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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, *inputs):
xs = [x.data for x in inputs]
ys = self.forward(*xs)
if not isinstance(ys, tuple):
ys = (ys,)
outputs = [Variable(as_array(y)) for y in ys]
for output in outputs:
output.set_creator(self)
self.inputs = inputs
self.outputs = outputs
return outputs if len(outputs) > 1 else outputs[0]
def forward(self, xs):
raise NotImplementedError()
def backward(self, gys):
raise NotImplementedError()
class Add(Function):
def forward(self, x0, x1):
y = x0 + x1
return y
def add(x0, x1):
return Add()(x0, x1)
x0 = Variable(np.array(2))
x1 = Variable(np.array(3))
y = add(x0, x1)
print(y.data)
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