cosine_embedding_loss¶

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

This operator computes 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, ‘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, ‘M’ means the length of input array.: Available dtypes are float32, float64.
label (Tensor): tensor with shape: [N] or [1]. 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 [1].

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]

output = paddle.nn.functional.cosine_embedding_loss(input1, input2, label, margin=0.5, reduction='sum')
print(output)  # [0.42310387]

output = paddle.nn.functional.cosine_embedding_loss(input1, input2, label, margin=0.5, reduction='none')
print(output)  # [0.42310387, 0.        ]

          