Returns an element-wise x * y.

For example:

x = tf.constant(([1, 2, 3, 4]))
tf.math.multiply(x, x)
<tf.Tensor: shape=(4,), dtype=..., numpy=array([ 1,  4,  9, 16], dtype=int32)>

Since tf.math.multiply will convert its arguments to Tensors, you can also pass in non-Tensor arguments:

<tf.Tensor: shape=(), dtype=int32, numpy=42>

If x.shape is not the same as y.shape, they will be broadcast to a compatible shape. (More about broadcasting here.)

For example:

x = tf.ones([1, 2]);
y = tf.ones([2, 1]);
x * y  # Taking advantage of operator overriding
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[1., 1.],
     [1., 1.]], dtype=float32)>

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

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

A Tensor. Has the same type as x.

  • InvalidArgumentError: When x and y have incompatible shapes or types.