Google I/O is a wrap! Catch up on TensorFlow sessions

This operation creates a new tensor by adding sparse `updates` to the passed in `tensor`. This operation is very similar to `tf.scatter_nd_add`, except that the updates are added onto 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 `tensor.shape`. The last dimension of `indices` can be at most the rank of `tensor.shape`:

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

The last dimension of `indices` corresponds to indices into elements (if `indices.shape[-1] = tensor.shape.rank`) or slices (if `indices.shape[-1] < tensor.shape.rank`) along dimension `indices.shape[-1]` of `tensor.shape`. `updates` is a tensor with shape

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

The simplest form of tensor_scatter_add is to add individual elements to a tensor by index. For example, say we want to add 4 elements in a rank-1 tensor with 8 elements.

In Python, this scatter add operation would look like this:

``````indices = tf.constant([, , , ])
updates = tf.constant([9, 10, 11, 12])
tensor = tf.ones(, dtype=tf.int32)
print(updated)
``````
The resulting tensor would look like this:

[1, 12, 1, 11, 10, 1, 1, 13]

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.

In Python, this scatter add operation would look like this:

``````indices = tf.constant([, ])
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],dtype=tf.int32)
print(updated)
``````
The resulting tensor would look like this:

[[[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], [[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]]

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.

### Public Methods

 Output () Returns the symbolic handle of a tensor. static TensorScatterAdd ( Scope scope, Operand tensor, Operand indices, Operand updates) Factory method to create a class wrapping a new TensorScatterAdd operation. Output () A new tensor copied from tensor and updates added according to the indices.

## Public Methods

#### public Output <T> asOutput ()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

#### public static TensorScatterAdd <T> create ( Scope scope, Operand <T> tensor, Operand <U> indices, Operand <T> updates)

Factory method to create a class wrapping a new TensorScatterAdd operation.

##### Parameters
 scope current scope Tensor to copy/update. Index tensor. Updates to scatter into output.
##### Returns
• a new instance of TensorScatterAdd

#### public Output <T> output ()

A new tensor copied from tensor and updates added according to the indices.

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