def array_equal(a, b):
"""True if two arrays have the same shape and elements, False otherwise.
Args:
a, b (numpy.ndarray or cupy.ndarray or dezero.Variable): input arrays
to compare
Returns:
bool: True if the two arrays are equal.
"""
a = a.data if isinstance(a, Variable) else a
b = b.data if isinstance(b, Variable) else b
a, b = cuda.as_numpy(a), cuda.as_numpy(b)
return np.array_equal(a, b)
def array_allclose(a, b, rtol=1e-4, atol=1e-5):
"""Returns True if two arrays(or variables) are element-wise equal within a
tolerance.
Args:
a, b (numpy.ndarray or cupy.ndarray or dezero.Variable): input arrays
to compare
rtol (float): The relative tolerance parameter.
atol (float): The absolute tolerance parameter.
Returns:
bool: True if the two arrays are equal within the given tolerance,
False otherwise.
"""
a = a.data if isinstance(a, Variable) else a
b = b.data if isinstance(b, Variable) else b
a, b = cuda.as_numpy(a), cuda.as_numpy(b)
return np.allclose(a, b, atol=atol, rtol=rtol)
def array_equal(a, b):
""" 2つの array が同じ形状で同じ要素をもつ場合は True を返し、それ以外は False を返す
Parameters
----------
a : ndarray (numpy or cupy) or Variable
b : ndarray (numpy or cupy) or Variable
Returns
-------
c : bool
"""
a = a.data if isinstance(a, Variable) else a
b = b.data if isinstance(b, Variable) else b
a, b = cuda.as_numpy(a), cuda.as_numpy(b)
return np.array_equal(a, b)
def array_allclose(a, b, atol=1e-5, rtol=1e-4):
"""2つの array が同じ形状で近い値の要素をもつ場合は True を返し、それ以外は False を返す
Parameters
----------
a : ndarray (numpy or cupy) or Variable
b : ndarray (numpy or cupy) or Variable
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）
Returns
-------
"""
a = a.data if isinstance(a, Variable) else a
b = b.data if isinstance(b, Variable) else b
a, b = cuda.as_numpy(a), cuda.as_numpy(b)
return np.allclose(a, b, atol=atol, rtol=rtol)
def numerical_grad(f, x, *args, **kwargs):
"""数値微分で勾配を求める
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
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
-------
grad : ndarray
"""
eps = 1e-4
x = x.data if isinstance(x, Variable) else x
xp = cuda.get_array_module(x)
if xp is not np:
np_x = cuda.as_numpy(x)
else:
np_x = x
grad = xp.zeros_like(x)
it = np.nditer(np_x, flags=['multi_index'], op_flags=['readwrite'])
while not it.finished:
idx = it.multi_index
tmp_val = x[idx].copy()
x[idx] = tmp_val + eps
y1 = f(x, *args, **kwargs) # f(x+h)
if isinstance(y1, Variable):
y1 = y1.data
y1 = y1.copy()
x[idx] = tmp_val - eps
y2 = f(x, *args, **kwargs) # f(x-h)
if isinstance(y2, Variable):
y2 = y2.data
y2 = y2.copy()
diff = (y1 - y2).sum()
grad[idx] = diff / (2 * eps)
x[idx] = tmp_val
it.iternext()
return grad
def numerical_grad(f, x, *args, **kwargs):
"""Computes numerical gradient by finite differences.
Args:
f (callable): A function which gets `Variable`s and returns `Variable`s.
x (`ndarray` or `dezero.Variable`): A target `Variable` for computing
the gradient.
*args: If `f` needs variables except `x`, you can specify with this
argument.
**kwargs: If `f` needs keyword variables, you can specify with this
argument.
Returns:
`ndarray`: Gradient.
"""
eps = 1e-4
x = x.data if isinstance(x, Variable) else x
xp = cuda.get_array_module(x)
if xp is not np:
np_x = cuda.as_numpy(x)
else:
np_x = x
grad = xp.zeros_like(x)
it = np.nditer(np_x, flags=['multi_index'], op_flags=['readwrite'])
while not it.finished:
idx = it.multi_index
tmp_val = x[idx].copy()
x[idx] = tmp_val + eps
y1 = f(x, *args, **kwargs) # f(x+h)
if isinstance(y1, Variable):
y1 = y1.data
y1 = y1.copy()
x[idx] = tmp_val - eps
y2 = f(x, *args, **kwargs) # f(x-h)
if isinstance(y2, Variable):
y2 = y2.data
y2 = y2.copy()
diff = (y1 - y2).sum()
grad[idx] = diff / (2 * eps)
x[idx] = tmp_val
it.iternext()
return grad
