Assertion and Testing¶
Chainer provides some facilities to make debugging easy.
Type checking utilities¶
Function
uses a systematic type checking of the chainer.utils.type_check
module.
It enables users to easily find bugs of forward and backward implementations.
You can find examples of type checking in some function implementations.

class
chainer.utils.type_check.
Expr
(priority)[source]¶ Abstract syntax tree of an expression.
It represents an abstract syntax tree, and isn’t a value. You can get its actual value with
eval()
function, and get syntax representation with the__str__()
method. Each comparison operator (e.g.==
) generates a newExpr
object which represents the result of comparison between two expressions.Example
Let
x
andy
be instances ofExpr
, then>>> x = Variable(1, 'x') >>> y = Variable(1, 'y') >>> c = (x == y)
is also an instance of
Expr
. To evaluate and get its value, calleval()
method:>>> c.eval() True
Call
str
function to get a representation of the original equation:>>> str(c) 'x == y'
You can actually compare an expression with a value:
>>> (x == 1).eval() True
Note that you can’t use boolean operators such as
and
, as they try to cast expressions to boolean values:>>> z = Variable(1, 'z') >>> x == y and y == z # raises an error Traceback (most recent call last): RuntimeError: Don't convert Expr to bool. Please call Expr.eval method to evaluate expression.

chainer.utils.type_check.
expect
(*bool_exprs)[source]¶ Evaluates and tests all given expressions.
This function evaluates given boolean expressions in order. When at least one expression is evaluated as
False
, that means the given condition is not satisfied. You can check conditions with this function.Parameters: bool_exprs (tuple of Bool expressions) – Bool expressions you want to evaluate.

class
chainer.utils.type_check.
TypeInfo
(shape, dtype)[source]¶ Type information of an input/gradient array.
It contains type information of an array, such as the shape of array and the number of dimensions. This information is independent of CPU or GPU array.
Gradient checking utilities¶
Most function implementations are numerically tested by gradient checking.
This method computes numerical gradients of forward routines and compares their results with the corresponding backward routines.
It enables us to make the source of issues clear when we hit an error of gradient computations.
The chainer.gradient_check
module makes it easy to implement the gradient checking.

chainer.gradient_check.
check_backward
(func, x_data, y_grad, params=(), eps=0.001, atol=1e05, rtol=0.0001, no_grads=None, dtype=None)[source]¶ Test backward procedure of a given function.
This function automatically check backwardprocess of given function. For example, when you have a
Function
classMyFunc
, that gets two arguments and returns one value, you can make its test like this:>> def test_my_func(self): >> func = MyFunc() >> x1_data = xp.array(...) >> x2_data = xp.array(...) >> gy_data = xp.array(...) >> check_backward(func, (x1_data, x2_data), gy_data)
This method creates
Variable
objects withx_data
and callsfunc
with theVariable
s to get its result asVariable
. Then, it setsy_grad
array tograd
attribute of the result and callsbackward
method to get gradients of the inputs. To check correctness of the gradients, the function callsnumerical_grad()
to calculate numerically the gradients and compares the types of gradients withchainer.testing.assert_allclose()
. If input objects (x1_data
or/andx2_data
in this example) represent integer variables, their gradients are ignored.You can simplify a test when
MyFunc
gets only one argument:>> check_backward(func, x1_data, gy_data)
If
MyFunc
is a loss function which returns a zerodimensional array, passNone
togy_data
. In this case, it sets1
tograd
attribute of the result:>> check_backward(my_loss_func, (x1_data, x2_data), None)
If
MyFunc
returns multiple outputs, pass all gradients for outputs as a tuple:>> gy1_data = xp.array(...) >> gy2_data = xp.array(...) >> check_backward(func, x1_data, (gy1_data, gy2_data))
You can also test a
Link
. To check gradients of parameters of the link, set a tuple of the parameters toparams
arguments:>> check_backward(my_link, (x1_data, x2_data), gy_data, >> (my_link.W, my_link.b))
Note that
params
are notndarray
s, butVariables
s.Function objects are acceptable as
func
argument:>> check_backward(lambda x1, x2: f(x1, x2), >> (x1_data, x2_data), gy_data)
Note
func
is called many times to get numerical gradients for all inputs. This function doesn’t work correctly whenfunc
behaves randomly as it gets different gradients.Parameters:  func (callable) – A function which gets
Variable
s and returnsVariable
s.func
must returns a tuple ofVariable
s or oneVariable
. You can useFunction
object,Link
object or a function satisfying the condition.  x_data (ndarray or tuple of ndarrays) – A set of
ndarray
s to be passed tofunc
. Ifx_data
is onendarray
object, it is treated as(x_data,)
.  y_grad (ndarray or tuple of ndarrays or None) – A set of
ndarray
s representing gradients of returnvalues offunc
. Ify_grad
is onendarray
object, it is treated as(y_grad,)
. Iffunc
is a lossfunction,y_grad
should be set toNone
.  params (Variable) – A set of
Variable
s whose gradients are checked. Whenfunc
is aLink
object, set its parameters asparams
. Ifparams
is oneVariable
object, it is treated as(params,)
.  eps (float) – Epsilon value to be passed to
numerical_grad()
.  atol (float) – Absolute tolerance to be passed to
chainer.testing.assert_allclose()
.  rtol (float) – Relative tolerance to be passed to
chainer.testing.assert_allclose()
.  no_grads (list of bool) – Flag to skip variable for gradient assertion.
It should be same length as
x_data
.  dtype (dtype) –
x_data
andy_grad
are casted to this dtype when calculating numerical gradients. Only float types andNone
are allowed.
 See:
numerical_grad()
 func (callable) – A function which gets

chainer.gradient_check.
numerical_grad
(f, inputs, grad_outputs, eps=0.001)[source]¶ Computes numerical gradient by finite differences.
This function is used to implement gradient check. For usage example, see unit tests of
chainer.functions
.Parameters:  f (function) – Python function with no arguments that runs forward computation and returns the result.
 inputs (tuple of arrays) – Tuple of arrays that should be treated as inputs. Each element of them is slightly modified to realize numerical gradient by finite differences.
 grad_outputs (tuple of arrays) – Tuple of arrays that are treated as output gradients.
 eps (float) – Epsilon value of finite differences.
Returns: Numerical gradient arrays corresponding to
inputs
.Return type:
Standard Assertions¶
The assertions have same names as NumPy’s ones.
The difference from NumPy is that they can accept both numpy.ndarray
and cupy.ndarray
.
Function testing utilities¶
Chainer provides some utilities for testing its functions.

chainer.testing.
unary_math_function_unittest
(func, func_expected=None, label_expected=None, make_data=None)[source]¶ Decorator for testing unary mathematical Chainer functions.
This decorator makes test classes test unary mathematical Chainer functions. Tested are forward and backward computations on CPU and GPU across parameterized
shape
anddtype
.Parameters:  func (Function) – Chainer function to be tested by the decorated test class.
 func_expected – Function used to provide expected values for
testing forward computation. If not given, a corresponsing numpy
function for
func
is implicitly picked up by its class name.  label_expected (string) – String used to test labels of Chainer
functions. If not given, the class name of
func
lowered is implicitly used.  make_data – Function to customize input and gradient data used
in the tests. It takes
shape
anddtype
as its arguments, and returns a tuple of input and gradient data. By default, uniform destribution ranged[1, 1]
is used for both.
The decorated test class tests forward and backward computations on CPU and GPU across the following
parameterize()
ed parameters: shape: rank of zero, and rank of more than zero
 dtype:
numpy.float16
,numpy.float32
andnumpy.float64
Additionally, it tests the label of the Chainer function.
Chainer functions tested by the test class decorated with the decorator should have the following properties:
 Unary, taking one parameter and returning one value
dtype
of input and output are the same Elementwise operation for the supplied ndarray
Example
The following code defines a test class that tests
sin()
Chainer function, which takes a parameter withdtype
of float and returns a value with the samedtype
.>>> import unittest >>> from chainer import testing >>> from chainer import functions as F >>> >>> @testing.unary_math_function_unittest(F.Sin()) ... class TestSin(unittest.TestCase): ... pass
Because the test methods are implicitly injected to
TestSin
class by the decorator, it is enough to placepass
in the class definition.Now the test is run with
nose
module.>>> import nose >>> nose.run( ... defaultTest=__name__, argv=['', 'a', '!gpu'], exit=False) True
To customize test data,
make_data
optional parameter can be used. The following is an example of testingsqrt
Chainer function, which is tested in positive value domain here instead of the default input.>>> import numpy >>> >>> def make_data(shape, dtype): ... x = numpy.random.uniform(0.1, 1, shape).astype(dtype) ... gy = numpy.random.uniform(1, 1, shape).astype(dtype) ... return x, gy ... >>> @testing.unary_math_function_unittest(F.Sqrt(), ... make_data=make_data) ... class TestSqrt(unittest.TestCase): ... pass ... >>> nose.run( ... defaultTest=__name__, argv=['', 'a', '!gpu'], exit=False) True
make_data
function which returns input and gradient data generated in proper value domains with givenshape
anddtype
parameters is defined, then passed to the decorator’smake_data
parameter.