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step18.py
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step18.py
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import weakref
import numpy as np
import contextlib
class Config:
enable_backprop = True
@contextlib.contextmanager
def using_config(name, value):
old_value = getattr(Config, name)
setattr(Config, name, value)
try:
yield
finally:
setattr(Config, name, old_value)
def no_grad():
return using_config('enable_backprop', False)
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
self.generation = 0
def set_creator(self, func):
self.creator = func
self.generation = func.generation + 1
def cleargrad(self):
self.grad = None
def backward(self, retain_grad=False):
if self.grad is None:
self.grad = np.ones_like(self.data)
funcs = []
seen_set = set()
def add_func(f):
if f not in seen_set:
funcs.append(f)
seen_set.add(f)
funcs.sort(key=lambda x: x.generation)
add_func(self.creator)
while funcs:
f = funcs.pop()
gys = [output().grad for output in f.outputs] # output is weakref
gxs = f.backward(*gys)
if not isinstance(gxs, tuple):
gxs = (gxs,)
for x, gx in zip(f.inputs, gxs):
if x.grad is None:
x.grad = gx
else:
x.grad = x.grad + gx
if x.creator is not None:
add_func(x.creator)
if not retain_grad:
for y in f.outputs:
y().grad = None # y is weakref
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]
if Config.enable_backprop:
self.generation = max([x.generation for x in inputs])
for output in outputs:
output.set_creator(self)
self.inputs = inputs
self.outputs = [weakref.ref(output) for output in outputs]
return outputs if len(outputs) > 1 else outputs[0]
def forward(self, xs):
raise NotImplementedError()
def backward(self, gys):
raise NotImplementedError()
class Square(Function):
def forward(self, x):
y = x ** 2
return y
def backward(self, gy):
x = self.inputs[0].data
gx = 2 * x * gy
return gx
def square(x):
return Square()(x)
class Add(Function):
def forward(self, x0, x1):
y = x0 + x1
return y
def backward(self, gy):
return gy, gy
def add(x0, x1):
return Add()(x0, x1)
x0 = Variable(np.array(1.0))
x1 = Variable(np.array(1.0))
t = add(x0, x1)
y = add(x0, t)
y.backward()
print(y.grad, t.grad) # None None
print(x0.grad, x1.grad) # 2.0 1.0
with using_config('enable_backprop', False):
x = Variable(np.array(2.0))
y = square(x)
with no_grad():
x = Variable(np.array(2.0))
y = square(x)