, NDIMS >::ConstTensor
Return the tensor data to an Eigen::Tensor with the new shape specified in new_sizes and cast to a new dtype T.
bit_casted_tensor()
TTypes< T, NDIMS >::Tensor
Return the tensor data to an Eigen::Tensor with the same size but a bitwise cast to the specified dtype T.
bit_casted_tensor() const
TTypes< T, NDIMS >::ConstTensor
Return the tensor data to an Eigen::Tensor with the same size but a bitwise cast to the specified dtype T.
dim_size(int d) const
int64
Convenience accessor for the tensor shape.
dims() const
int
Convenience accessor for the tensor shape.
dtype() const
DataType
Returns the data type.
flat()
TTypes< T >::Flat
Return the tensor data as an Eigen::Tensor of the data type and a specified shape.
flat() const
TTypes< T >::ConstFlat
flat_inner_dims()
TTypes< T, NDIMS >::Tensor
Returns the data as an Eigen::Tensor with NDIMS dimensions, collapsing all Tensor dimensions but the last NDIMS-1 into the first dimension of the result.
flat_inner_dims() const
TTypes< T, NDIMS >::ConstTensor
flat_outer_dims()
TTypes< T, NDIMS >::Tensor
Returns the data as an Eigen::Tensor with NDIMS dimensions, collapsing all Tensor dimensions but the first NDIMS-1 into the last dimension of the result.
flat_outer_dims() const
TTypes< T, NDIMS >::ConstTensor
matrix()
TTypes< T >::Matrix
matrix() const
TTypes< T >::ConstMatrix
operator=(const Tensor & other)
Tensor &
Assign operator. This tensor shares other's underlying storage.
operator=(Tensor && other)
Tensor &
Move operator. See move constructor for details.
scalar()
TTypes< T >::Scalar
Return the Tensor data as a TensorMap of fixed size 1: TensorMap>.
scalar() const
TTypes< T >::ConstScalar
shape() const
const TensorShape &
Returns the shape of the tensor.
shaped(gtl::ArraySlice< int64 > new_sizes)
TTypes< T, NDIMS >::Tensor
shaped(gtl::ArraySlice< int64 > new_sizes) const
TTypes< T, NDIMS >::ConstTensor
tensor()
TTypes< T, NDIMS >::Tensor
tensor() const
TTypes< T, NDIMS >::ConstTensor
tensor_data() const
StringPiece
Returns a StringPiece mapping the current tensor's buffer.
unaligned_flat()
TTypes< T >::UnalignedFlat
unaligned_flat() const
TTypes< T >::UnalignedConstFlat
unaligned_shaped(gtl::ArraySlice< int64 > new_sizes)
TTypes< T, NDIMS >::UnalignedTensor
unaligned_shaped(gtl::ArraySlice< int64 > new_sizes) const
TTypes< T, NDIMS >::UnalignedConstTensor
vec()
TTypes< T >::Vec
Return the tensor data as an Eigen::Tensor with the type and sizes of this Tensor.
vec() const
TTypes< T >::ConstVec
Const versions of all the methods above.

Public functions

AsProtoField

void AsProtoField(
  TensorProto *proto
) const 

Fills in proto with *this tensor's content.

AsProtoField() fills in the repeated field for proto.dtype(), while AsProtoTensorContent() encodes the content in proto.tensor_content() in a compact form.

AsProtoTensorContent

void AsProtoTensorContent(
  TensorProto *proto
) const 

CopyFrom

bool CopyFrom(
  const Tensor & other,
  const TensorShape & shape
) TF_MUST_USE_RESULT

Copy the other tensor into this tensor and reshape it.

This tensor shares other's underlying storage. Returns true iff other.shape() has the same number of elements of the given shape.

DebugString

string DebugString() const 

A human-readable summary of the tensor suitable for debugging.

FillDescription

void FillDescription(
  TensorDescription *description
) const 

Fill in the TensorDescription proto with metadata about the tensor that is useful for monitoring and debugging.

FromProto

bool FromProto(
  const TensorProto & other
) TF_MUST_USE_RESULT

Parse other and construct the tensor.

Returns true iff the parsing succeeds. If the parsing fails, the state of *this is unchanged.

FromProto

bool FromProto(
  Allocator *a,
  const TensorProto & other
) TF_MUST_USE_RESULT

IsAligned

bool IsAligned() const 

Returns true iff this tensor is aligned.

IsInitialized

bool IsInitialized() const 

If necessary, has this Tensor been initialized?

Zero-element Tensors are always considered initialized, even if they have never been assigned to and do not have any memory allocated.

IsSameSize

bool IsSameSize(
  const Tensor & b
) const 

NumElements

int64 NumElements() const 

Convenience accessor for the tensor shape.

SharesBufferWith

bool SharesBufferWith(
  const Tensor & b
) const 

Slice

Tensor Slice(
  int64 dim0_start,
  int64 dim0_limit
) const 

Slice this tensor along the 1st dimension.

I.e., the returned tensor satisfies returned[i, ...] == this[dim0_start + i, ...]. The returned tensor shares the underlying tensor buffer with this tensor.

NOTE: The returned tensor may not satisfies the same alignment requirement as this tensor depending on the shape. The caller must check the returned tensor's alignment before calling certain methods that have alignment requirement (e.g., flat(), tensor()).

REQUIRES: dims() >= 1 REQUIRES: 0 <= dim0_start <= dim0_limit <= dim_size(0)

SummarizeValue

string SummarizeValue(
  int64 max_entries
) const 

Render the first max_entries values in *this into a string.

Tensor

 Tensor()

Creates a 1-dimensional, 0-element float tensor.

The returned Tensor is not a scalar (shape {}), but is instead an empty one-dimensional Tensor (shape {0}, NumElements() == 0). Since it has no elements, it does not need to be assigned a value and is initialized by default (IsInitialized() is true). If this is undesirable, consider creating a one-element scalar which does require initialization:

```c++

Tensor(DT_FLOAT, TensorShape({}))

```

Tensor

 Tensor(
  DataType type,
  const TensorShape & shape
)

Creates a Tensor of the given type and shape.

If LogMemory::IsEnabled() the allocation is logged as coming from an unknown kernel and step. Calling the Tensor constructor directly from within an Op is deprecated: use the OpKernelConstruction/OpKernelContext allocate_* methods to allocate a new tensor, which record the kernel and step.

The underlying buffer is allocated using a CPUAllocator.

Tensor

 Tensor(
  Allocator *a,
  DataType type,
  const TensorShape & shape
)

Creates a tensor with the input type and shape, using the allocator a to allocate the underlying buffer.

If LogMemory::IsEnabled() the allocation is logged as coming from an unknown kernel and step. Calling the Tensor constructor directly from within an Op is deprecated: use the OpKernelConstruction/OpKernelContext allocate_* methods to allocate a new tensor, which record the kernel and step.

a must outlive the lifetime of this Tensor.

Tensor

 Tensor(
  Allocator *a,
  DataType type,
  const TensorShape & shape,
  const AllocationAttributes & allocation_attr
)

Creates a tensor with the input type and shape, using the allocator a and the specified "allocation_attr" to allocate the underlying buffer.

If the kernel and step are known allocation_attr.allocation_will_be_logged should be set to true and LogMemory::RecordTensorAllocation should be called after the tensor is constructed. Calling the Tensor constructor directly from within an Op is deprecated: use the OpKernelConstruction/OpKernelContext allocate_* methods to allocate a new tensor, which record the kernel and step.

a must outlive the lifetime of this Tensor.

Tensor

 Tensor(
  DataType type
)

Creates an empty Tensor of the given data type.

Like Tensor(), returns a 1-dimensional, 0-element Tensor with IsInitialized() returning True. See the Tensor() documentation for details.

Tensor

 Tensor(
  const Tensor & other
)

Copy constructor.

Tensor

 Tensor(
  Tensor && other
)

Move constructor.

After this call, is safely destructible and can be assigned to, but other calls on it (e.g. shape manipulation) are not valid.

TotalBytes

size_t TotalBytes() const 

Returns the estimated memory usage of this tensor.

UnsafeCopyFromInternal

void UnsafeCopyFromInternal(
  const Tensor &,
  DataType dtype,
  const TensorShape &
)

Copy the other tensor into this tensor and reshape it and reinterpret the buffer's datatype.

This tensor shares other's underlying storage.

bit_casted_shaped

TTypes< T, NDIMS >::Tensor bit_casted_shaped(
  gtl::ArraySlice< int64 > new_sizes
)

Return the tensor data to an Eigen::Tensor with the new shape specified in new_sizes and cast to a new dtype T.

Using a bitcast is useful for move and copy operations. The allowed bitcast is the only difference from shaped().

bit_casted_shaped

TTypes< T, NDIMS >::ConstTensor bit_casted_shaped(
  gtl::ArraySlice< int64 > new_sizes
) const 

Return the tensor data to an Eigen::Tensor with the new shape specified in new_sizes and cast to a new dtype T.

Using a bitcast is useful for move and copy operations. The allowed bitcast is the only difference from shaped().

bit_casted_tensor

TTypes< T, NDIMS >::Tensor bit_casted_tensor()

Return the tensor data to an Eigen::Tensor with the same size but a bitwise cast to the specified dtype T.

Using a bitcast is useful for move and copy operations. NOTE: this is the same as tensor() except a bitcast is allowed.

bit_casted_tensor

TTypes< T, NDIMS >::ConstTensor bit_casted_tensor() const 

Return the tensor data to an Eigen::Tensor with the same size but a bitwise cast to the specified dtype T.

Using a bitcast is useful for move and copy operations. NOTE: this is the same as tensor() except a bitcast is allowed.

dim_size

int64 dim_size(
  int d
) const 

Convenience accessor for the tensor shape.

dims

int dims() const 

Convenience accessor for the tensor shape.

For all shape accessors, see comments for relevant methods of TensorShape in tensor_shape.h.

dtype

DataType dtype() const 

Returns the data type.

flat

TTypes< T >::Flat flat()

Return the tensor data as an Eigen::Tensor of the data type and a specified shape.

These methods allow you to access the data with the dimensions and sizes of your choice. You do not need to know the number of dimensions of the Tensor to call them. However, they CHECK that the type matches and the dimensions requested creates an Eigen::Tensor with the same number of elements as the tensor.

Example:

```c++

typedef float T;
Tensor my_ten(...built with Shape{planes: 4, rows: 3, cols: 5}...);
// 1D Eigen::Tensor, size 60:
auto flat = my_ten.flat();
// 2D Eigen::Tensor 12 x 5:
auto inner = my_ten.flat_inner_dims();
// 2D Eigen::Tensor 4 x 15:
auto outer = my_ten.shaped({4, 15});
// CHECK fails, bad num elements:
auto outer = my_ten.shaped({4, 8});
// 3D Eigen::Tensor 6 x 5 x 2:
auto weird = my_ten.shaped({6, 5, 2});
// CHECK fails, type mismatch:
auto bad   = my_ten.flat();

```

flat

TTypes< T >::ConstFlat flat() const 

flat_inner_dims

TTypes< T, NDIMS >::Tensor flat_inner_dims()

Returns the data as an Eigen::Tensor with NDIMS dimensions, collapsing all Tensor dimensions but the last NDIMS-1 into the first dimension of the result.

If NDIMS > dims() then leading dimensions of size 1 will be added to make the output rank NDIMS.

flat_inner_dims

TTypes< T, NDIMS >::ConstTensor flat_inner_dims() const 

flat_outer_dims

TTypes< T, NDIMS >::Tensor flat_outer_dims()

Returns the data as an Eigen::Tensor with NDIMS dimensions, collapsing all Tensor dimensions but the first NDIMS-1 into the last dimension of the result.

If NDIMS > dims() then trailing dimensions of size 1 will be added to make the output rank NDIMS.

flat_outer_dims

TTypes< T, NDIMS >::ConstTensor flat_outer_dims() const 

matrix

TTypes< T >::Matrix matrix()

matrix

TTypes< T >::ConstMatrix matrix() const 

operator=

Tensor & operator=(
  const Tensor & other
)

Assign operator. This tensor shares other's underlying storage.

operator=

Tensor & operator=(
  Tensor && other
)

Move operator. See move constructor for details.

scalar

TTypes< T >::Scalar scalar()

Return the Tensor data as a TensorMap of fixed size 1: TensorMap>.

Using scalar() allows the compiler to perform optimizations as the size of the tensor is known at compile time.

scalar

TTypes< T >::ConstScalar scalar() const 

shape

const TensorShape & shape() const 

Returns the shape of the tensor.

shaped

TTypes< T, NDIMS >::Tensor shaped(
  gtl::ArraySlice< int64 > new_sizes
)

shaped

TTypes< T, NDIMS >::ConstTensor shaped(
  gtl::ArraySlice< int64 > new_sizes
) const 

tensor

TTypes< T, NDIMS >::Tensor tensor()

tensor

TTypes< T, NDIMS >::ConstTensor tensor() const 

tensor_data

StringPiece tensor_data() const 

Returns a StringPiece mapping the current tensor's buffer.

The returned StringPiece may point to memory location on devices that the CPU cannot address directly.

NOTE: The underlying tensor buffer is refcounted, so the lifetime of the contents mapped by the StringPiece matches the lifetime of the buffer; callers should arrange to make sure the buffer does not get destroyed while the StringPiece is still used.

REQUIRES: DataTypeCanUseMemcpy(dtype()).

unaligned_flat

TTypes< T >::UnalignedFlat unaligned_flat()

unaligned_flat

TTypes< T >::UnalignedConstFlat unaligned_flat() const 

unaligned_shaped

TTypes< T, NDIMS >::UnalignedTensor unaligned_shaped(
  gtl::ArraySlice< int64 > new_sizes
)

unaligned_shaped

TTypes< T, NDIMS >::UnalignedConstTensor unaligned_shaped(
  gtl::ArraySlice< int64 > new_sizes
) const 

vec

TTypes< T >::Vec vec()

Return the tensor data as an Eigen::Tensor with the type and sizes of this Tensor.

Use these methods when you know the data type and the number of dimensions of the Tensor and you want an Eigen::Tensor automatically sized to the Tensor sizes. The implementation check fails if either type or sizes mismatch.

Example:

```c++

typedef float T;
Tensor my_mat(...built with Shape{rows: 3, cols: 5}...);
auto mat = my_mat.matrix();    // 2D Eigen::Tensor, 3 x 5.
auto mat = my_mat.tensor(); // 2D Eigen::Tensor, 3 x 5.
auto vec = my_mat.vec();       // CHECK fails as my_mat is 2D.
auto vec = my_mat.tensor(); // CHECK fails as my_mat is 2D.
auto mat = my_mat.matrix();// CHECK fails as type mismatch.

```

vec

TTypes< T >::ConstVec vec() const 

Const versions of all the methods above.

~Tensor

 ~Tensor()