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Extracts a slice from a tensor.
tf.slice(
input_, begin, size, name=None
)
See also tf.strided_slice
.
This operation extracts a slice of size size
from a tensor input_
starting
at the location specified by begin
. The slice size
is represented as a
tensor shape, where size[i]
is the number of elements of the 'i'th dimension
of input_
that you want to slice. The starting location (begin
) for the
slice is represented as an offset in each dimension of input_
. In other
words, begin[i]
is the offset into the i'th dimension of input_
that you
want to slice from.
Note that tf.Tensor.getitem
is typically a more pythonic way to
perform slices, as it allows you to write foo[3:7, :2]
instead of
tf.slice(foo, [3, 0], [4, foo.get_shape()[1]2])
.
begin
is zerobased; size
is onebased. If size[i]
is 1,
all remaining elements in dimension i are included in the
slice. In other words, this is equivalent to setting:
size[i] = input_.dim_size(i)  begin[i]
This operation requires that:
0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n]
For example:
t = tf.constant([[[1, 1, 1], [2, 2, 2]],
[[3, 3, 3], [4, 4, 4]],
[[5, 5, 5], [6, 6, 6]]])
tf.slice(t, [1, 0, 0], [1, 1, 3]) # [[[3, 3, 3]]]
tf.slice(t, [1, 0, 0], [1, 2, 3]) # [[[3, 3, 3],
# [4, 4, 4]]]
tf.slice(t, [1, 0, 0], [2, 1, 3]) # [[[3, 3, 3]],
# [[5, 5, 5]]]
Args  

input_

A Tensor .

begin

An int32 or int64 Tensor .

size

An int32 or int64 Tensor .

name

A name for the operation (optional). 
Returns  

A Tensor the same type as input_ .
