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paddle. multiply ( x: Tensor, y: Tensor, name: str | None = None ) → Tensor [source]
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multiply two tensors element-wise. The equation is:

\[out = x * y\]

Note

Supported shape of x and y for this operator: 1. x.shape == y.shape. 2. x.shape could be the continuous subsequence of y.shape. paddle.multiply supports broadcasting. If you would like to know more about broadcasting, please refer to Introduction to Tensor .

Parameters

  • x (Tensor) – the input tensor, its data type should be one of bfloat16, float16, float32, float64, int32, int64, bool, complex64, complex128.

  • y (Tensor) – the input tensor, its data type should be one of bfloat16, float16, float32, float64, int32, int64, bool, complex64, complex128.

  • name (str|None, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

N-D Tensor. A location into which the result is stored. If x, y have different shapes and are “broadcastable”, the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y.

Examples

>>> import paddle

>>> x = paddle.to_tensor([[1, 2], [3, 4]])
>>> y = paddle.to_tensor([[5, 6], [7, 8]])
>>> res = paddle.multiply(x, y)
>>> print(res)
Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True,
[[5 , 12],
 [21, 32]])
>>> x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]])
>>> y = paddle.to_tensor([2])
>>> res = paddle.multiply(x, y)
>>> print(res)
Tensor(shape=[1, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
[[[2, 4, 6],
  [2, 4, 6]]])