MeanSquaredError¶
- class dragon.vm.tensorflow.keras.losses.MeanSquaredError(
 reduction='mean',
 name=None
 )[source]¶
- A criterion to compute the reduced element-wise squared error. - The SquaredError function is defined as: \[\text{SquaredError}(y_{true}, y_{pred}) = (y_{pred} - y_{true})^{2} \]- Examples: - criterion = tf.keras.cost.MeanSquaredError() y_true = tf.constant([1., 2., 3.]) y_pred = tf.constant([0., 0., 0.]) print(criterion(y_true, y_pred)) # 4.666666 
__init__¶
Methods¶
__call__¶
- Loss.- __call__(
 y_true,
 y_pred
 )[source]
- Compute the defined loss function. - Parameters:
- y_true (dragon.Tensor) – The ground-truth tensor.
- y_pred (dragon.Tensor) – The logits tensor.
 
 - Returns:
- dragon.Tensor – The loss. 
 
