Function strided_slice

tensorflow/python/ops/array_ops.py:860–996

Extracts a strided slice of a tensor (generalized python array indexing). **Instead of calling this op directly most users will want to use the NumPy-style slicing syntax (e.g. `tensor[..., 3:4:-1, tf.newaxis, 3]`), which is supported via `tf.Tensor.__getitem__` and `tf.Variable.__getitem__`.

(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)

# pylint: disable=invalid-name
@tf_export("strided_slice")
def 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):
  """Extracts a strided slice of a tensor (generalized python array indexing).

  **Instead of calling this op directly most users will want to use the
  NumPy-style 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 low-level encoding of the slicing syntax.

  Roughly speaking, this op extracts a slice of size `(end-begin)/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
  non-zero 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