tf.tensor_scatter_nd_sub

Defined in generated file: python/ops/gen_array_ops.py

Subtracts sparse updates from an existing tensor according to indices.

Aliases:

  • tf.compat.v1.tensor_scatter_nd_sub
  • tf.compat.v1.tensor_scatter_sub
  • tf.compat.v2.tensor_scatter_nd_sub
tf.tensor_scatter_nd_sub(
    tensor,
    indices,
    updates,
    name=None
)

This operation creates a new tensor by subtracting sparse updates from the passed in tensor. This operation is very similar to tf.scatter_nd_sub, except that the updates are subtracted from an existing tensor (as opposed to a variable). If the memory for the existing tensor cannot be re-used, a copy is made and updated.

indices is an integer tensor containing indices into a new tensor of shape shape. The last dimension of indices can be at most the rank of shape:

indices.shape[-1] <= shape.rank

The last dimension of indices corresponds to indices into elements (if indices.shape[-1] = shape.rank) or slices (if indices.shape[-1] < shape.rank) along dimension indices.shape[-1] of shape. updates免费成长做爱直播有哪些 is a tensor with shape

indices.shape[:-1] + shape[indices.shape[-1]:]

The simplest form of tensor_scatter_sub is to subtract individual elements from a tensor by index. For example, say we want to insert 4 scattered elements in a rank-1 tensor with 8 elements.

免费成长做爱直播有哪些In Python, this scatter subtract operation would look like this:

    indices = tf.constant([[4], [3], [1], [7]])
    updates = tf.constant([9, 10, 11, 12])
    tensor = tf.ones([8], dtype=tf.int32)
    updated = tf.tensor_scatter_sub(tensor, indices, updates)
    with tf.Session() as sess:
      print(sess.run(scatter))

The resulting tensor would look like this:

[1, -10, 1, -9, -8, 1, 1, -11]

We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted to insert two slices in the first dimension of a rank-3 tensor with two matrices of new values.

免费成长做爱直播有哪些In Python, this scatter add operation would look like this:

    indices = tf.constant([[0], [2]])
    updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
                            [7, 7, 7, 7], [8, 8, 8, 8]],
                           [[5, 5, 5, 5], [6, 6, 6, 6],
                            [7, 7, 7, 7], [8, 8, 8, 8]]])
    tensor = tf.ones([4, 4, 4])
    updated = tf.tensor_scatter_sub(tensor, indices, updates)
    with tf.Session() as sess:
      print(sess.run(scatter))

The resulting tensor would look like this:

[[[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]],
 [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
 [[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]],
 [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]]

免费成长做爱直播有哪些Note that on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, the index is ignored.

Args:

  • tensor: A Tensor. Tensor to copy/update.
  • indices: A Tensor. Must be one of the following types: int32, int64. Index tensor.
  • updates: A Tensor. Must have the same type as tensor. Updates to scatter into output.
  • name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as tensor.

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