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Returns x + y element-wise.
tf.math.add( x, y, name=None )
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, float32, float64, uint8, 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) |
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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/math/add