def check_backward(func, x_data, y_grad=None, eps=0.001,
atol=1e-5, rtol=1e-4, verbose=True):
x_data = _as_tuple(x_data)
x_data = tuple([x.astype(np.float64) for x in x_data])
if y_grad is not None:
y_grad = y_grad.astype(np.float64)
def f(inputs):
inputs = _as_tuple(inputs)
inputs = [as_variable(x) for x in inputs]
y = func(*inputs)
return y.data
num_grads = numerical_grad(f, x_data, y_grad, eps)
inputs = [as_variable(x) for x in x_data]
y = func(*inputs)
if y_grad is not None:
y.grad = Variable(y_grad)
y.backward()
bp_grads = [x.grad.data for x in inputs]
results = []
for num_grad, bp_grad in zip(num_grads, bp_grads):
assert bp_grad.shape == num_grad.shape
res = np.allclose(num_grad, bp_grad, atol=atol, rtol=rtol)
results.append(res)
if not res and verbose:
diff = abs(num_grad - bp_grad)
print('-------------------------')
print('diff', diff)
print('diff mean', np.array(diff).mean())
# print('num_grad:', num_grad.shape, num_grad)
# print('bp_grad:', bp_grad.shape, bp_grad)
return all(results)
def gradient_check(f, x, *args, atol=1e-5, rtol=1e-4, **kwargs):
"""勾配確認を行う
誤差逆伝播法と数値微分との結果を比較し、その結果がある誤差以内の場合は True を返す
誤差の基準は atol と rtol で指定する
Parameters
----------
f : DeZero function
DeZeroの関数やレイヤ
x : ndarray or dezero.Variable
勾配を求める変数
args : 可変長引数
f(x, y) のように、入力する変数がx以外にある場合はここで与える
atol : float
numpy.allclose関数で使用する atol（絶対許容パラメータ）
rtol : float
numpy.allclose関数で使用する rtol（相対許容パラメータ）
kwargs : キーワード引数
f(x, key=y) のように、入力する変数がx以外にある場合はここで与える
Returns
-------
res : bool
"""
x = as_variable(x)
x.data = x.data.astype(np.float64)
num_grad = numerical_grad(f, x, *args, **kwargs)
y = f(x, *args, **kwargs)
y.backward()
bp_grad = x.grad.data
assert bp_grad.shape == num_grad.shape
res = array_allclose(num_grad, bp_grad, atol=atol, rtol=rtol)
if not res:
print('')
print('========== FAILED (Gradient Check) ==========')
print('Numerical Grad')
print(' shape: {}'.format(num_grad.shape))
val = str(num_grad.flatten()[:10])
print(' values: {} ...'.format(val[1:-1]))
print('Backprop Grad')
print(' shape: {}'.format(bp_grad.shape))
val = str(bp_grad.flatten()[:10])
print(' values: {} ...'.format(val[1:-1]))
return res
def gradient_check(f, x, *args, rtol=1e-4, atol=1e-5, **kwargs):
"""Test backward procedure of a given function.
This automatically checks the backward-process of a given function. For
checking the correctness, this function compares gradients by
backprop and ones by numerical derivation. If the result is within a
tolerance this function return True, otherwise False.
Args:
f (callable): A function which gets `Variable`s and returns `Variable`s.
x (`ndarray` or `dezero.Variable`): A traget `Variable` for computing
the gradient.
*args: If `f` needs variables except `x`, you can specify with this
argument.
rtol (float): The relative tolerance parameter.
atol (float): The absolute tolerance parameter.
**kwargs: If `f` needs keyword variables, you can specify with this
argument.
Returns:
bool: Return True if the result is within a tolerance, otherwise False.
"""
x = as_variable(x)
x.data = x.data.astype(np.float64)
num_grad = numerical_grad(f, x, *args, **kwargs)
y = f(x, *args, **kwargs)
y.backward()
bp_grad = x.grad.data
assert bp_grad.shape == num_grad.shape
res = array_allclose(num_grad, bp_grad, atol=atol, rtol=rtol)
if not res:
print('')
print('========== FAILED (Gradient Check) ==========')
print('Numerical Grad')
print(' shape: {}'.format(num_grad.shape))
val = str(num_grad.flatten()[:10])
print(' values: {} ...'.format(val[1:-1]))
print('Backprop Grad')
print(' shape: {}'.format(bp_grad.shape))
val = str(bp_grad.flatten()[:10])
print(' values: {} ...'.format(val[1:-1]))
return res
def softmax1d(x):
x = as_variable(x)
y = F.exp(x)
sum_y = F.sum(y)
return y / sum_y
def test_as_variable(self, input, expected):
assert type(as_variable(input)) == type(expected)
def f(inputs):
inputs = _as_tuple(inputs)
inputs = [as_variable(x) for x in inputs]
y = func(*inputs)
return y.data
