chainer.Function¶

class
chainer.
Function
[source]¶ Oldstyle interface of a differentiable function.
This class provides an interface to implement an oldstyle differentiable function (i.e., the function application is recorded to the computational graph). The subclass of
Function
that implementforward()
andbackward()
can be used to run the forward computation and automatically induce the backpropagation procedure.There is another way to implement such a function: subclassing
FunctionNode
. There are mainly two differences between them. The differentiable backprop is available for
FunctionNode
, while it is not forFunction
because thebackward()
of the latter directly operates on the arrays instead ofVariable
objects so that it cannot record the history of the computation.  The information passed to
backward()
is different. InFunctionNode
, which inputs the function node has to compute the gradients w.r.t. is passed so that it can omit unnecessary computations, whileFunction
always has to compute gradients w.r.t. all the input nodes. TheFunctionNode
also accepts the current gradient values of the input nodes so that the accumulation work can be merged with the gradient computation if an efficient kernel is available.
This class uses
FunctionAdapter
to convert the interface to that ofFunctionNode
and adds theFunctionNode
object to the computational graph.See
FunctionNode
for the details of building the computational graph in Chainer.Methods

__call__
(*inputs)[source]¶ Applies forward propagation with chaining backward references.
This method creates a new
FunctionAdapter
object and runs the forward propagation using it.See
FunctionNode
for the detailed behavior of building the computational graph.Parameters: inputs – Tuple of input Variable
,numpy.ndarray
orcupy.ndarray
objects. If the input is annumpy.ndarray
or acupy.ndarray
, it is automatically wrapped withVariable
.Returns: One Variable
object or a tuple of multipleVariable
objects.

add_hook
(hook, name=None)[source]¶ Registers a function hook.
See
FunctionNode.add_hook()
for the detail.Parameters:  hook (FunctionHook) – Function hook to be registered.
 name (str) – Name of the function hook.
name must be unique among function hooks
registered to the function. If
None
, default name of the function hook is used.

backward
(inputs, grad_outputs)[source]¶ Applies backprop to output gradient arrays.
It delegates the procedure to
backward_cpu()
orbackward_gpu()
by default. Which it selects is determined by the type of input arrays and output gradient arrays. Implementations ofFunction
must implement either CPU/GPU methods or this method, if the function is intended to be backproped.Parameters:  inputs – Tuple of input arrays.
 grad_outputs – Tuple of output gradient arrays.
Returns: Tuple of input gradient arrays. Some or all of them can be
None
, if the function is not differentiable on inputs.Return type: Warning
Implementations of
Function
must take care that the return value must be a tuple even if it returns only one array.

backward_cpu
(inputs, grad_outputs)[source]¶ Applies backprop to output gradient arrays on CPU.
Parameters:  inputs – Tuple of input
numpy.ndarray
object(s).  grad_outputs – Tuple of output gradient
numpy.ndarray
object(s).
Returns: Tuple of input gradient
numpy.ndarray
object(s). Some or all of them can beNone
, if the function is not differentiable on corresponding inputs.Return type: Warning
Implementations of
Function
must take care that the return value must be a tuple even if it returns only one array. inputs – Tuple of input

backward_gpu
(inputs, grad_outputs)[source]¶ Applies backprop to output gradient arrays on GPU.
Parameters:  inputs – Tuple of input
cupy.ndarray
object(s).  grad_outputs – Tuple of output gradient
cupy.ndarray
object(s).
Returns: Tuple of input gradient
cupy.ndarray
object(s). Some or all of them can beNone
, if the function is not differentiable on corresponding inputs.Return type: Warning
Implementations of
Function
must take care that the return value must be a tuple even if it returns only one array. inputs – Tuple of input

check_type_forward
(in_types)[source]¶ Checks types of input data before forward propagation.
Before
forward()
is called, this function is called. You need to validate types of input data in this function using the type checking utilities.Parameters: in_types (TypeInfoTuple) – The type information of input data for forward()
.

delete_hook
(name)[source]¶ Unregisters the specified function hook.
Parameters: name (str) – the name of the function hook to be unregistered.

forward
(inputs)[source]¶ Applies forward propagation to input arrays.
It delegates the procedure to
forward_cpu()
orforward_gpu()
by default. Which it selects is determined by the type of input arrays. Implementations ofFunction
must implement either CPU/GPU methods or this method.Parameters: inputs – Tuple of input array(s). Returns: Tuple of output array(s). Warning
Implementations of
Function
must take care that the return value must be a tuple even if it returns only one array.

forward_cpu
(inputs)[source]¶ Applies forward propagation to input arrays on CPU.
Parameters: inputs – Tuple of numpy.ndarray
object(s).Returns: Tuple of numpy.ndarray
object(s).Return type: tuple Warning
Implementations of
Function
must take care that the return value must be a tuple even if it returns only one array.

forward_gpu
(inputs)[source]¶ Applies forward propagation to input arrays on GPU.
Parameters: inputs – Tuple of cupy.ndarray
object(s).Returns: Tuple of cupy.ndarray
object(s).Return type: tuple Warning
Implementations of
Function
must take care that the return value must be a tuple even if it returns only one array.

retain_inputs
(indexes)[source]¶ Lets specified input variable nodes keep data arrays.
By calling this method from
forward()
, the function can specify which inputs are required for backprop.If this method is not called, the function keeps all input arrays. If you want to release all input arrays, call this method by passing an empty sequence. Note that this behavior is different from that of
FunctionNode.retain_inputs()
.Note that this method must not be called from the outside of
forward()
.Parameters: indexes (iterable of int) – Indexes of input variables that the function will require for backprop.

retain_outputs
(indexes, retain_after_backward=False)[source]¶ Lets specified output variable nodes keep data arrays.
By calling this method from
forward()
, the function can specify which outputs are required for backprop. If this method is not called, any output variables are not marked to keep the data array at the point of returning from__call__()
. The retained arrays are stored tooutput_data
.Note
It is STRONGLY RECOMMENDED to use this method if the function requires some or all output arrays in backprop. The function can also use output arrays just by keeping references to them directly, whereas it might influence on the performance of later function applications to the output variables.
Note that this method must not be called from the outside of
forward()
.Parameters:  indexes (iterable of int) – Indexes of input variables that the function will require for backprop.
 retain_after_backward (bool) – This option has no effect. It is left only for the backward compatibility.

unchain
()[source]¶ Purges in/out nodes and this function itself from the graph.
See
FunctionNode.unchain()
for the detail.
 The differentiable backprop is available for