Warning: This API is deprecated and will be removed in a future version of TensorFlow after the replacement is stable.

StridedSlice.Options

public static class StridedSlice.Options

Optional attributes for StridedSlice

Public Methods

StridedSlice.Options
beginMask(Long beginMask)
StridedSlice.Options
ellipsisMask(Long ellipsisMask)
StridedSlice.Options
endMask(Long endMask)
StridedSlice.Options
newAxisMask(Long newAxisMask)
StridedSlice.Options
shrinkAxisMask(Long shrinkAxisMask)

Inherited Methods

Public Methods

public StridedSlice.Options beginMask (Long beginMask)

Parameters
beginMask a bitmask where a bit i being 1 means to ignore the begin value and instead use the largest interval possible. At runtime begin[i] will be replaced with `[0, n-1)` if `stride[i] > 0` or `[-1, n-1]` if `stride[i] < 0`

public StridedSlice.Options ellipsisMask (Long ellipsisMask)

Parameters
ellipsisMask a bitmask where bit `i` being 1 means the `i`th position is actually an ellipsis. One bit at most can be 1. If `ellipsis_mask == 0`, then an implicit ellipsis mask of `1 << (m+1)` is provided. This means that `foo[3:5] == foo[3:5, ...]`. An ellipsis implicitly creates as many range specifications as necessary to fully specify the sliced range for every dimension. For example for a 4-dimensional tensor `foo` the slice `foo[2, ..., 5:8]` implies `foo[2, :, :, 5:8]`.

public StridedSlice.Options endMask (Long endMask)

Parameters
endMask analogous to `begin_mask`

public StridedSlice.Options newAxisMask (Long newAxisMask)

Parameters
newAxisMask a bitmask where bit `i` being 1 means the `i`th specification creates a new shape 1 dimension. For example `foo[:4, tf.newaxis, :2]` would produce a shape `(4, 1, 2)` tensor.

public StridedSlice.Options shrinkAxisMask (Long shrinkAxisMask)

Parameters
shrinkAxisMask a bitmask where bit `i` implies that the `i`th specification should shrink the dimensionality. begin and end must imply a slice of size 1 in the dimension. For example in python one might do `foo[:, 3, :]` which would result in `shrink_axis_mask` being 2.