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import numpy as np
class Variable:
def __init__(self, data):
if data is not None:
if not isinstance(data, np.ndarray):
raise TypeError('{} is not supported'.format(type(data)))
self.data = data
self.grad = None
self.creator = None
def set_creator(self, func):
self.creator = func
def backward(self):
if self.grad is None:
self.grad = np.ones_like(self.data)
funcs = [self.creator]
while funcs:
f = funcs.pop()
x, y = f.input, f.output
x.grad = f.backward(y.grad)
if x.creator is not None:
funcs.append(x.creator)
def as_array(x):
if np.isscalar(x):
return np.array(x)
return x
class Function:
def __call__(self, input):
x = input.data
y = self.forward(x)
output = Variable(as_array(y))
output.set_creator(self)
self.input = input
self.output = output
return output
def forward(self, x):
raise NotImplementedError()
def backward(self, gy):
raise NotImplementedError()
class Square(Function):
def forward(self, x):
y = x ** 2
return y
def backward(self, gy):
x = self.input.data
gx = 2 * x * gy
return gx
class Exp(Function):
def forward(self, x):
y = np.exp(x)
return y
def backward(self, gy):
x = self.input.data
gx = np.exp(x) * gy
return gx
def square(x):
return Square()(x)
def exp(x):
return Exp()(x)
x = Variable(np.array(0.5))
y = square(exp(square(x)))
y.backward()
print(x.grad)
x = Variable(np.array(1.0)) # OK
x = Variable(None) # OK
x = Variable(1.0) # NG
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