tf.strided_slice

TensorFlow 1 version

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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
)

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 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]]]