mindspore.ops.BinaryCrossEntropy
- class mindspore.ops.BinaryCrossEntropy(reduction='mean')[source]
Computes the binary cross entropy between the logits and the labels.
Sets logits as \(x\), labels as \(y\), output as \(\ell(x, y)\). Let,
\[L = \{l_1,\dots,l_N\}^\top, \quad l_n = - w_n \left[ y_n \cdot \log x_n + (1 - y_n) \cdot \log (1 - x_n) \right]\]In which, \(L\) indicates the loss of all batch_sizes, \(l\) indicates the loss of one batch_size, and n indicates one batch_size in the 1-N range. Then,
\[\begin{split}\ell(x, y) = \begin{cases} L, & \text{if reduction} = \text{'none';}\\ \operatorname{mean}(L), & \text{if reduction} = \text{'mean';}\\ \operatorname{sum}(L), & \text{if reduction} = \text{'sum'.} \end{cases}\end{split}\]Warning
The value of “x” must range from 0 to 1.
The value of “y” must be “0” or “1”.
- Parameters
reduction (str) – Specifies the reduction to be applied to the output. Its value must be one of ‘none’, ‘mean’ or ‘sum’. Default: ‘mean’.
- Inputs:
logits (Tensor) - The input Tensor. The data type must be float16 or float32, The shape is \((N, *)\) where \(*\) means, any number of additional dimensions.
labels (Tensor) - The label Tensor which has the same shape and data type as logits.
weight (Tensor, optional) - A rescaling weight applied to the loss of each batch element. And it must have the same shape and data type as logits. Default: None.
- Outputs:
Tensor or Scalar, if reduction is ‘none’, then output is a tensor and has the same shape as logits. Otherwise, the output is a scalar.
- Raises
TypeError – If dtype of logits, labels or weight (if given) is neither float16 nor float32.
ValueError – If reduction is not one of ‘none’, ‘mean’ or ‘sum’.
ValueError – If shape of labels is not the same as logits or weight (if given).
TypeError – If logits, labels or weight is not a Tensor.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self.binary_cross_entropy = ops.BinaryCrossEntropy() ... def construct(self, logits, labels, weight): ... result = self.binary_cross_entropy(logits, labels, weight) ... return result ... >>> net = Net() >>> logits = Tensor(np.array([0.2, 0.7, 0.1]), mindspore.float32) >>> labels = Tensor(np.array([0., 1., 0.]), mindspore.float32) >>> weight = Tensor(np.array([1, 2, 2]), mindspore.float32) >>> output = net(logits, labels, weight) >>> print(output) 0.38240486