tensorflow::Tensor

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 satisfy 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()).

NOTE: When fed with an N-dimensional tensor, this method returns a tensor also with N dimensions. If you want to select a sub tensor, see SubSlice.

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

Select a subslice from this tensor along the 1st dimension.

When fed with an N-dimensional tensor, this method returns a tensor with N-1 dimensions, where the returned tensor is a subslice of the input tensor along the first dimension. The N-1 dimensions of the returned tensor are the last N-1 dimensions of the input tensor.

NOTE: The returned tensor may not satisfy 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 <= index < dim_size(0)

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

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:

Tensor(DT_FLOAT, TensorShape({}))

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.

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.

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.

Creates a tensor with the input datatype, shape and buf.

Acquires a ref on buf that belongs to this Tensor.

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(
  double scalar_value
)

 Tensor(
  int32 scalar_value
)

 Tensor(
  uint32 scalar_value
)

 Tensor(
  uint16 scalar_value
)

 Tensor(
  uint8 scalar_value
)

 Tensor(
  int16 scalar_value
)

 Tensor(
  int8 scalar_value
)

 Tensor(
  tstring scalar_value
)

 Tensor(
  complex64 scalar_value
)

 Tensor(
  complex128 scalar_value
)

 Tensor(
  int64 scalar_value
)

 Tensor(
  uint64 scalar_value
)

 Tensor(
  bool scalar_value
)

 Tensor(
  qint8 scalar_value
)

 Tensor(
  quint8 scalar_value
)

 Tensor(
  qint16 scalar_value
)

 Tensor(
  quint16 scalar_value
)

 Tensor(
  qint32 scalar_value
)

 Tensor(
  bfloat16 scalar_value
)

 Tensor(
  Eigen::half scalar_value
)

 Tensor(
  ResourceHandle scalar_value
)

 Tensor(
  const char *scalar_value
)

 Tensor(
  const Tensor & other
)

 Tensor(
  Tensor && other
)

 Tensor(
  T *t
)=delete

size_t TotalBytes() const 

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

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

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

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

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

void * data() const 

int64 dim_size(
  int d
) const 

int dims() const 

DataType dtype() const 

TTypes< T >::Flat flat()

  
    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();

TTypes< T >::ConstFlat flat() const 

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

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

TTypes< T, NDIMS >::Tensor flat_inner_outer_dims(
  int64 begin
)

TTypes< T, NDIMS >::ConstTensor flat_inner_outer_dims(
  int64 begin
) const 

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

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

TTypes< T >::Matrix matrix()

TTypes< T >::ConstMatrix matrix() const 

Tensor & operator=(
  const Tensor & other
)

Tensor & operator=(
  Tensor && other
)

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

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

TTypes< T >::Scalar scalar()

TTypes< T >::ConstScalar scalar() const 

const TensorShape & shape() const 

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

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

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

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

StringPiece tensor_data() const 

TTypes< T >::UnalignedFlat unaligned_flat()

TTypes< T >::UnalignedConstFlat unaligned_flat() const 

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

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

TTypes< T >::Vec vec()

  
    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.

TTypes< T >::ConstVec vec() const 

 ~Tensor()