# cosine_embedding_loss¶

paddle.nn.functional. cosine_embedding_loss ( input1, input2, label, margin=0, reduction='mean', name=None ) [source]

Compute the cosine embedding loss of Tensor input1, input2 and label as follows.

If label = 1, then the loss value can be calculated as follow:

$Out = 1 - cos(input1, input2)$

If label = -1, then the loss value can be calculated as follow:

$Out = max(0, cos(input1, input2)) - margin$
The operator cos can be described as follow:
$cos(x1, x2) = \frac{x1 \cdot{} x2}{\Vert x1 \Vert_2 * \Vert x2 \Vert_2}$
Parameters
• input1 (Tensor) – tensor with shape: [N, M] or [M], ‘N’ means batch size, which can be 0, ‘M’ means the length of input array. Available dtypes are float32, float64.

• input2 (Tensor) – tensor with shape: [N, M] or [M], ‘N’ means batch size, which can be 0, ‘M’ means the length of input array. Available dtypes are float32, float64.

• label (Tensor) – tensor with shape: [N] or [1], ‘N’ means the length of input array. The target labels values should be -1 or 1. Available dtypes are int32, int64, float32, float64.

• margin (float, optional) – Should be a number from $$-1$$ to $$1$$, $$0$$ to $$0.5$$ is suggested. If margin is missing, the default value is $$0$$.

• reduction (string, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output 'sum': the output will be summed.

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

Returns

Tensor, the cosine embedding Loss of Tensor input1 input2 and label.

If reduction is 'none', the shape of output loss is [N], the same as input . If reduction is 'mean' or 'sum', the shape of output loss is [].

Examples

>>> import paddle

>>> input1 = paddle.to_tensor([[1.6, 1.2, -0.5], [3.2, 2.6, -5.8]], 'float32')
>>> input2 = paddle.to_tensor([[0.5, 0.5, -1.8], [2.3, -1.4, 1.1]], 'float32')
>>> label = paddle.to_tensor([1, -1], 'int64')

>>> output = paddle.nn.functional.cosine_embedding_loss(input1, input2, label, margin=0.5, reduction='mean')
>>> print(output)  # 0.21155193