multiply¶
- paddle. multiply ( x, y, name=None ) [source]
-
multiply two tensors element-wise. The equation is:
\[out = x * y\]Note
Supported shape of
x
andy
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 float32, float64, int32, int64, bool.
y (Tensor) – the input tensor, its data type should be one of float32, float64, int32, int64, bool.
name (str, 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 ofx
andy
after broadcasting. Ifx
,y
have the same shape, its shape is the same asx
andy
.
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]]])