chainer.functions.forget¶

chainer.functions.
forget
(func, *xs)[source]¶ Calls a function without storing intermediate results.
On a forward propagation, Chainer normally stores all intermediate results of
VariableNode
s on a computational graph as they are required on backward propagation. Sometimes these results consume too much memory.F.forget
forgets such intermediate results on forward propagation, and still supports backpropagation with recalculation.On a forward propagation,
F.forget
calls a given function with given variables without creating a computational graph. That means, no intermediate results are stored. On a backward propagation,F.forget
calls the given function again to create a computational graph for backpropagation.F.forget
reduces internal memory usage, whereas it requires more calculation time as it calls the function twice.Example
Let
f
be a function defined as:>>> def f(a, b): ... return a + b + a
and,
x
andy
beVariable
s:>>> x = chainer.Variable(np.random.uniform(1, 1, 5).astype('f')) >>> y = chainer.Variable(np.random.uniform(1, 1, 5).astype('f'))
When
z
is calculated asz = f(x, y)
, its intermediate resultx + y
is stored in memory. Instead, if you callf
withF.forget
:>>> z = F.forget(f, x, y)
intermediate
x + y
is forgotten.Note
F.forget
does not support functions which behave differently in multiple calls with the same inputs, such asF.dropout()
andF.negative_sampling()
.Parameters: Returns: A variable
func
returns. If it returns a tuple, the method returns a tuple too.Return type: