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TensorFlow 1 version
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View source on GitHub
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Extracts a slice from a tensor.
tf.slice(
input_, begin, size, name=None
)
Used in the notebooks
| Used in the tutorials |
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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 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]]]
Args | |
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input_
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A Tensor.
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begin
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An int32 or int64 Tensor.
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size
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An int32 or int64 Tensor.
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name
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A name for the operation (optional). |
Returns | |
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A Tensor the same type as input_.
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