# elementwise_mod¶

paddle.fluid.layers.nn. elementwise_mod ( x, y, axis=- 1, act=None, name=None ) [source]

Elementwise Mod Operator.

Mod two tensors element-wise

The equation is:

\(Out = X \\% Y\)

• \$X\$: a tensor of any dimension.

• \$Y\$: a tensor whose dimensions must be less than or equal to the dimensions of \$X\$.

There are two cases for this operator:

1. The shape of \$Y\$ is the same with \$X\$.

2. The shape of \$Y\$ is a continuous subsequence of \$X\$.

For case 2:

1. Broadcast \$Y\$ to match the shape of \$X\$, where \$axis\$ is the start dimension index for broadcasting \$Y\$ onto \$X\$.

2. If \$axis\$ is -1 (default), \$axis = rank(X) - rank(Y)\$.

3. The trailing dimensions of size 1 for \$Y\$ will be ignored for the consideration of subsequence, such as shape(Y) = (2, 1) => (2).

For example:

```shape(X) = (2, 3, 4, 5), shape(Y) = (,)
shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5), with axis=-1(default) or axis=2
shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0
```
Parameters
• x (Tensor) – (Tensor), Tensor of any dimensions. Its dtype should be int32, int64, float32 or float64.

• y (Tensor) – (Tensor), Tensor of any dimensions. Its dtype should be int32, int64, float32 or float64.

• with_quant_attr (BOOLEAN) – Whether the operator has attributes used by quantization.

• axis (int32, optional) – If X.dimension != Y.dimension, Y.dimension must be a subsequence of x.dimension. And axis is the start dimension index for broadcasting Y onto X.

• act (string, optional) – Activation applied to the output. Default is None. Details: Activation Function

• name (string, optional) – Name of the output. Default is None. It’s used to print debug info for developers. Details: Name

Returns

N-dimension tensor. A location into which the result is stored. It’s dimension equals with x

Examples

```import paddle.fluid as fluid
import numpy as np

def gen_data():
return {
"x": np.array([10, 15, 8]).astype('int32'),
"y": np.array([3, 6, 5]).astype('int32')
}
x = fluid.data(name="x", shape=[3], dtype='int32')
y = fluid.data(name="y", shape=[3], dtype='int32')
z = fluid.layers.elementwise_mod(x, y)

place = fluid.CPUPlace()
exe = fluid.Executor(place)
z_value = exe.run(feed=gen_data(),
fetch_list=[z.name])

print(z_value) #[1, 3, 3]
```
Return type

out (Tensor)