Extracts a slice from a tensor.

# Aliases:

• tf.compat.v1.slice
• tf.compat.v2.slice

 tf.slice(
    input_,
    begin,
    size,
    name=None
)

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. tf.Tensor.getitemNote that 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:

• input_: A Tensor.
• begin: An int32 or int64 Tensor.
• size: An int32 or int64 Tensor.
• name: A name for the operation (optional).

# Returns:

A Tensor the same type as input_.