tf.slice( input_, begin, size, name=None )
Defined in tensorflow/python/ops/array_ops.py
.
See the guide: Tensor Transformations > Slicing and Joining
Extracts a slice from a tensor.
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 zero-based; size
is one-based. 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]]]
input_
: A Tensor
.begin
: An int32
or int64
Tensor
.size
: An int32
or int64
Tensor
.name
: A name for the operation (optional).A Tensor
the same type as input
.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/slice