mindspore.nn.RMSELoss
- class mindspore.nn.RMSELoss[source]
RMSELoss creates a criterion to measure the root mean square error between \(x\) and \(y\) element-wise, where \(x\) is the input and \(y\) is the labels.
For simplicity, let \(x\) and \(y\) be 1-dimensional Tensor with length \(N\), the loss of \(x\) and \(y\) is given as:
\[loss = \sqrt{\frac{1}{N}\sum_{i=1}^{N}{(x_i-y_i)^2}}\]- Inputs:
logits (Tensor) - Tensor of shape \((N, *)\) where \(*\) means, any number of additional dimensions.
labels (Tensor) - Tensor of shape \((N, *)\), same shape as the logits in common cases. However, it supports the shape of logits is different from the shape of labels and they should be broadcasted to each other.
- Outputs:
Tensor, weighted loss float tensor and its shape is zero.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> # Case 1: logits.shape = labels.shape = (3,) >>> loss = nn.RMSELoss() >>> logits = Tensor(np.array([1, 2, 3]), mindspore.float32) >>> labels = Tensor(np.array([1, 2, 2]), mindspore.float32) >>> output = loss(logits, labels) >>> print(output) 0.57735026 >>> # Case 2: logits.shape = (3,), labels.shape = (2, 3) >>> loss = nn.RMSELoss() >>> logits = Tensor(np.array([1, 2, 3]), mindspore.float32) >>> labels = Tensor(np.array([[1, 1, 1], [1, 2, 2]]), mindspore.float32) >>> output = loss(logits, labels) >>> print(output) 1.0