View source on GitHub 
Extracts a strided slice of a tensor (generalized Python array indexing).
tf.strided_slice(
input_,
begin,
end,
strides=None,
begin_mask=0,
end_mask=0,
ellipsis_mask=0,
new_axis_mask=0,
shrink_axis_mask=0,
var=None,
name=None
)
See also tf.slice
.
Instead of calling this op directly most users will want to use the
NumPystyle slicing syntax (e.g. tensor[..., 3:4:1, tf.newaxis, 3]
), which
is supported via tf.Tensor.getitem
and tf.Variable.getitem
.
The interface of this op is a lowlevel encoding of the slicing syntax.
Roughly speaking, this op extracts a slice of size (endbegin)/stride
from the given input_
tensor. Starting at the location specified by begin
the slice continues by adding stride
to the index until all dimensions are
not less than end
.
Note that a stride can be negative, which causes a reverse slice.
Given a Python slice input[spec0, spec1, ..., specn]
,
this function will be called as follows.
begin
, end
, and strides
will be vectors of length n.
n in general is not equal to the rank of the input_
tensor.
In each mask field (begin_mask
, end_mask
, ellipsis_mask
,
new_axis_mask
, shrink_axis_mask
) the ith bit will correspond to
the ith spec.
If the ith bit of begin_mask
is set, begin[i]
is ignored and
the fullest possible range in that dimension is used instead.
end_mask
works analogously, except with the end range.
foo[5:,:,:3]
on a 7x8x9 tensor is equivalent to foo[5:7,0:8,0:3]
.
foo[::1]
reverses a tensor with shape 8.
If the ith bit of ellipsis_mask
is set, as many unspecified dimensions
as needed will be inserted between other dimensions. Only one
nonzero bit is allowed in ellipsis_mask
.
For example foo[3:5,...,4:5]
on a shape 10x3x3x10 tensor is
equivalent to foo[3:5,:,:,4:5]
and
foo[3:5,...]
is equivalent to foo[3:5,:,:,:]
.
If the ith bit of new_axis_mask
is set, then begin
,
end
, and stride
are ignored and a new length 1 dimension is
added at this point in the output tensor.
For example,
foo[:4, tf.newaxis, :2]
would produce a shape (4, 1, 2)
tensor.
If the ith bit of shrink_axis_mask
is set, it implies that the ith
specification shrinks the dimensionality by 1, taking on the value at index
begin[i]
. end[i]
and strides[i]
are ignored in this case. For example in
Python one might do foo[:, 3, :]
which would result in shrink_axis_mask
equal to 2.
t = tf.constant([[[1, 1, 1], [2, 2, 2]],
[[3, 3, 3], [4, 4, 4]],
[[5, 5, 5], [6, 6, 6]]])
tf.strided_slice(t, [1, 0, 0], [2, 1, 3], [1, 1, 1]) # [[[3, 3, 3]]]
tf.strided_slice(t, [1, 0, 0], [2, 2, 3], [1, 1, 1]) # [[[3, 3, 3],
# [4, 4, 4]]]
tf.strided_slice(t, [1, 1, 0], [2, 3, 3], [1, 1, 1]) # [[[4, 4, 4],
# [3, 3, 3]]]
Returns  

A Tensor the same type as input .
