oneflow.nn.MSELoss¶

class oneflow.nn.MSELoss(reduction: str = 'mean')¶

Creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input \(x\) and target \(y\).

The unreduced (i.e. with reduction set to 'none') loss can be described as:

\[\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad l_n = \left( x_n - y_n \right)^2,\]

where \(N\) is the batch size. If reduction is not 'none' (default 'mean'), then:

\[\begin{split}\ell(x, y) = \begin{cases} \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} \end{cases}\end{split}\]

\(x\) and \(y\) are tensors of arbitrary shapes with a total of \(n\) elements each.

The mean operation still operates over all the elements, and divides by \(n\).

The division by \(n\) can be avoided if one sets reduction = 'sum'.

The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.MSELoss.html.

Parameters: 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. Default: 'mean'

Shape:

Input: \((N, *)\) where \(*\) means, any number of additional dimensions
Target: \((N, *)\), same shape as the input

For example:

>>> import oneflow as flow
>>> import numpy as np
>>> input = flow.tensor(
... [[-0.02557137, 0.03101675, 1.37493674],
... [0.25599439, -1.08372561, -0.21006816]], dtype=flow.float32)
>>> target = flow.tensor(
... [[-1.53105064, -0.68137555, 0.5931354],
... [-0.49158347, 0.93673637, 0.1324141]], dtype=flow.float32)
>>> m = flow.nn.MSELoss(reduction="none")
>>> out = m(input, target)
>>> out
tensor([[2.2665, 0.5075, 0.6112],
        [0.5589, 4.0823, 0.1173]], dtype=oneflow.float32)
>>> m = flow.nn.MSELoss(reduction="mean")
>>> out = m(input, target)
>>> out
tensor(1.3573, dtype=oneflow.float32)
>>> m = flow.nn.MSELoss(reduction="sum")
>>> out = m(input, target)
>>> out
tensor(8.1436, dtype=oneflow.float32)

__init__(reduction: str = 'mean') → None ¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__call__`(args, *kwargs)	Call self as a function.
`__delattr__`(name, /)	Implement delattr(self, name).
`__dir__`()	Default dir() implementation.
`__eq__`(value, /)	Return self==value.
`__format__`(format_spec, /)	Default object formatter.
`__ge__`(value, /)	Return self>=value.
`__getattr__`(name)
`__getattribute__`(name, /)	Return getattr(self, name).
`__gt__`(value, /)	Return self>value.
`__hash__`()	Return hash(self).
`__init__`([reduction])	Initialize self.
`__init_subclass__`	This method is called when a class is subclassed.
`__le__`(value, /)	Return self<=value.
`__lt__`(value, /)	Return self<value.
`__ne__`(value, /)	Return self!=value.
`__new__`(**kwargs)	Create and return a new object.
`__reduce__`()	Helper for pickle.
`__reduce_ex__`(protocol, /)	Helper for pickle.
`__repr__`()	Return repr(self).
`__setattr__`(name, value)	Implement setattr(self, name, value).
`__sizeof__`()	Size of object in memory, in bytes.
`__str__`()	Return str(self).
`__subclasshook__`	Abstract classes can override this to customize issubclass().
`_apply`(fn[, applied_dict])
`_get_name`()
`_load_from_state_dict`(state_dict, prefix, …)
`_named_members`(get_members_fn[, prefix, recurse])
`_save_to_state_dict`(destination, prefix, …)
`_shallow_repr`()
`add_module`(name, module)	Adds a child module to the current module.
`apply`(fn)	Applies `fn` recursively to every submodule (as returned by `.children()`) as well as self.
`buffers`([recurse])	Returns an iterator over module buffers.
`children`()	Returns an iterator over immediate children modules.
`cpu`()	Moves all model parameters and buffers to the CPU.
`cuda`([device])	Moves all model parameters and buffers to the GPU.
`double`()	Casts all floating point parameters and buffers to `double` datatype.
`eval`()	Sets the module in evaluation mode.
`extra_repr`()	Set the extra representation of the module
`float`()	Casts all floating point parameters and buffers to `float` datatype.
`forward`(input, target)
`half`()	Casts all floating point parameters and buffers to `half` datatype.
`load_state_dict`(state_dict[, strict])	Copies parameters and buffers from `state_dict` into this module and its descendants.
`modules`()	Returns an iterator over all modules in the network.
`named_buffers`([prefix, recurse])	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
`named_children`()	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
`named_modules`([memo, prefix])	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
`named_parameters`([prefix, recurse])	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
`parameters`([recurse])	Returns an iterator over module parameters.
`register_buffer`(name, tensor[, persistent])	Adds a buffer to the module.
`register_forward_hook`(hook)	Registers a forward hook on the module.
`register_forward_pre_hook`(hook)	Registers a forward pre-hook on the module.
`register_parameter`(name, param)	Adds a parameter to the module.
`state_dict`([destination, prefix, keep_vars])	Returns a dictionary containing a whole state of the module.
`to`([device])	Moves the parameters and buffers.
`to_consistent`(args, *kwargs)	This interface is no longer available, please use `oneflow.nn.Module.to_global()` instead.
`to_global`([placement, sbp])	Convert the parameters and buffers to global.
`train`([mode])	Sets the module in training mode.
`zero_grad`([set_to_none])	Sets gradients of all model parameters to zero.