CosineEmbeddingLoss

class paddle.nn. CosineEmbeddingLoss ( margin=0, reduction='mean', name=None ) [source]

This interface is used to construct a callable object of the CosineEmbeddingLoss class. The CosineEmbeddingLoss layer measures the cosine_embedding loss between input predictions input1, input2 and target labels label with values 1 or 0. This is used for measuring whether two inputs are similar or dissimilar and is typically used for learning nonlinear embeddings or semi-supervised learning. The cosine embedding loss can be described as:

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

  • 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.

Shape:
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.

output (Tensor): 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')

>>> cosine_embedding_loss = paddle.nn.CosineEmbeddingLoss(margin=0.5, reduction='mean')
>>> output = cosine_embedding_loss(input1, input2, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.21155193)

>>> cosine_embedding_loss = paddle.nn.CosineEmbeddingLoss(margin=0.5, reduction='sum')
>>> output = cosine_embedding_loss(input1, input2, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.42310387)

>>> cosine_embedding_loss = paddle.nn.CosineEmbeddingLoss(margin=0.5, reduction='none')
>>> output = cosine_embedding_loss(input1, input2, label)
>>> print(output)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.42310387, 0.        ])
forward ( input1, input2, label )

forward

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

  • *inputs (tuple) – unpacked tuple arguments

  • **kwargs (dict) – unpacked dict arguments