tf.math.add

View source on GitHub

Returns x + y element-wise.

View aliases

Main aliases

tf.add

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.add

tf.math.add(
    x, y, name=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
TF-NumPy Type Promotion Distributed training with Core APIs and DTensor Logistic regression for binary classification with Core APIs Multilayer perceptrons for digit recognition with Core APIs Quickstart for the TensorFlow Core APIs	Customization basics: tensors and operations Introduction to Fairness Indicators Building Your Own Federated Learning Algorithm Custom Federated Algorithms, Part 1: Introduction to the Federated Core Client-efficient large-model federated learning via `federated_select` and sparse aggregation

Example usages below.

Add a scalar and a list:

x = [1, 2, 3, 4, 5]
y = 1
tf.add(x, y)
<tf.Tensor: shape=(5,), dtype=int32, numpy=array([2, 3, 4, 5, 6],
dtype=int32)>

Note that binary + operator can be used instead:

x = tf.convert_to_tensor([1, 2, 3, 4, 5])
y = tf.convert_to_tensor(1)
x + y
<tf.Tensor: shape=(5,), dtype=int32, numpy=array([2, 3, 4, 5, 6],
dtype=int32)>

Add a tensor and a list of same shape:

x = [1, 2, 3, 4, 5]
y = tf.constant([1, 2, 3, 4, 5])
tf.add(x, y)
<tf.Tensor: shape=(5,), dtype=int32,
numpy=array([ 2,  4,  6,  8, 10], dtype=int32)>

For example,

x = tf.constant([1, 2], dtype=tf.int8)
y = [2**7 + 1, 2**7 + 2]
tf.add(x, y)
<tf.Tensor: shape=(2,), dtype=int8, numpy=array([-126, -124], dtype=int8)>

When adding two input values of different shapes, Add follows NumPy broadcasting rules. The two input array shapes are compared element-wise. Starting with the trailing dimensions, the two dimensions either have to be equal or one of them needs to be 1.

For example,

x = np.ones(6).reshape(1, 2, 1, 3)
y = np.ones(6).reshape(2, 1, 3, 1)
tf.add(x, y).shape.as_list()
[2, 2, 3, 3]

Another example with two arrays of different dimension.

x = np.ones([1, 2, 1, 4])
y = np.ones([3, 4])
tf.add(x, y).shape.as_list()
[1, 2, 3, 4]

The reduction version of this elementwise operation is tf.math.reduce_sum

Args
x	A tf.Tensor. Must be one of the following types: bfloat16, half, float16, float32, float64, uint8, uint16, uint32, uint64, int8, int16, int32, int64, complex64, complex128, string.
y	A tf.Tensor. Must have the same type as x.
name	A name for the operation (optional)