MSELoss¶
-
class
paddle.fluid.dygraph.
MSELoss
(reduction='mean')[source] Mean Square Error Loss Computes the mean square error (squared L2 norm) of given input and label.
If
reduction
is set to'none'
, loss is calculated as:\[Out = (input - label)^2\]If
reduction
is set to'mean'
, loss is calculated as:\[Out = \operatorname{mean}((input - label)^2)\]If
reduction
is set to'sum'
, loss is calculated as:\[Out = \operatorname{sum}((input - label)^2)\]where input and label are float32 tensors of same shape.
- Parameters
input (Variable) – Input tensor, the data type is float32,
label (Variable) – Label tensor, the data type is float32,
reduction (string, optional) – The reduction method for the output, could be ‘none’ | ‘mean’ | ‘sum’. If
reduction
is'mean'
, the reduced mean loss is returned. Ifsize_average
is'sum'
, the reduced sum loss is returned. Ifreduction
is'none'
, the unreduced loss is returned. Default is'mean'
.
- Returns
The tensor variable storing the MSE loss of input and label.
- Return type:
Variable.
Examples
import numpy as np from paddle import fluid import paddle.fluid.dygraph as dg mse_loss = fluid.dygraph.MSELoss() input = fluid.data(name="input", shape=[1]) label = fluid.data(name="label", shape=[1]) place = fluid.CPUPlace() input_data = np.array([1.5]).astype("float32") label_data = np.array([1.7]).astype("float32") # declarative mode output = mse_loss(input,label) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) output_data = exe.run( fluid.default_main_program(), feed={"input":input_data, "label":label_data}, fetch_list=[output], return_numpy=True) print(output_data) # [array([0.04000002], dtype=float32)] # imperative mode with dg.guard(place) as g: input = dg.to_variable(input_data) label = dg.to_variable(label_data) output = mse_loss(input, label) print(output.numpy()) # [0.04000002]
-
forward
(input, label) Defines the computation performed at every call. Should be overridden by all subclasses.
- Parameters
*inputs (tuple) – unpacked tuple arguments
**kwargs (dict) – unpacked dict arguments