@Namespace(value="tensorflow::ops") @NoOffset public static class tensorflow.StridedSlice extends Pointer
input.
Note, most python users will want to use the Python Tensor.__getitem__
or Variable.__getitem__ rather than this op directly.
The goal of this op is to produce a new tensor with a subset of
the elements from the n dimensional input tensor. The subset is chosen using
a sequence of m sparse range specifications encoded into the arguments
of this function. Note, in some cases
m could be equal to n, but this need not be the case. Each
range specification entry can be one of the following:
- An ellipsis (...). Ellipses are used to imply zero or more
dimensions of full-dimension selection and are produced using
ellipsis_mask. For example, foo[...] is the identity slice.
- A new axis. This is used to insert a new shape=1 dimension and is
produced using new_axis_mask. For example, foo[:, ...] where
foo is shape (3, 4) produces a (1, 3, 4) tensor.
- A range begin:end:stride. This is used to specify how much to choose from
a given dimension. stride can be any integer but 0. begin is an integer
which represents the index of the first value to select while end represents
the index of the last value to select. The number of values selected in each
dimension is end - begin if stride > 0 and begin - end if stride < 0.
begin and end can be negative where -1 is the last element, -2 is
the second to last. begin_mask controls whether to replace the explicitly
given begin with an implicit effective value of 0 if stride > 0 and
-1 if stride < 0. end_mask is analogous but produces the number
required to create the largest open interval. For example, given a shape
(3,) tensor foo[:], the effective begin and end are 0 and 3. Do
not assume this is equivalent to foo[0:-1] which has an effective begin
and end of 0 and 2. Another example is foo[-2::-1] which reverses the
first dimension of a tensor while dropping the last two (in the original
order elements). For example foo = [1,2,3,4]; foo[-2::-1] is [4,3].
- A single index. This is used to keep only elements that have a given
index. For example (foo[2, :] on a shape (5,6) tensor produces a
shape (6,) tensor. This is encoded in begin and end and
shrink_axis_mask.
Each conceptual range specification is encoded in the op's argument. This
encoding is best understand by considering a non-trivial example. In
particular,
foo[1, 2:4, None, ..., :-3:-1, :] will be encoded as
begin = [1, 2, x, x, 0, x] # x denotes don't care (usually 0)
end = [2, 4, x, x, -3, x]
strides = [1, 1, x, x, -1, 1]
begin_mask = 1<<4 | 1 << 5 = 48
end_mask = 1<<5 = 32
ellipsis_mask = 1<<3 = 8
new_axis_mask = 1<<2 4
shrink_axis_mask = 1<<0
foo.shape is (5, 5, 5, 5, 5, 5) the final shape of
the slice becomes (2, 1, 5, 5, 2, 5).
Let us walk step by step through each argument specification.
1. The first argument in the example slice is turned into begin = 1 and
end = begin + 1 = 2. To disambiguate from the original spec 2:4 we
also set the appropriate bit in shrink_axis_mask.
2. 2:4 is contributes 2, 4, 1 to begin, end, and stride. All masks have
zero bits contributed.
3. None is a synonym for tf.newaxis. This means insert a dimension of size 1
dimension in the final shape. Dummy values are contributed to begin,
end and stride, while the new_axis_mask bit is set.
4. ... grab the full ranges from as many dimensions as needed to
fully specify a slice for every dimension of the input shape.
5. :-3:-1 shows the use of negative indices. A negative index i associated
with a dimension that has shape s is converted to a positive index
s + i. So -1 becomes s-1 (i.e. the last element). This conversion
is done internally so begin, end and strides receive x, -3, and -1.
The appropriate begin_mask bit is set to indicate the start range is the
full range (ignoring the x).
6. : indicates that the entire contents of the corresponding dimension
is selected. This is equivalent to :: or 0::1. begin, end, and strides
receive 0, 0, and 1, respectively. The appropriate bits in begin_mask and
end_mask are also set.
*Requirements*:
0 != strides[i] for i in [0, m)
ellipsis_mask must be a power of two (only one ellipsis)
Arguments:
* scope: A Scope object
* begin: begin[k] specifies the offset into the kth range specification.
The exact dimension this corresponds to will be determined by context.
Out-of-bounds values will be silently clamped. If the kth bit of
begin_mask then begin[k] is ignored and the full range of the
appropriate dimension is used instead. Negative values causes indexing
to start from the highest element e.g. If foo==[1,2,3] then foo[-1]==3.
* end: end[i] is like begin with the exception that end_mask is
used to determine full ranges.
* strides: strides[i] specifies the increment in the ith specification
after extracting a given element. Negative indices will reverse
the original order. Out or range values are
clamped to [0,dim[i]) if slice[i]>0 or [-1,dim[i]-1] if slice[i] < 0
Optional attributes (see Attrs):
* begin_mask: a bitmask where a bit i being 1 means to ignore the begin
value and instead use the largest interval possible. At runtime
begin[i] will be replaced with [0, n-1) if stride[i] > 0 or
[-1, n-1] if stride[i] < 0
* end_mask: analogous to begin_mask
* ellipsis_mask: a bitmask where bit i being 1 means the ith
position is actually an ellipsis. One bit at most can be 1.
If ellipsis_mask == 0, then an implicit ellipsis mask of 1 << (m+1)
is provided. This means that foo[3:5] == foo[3:5, ...]. An ellipsis
implicitly creates as many range specifications as necessary to fully
specify the sliced range for every dimension. For example for a 4-dimensional
tensor foo the slice foo[2, ..., 5:8] implies foo[2, :, :, 5:8].
* new_axis_mask: a bitmask where bit i being 1 means the ith
specification creates a new shape 1 dimension. For example
foo[:4, tf.newaxis, :2] would produce a shape (4, 1, 2) tensor.
* shrink_axis_mask: a bitmask where bit i implies that the ith
specification should shrink the dimensionality. begin and end
must imply a slice of size 1 in the dimension. For example in
python one might do foo[:, 3, :] which would result in
shrink_axis_mask being 2.
Returns:
* Output: The output tensor.| Modifier and Type | Class and Description |
|---|---|
static class |
tensorflow.StridedSlice.Attrs
Optional attribute setters for StridedSlice
|
Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator| Constructor and Description |
|---|
StridedSlice(Pointer p)
Pointer cast constructor.
|
StridedSlice(tensorflow.Scope scope,
tensorflow.Input input,
tensorflow.Input begin,
tensorflow.Input end,
tensorflow.Input strides) |
StridedSlice(tensorflow.Scope scope,
tensorflow.Input input,
tensorflow.Input begin,
tensorflow.Input end,
tensorflow.Input strides,
tensorflow.StridedSlice.Attrs attrs) |
| Modifier and Type | Method and Description |
|---|---|
tensorflow.Input |
asInput() |
tensorflow.Output |
asOutput() |
static tensorflow.StridedSlice.Attrs |
BeginMask(long x) |
static tensorflow.StridedSlice.Attrs |
EllipsisMask(long x) |
static tensorflow.StridedSlice.Attrs |
EndMask(long x) |
static tensorflow.StridedSlice.Attrs |
NewAxisMask(long x) |
tensorflow.Node |
node() |
tensorflow.Operation |
operation() |
tensorflow.StridedSlice |
operation(tensorflow.Operation operation) |
tensorflow.Output |
output() |
tensorflow.StridedSlice |
output(tensorflow.Output output) |
static tensorflow.StridedSlice.Attrs |
ShrinkAxisMask(long x) |
address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, position, put, realloc, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zeropublic StridedSlice(Pointer p)
Pointer.Pointer(Pointer).public StridedSlice(@Const @ByRef tensorflow.Scope scope, @ByVal tensorflow.Input input, @ByVal tensorflow.Input begin, @ByVal tensorflow.Input end, @ByVal tensorflow.Input strides)
public StridedSlice(@Const @ByRef tensorflow.Scope scope, @ByVal tensorflow.Input input, @ByVal tensorflow.Input begin, @ByVal tensorflow.Input end, @ByVal tensorflow.Input strides, @Const @ByRef tensorflow.StridedSlice.Attrs attrs)
@ByVal @Name(value="operator tensorflow::Output") public tensorflow.Output asOutput()
@ByVal @Name(value="operator tensorflow::Input") public tensorflow.Input asInput()
public tensorflow.Node node()
@ByVal public static tensorflow.StridedSlice.Attrs BeginMask(@Cast(value="tensorflow::int64") long x)
@ByVal public static tensorflow.StridedSlice.Attrs EndMask(@Cast(value="tensorflow::int64") long x)
@ByVal public static tensorflow.StridedSlice.Attrs EllipsisMask(@Cast(value="tensorflow::int64") long x)
@ByVal public static tensorflow.StridedSlice.Attrs NewAxisMask(@Cast(value="tensorflow::int64") long x)
@ByVal public static tensorflow.StridedSlice.Attrs ShrinkAxisMask(@Cast(value="tensorflow::int64") long x)
@ByRef public tensorflow.Operation operation()
public tensorflow.StridedSlice operation(tensorflow.Operation operation)
@ByRef public tensorflow.Output output()
public tensorflow.StridedSlice output(tensorflow.Output output)
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