def zeros(self):
return {k : zeros_like(v) for k, v in getval(self).iteritems()}
def zeros(self):
return [zeros_like(v) for v in getval(self)]
def __lt__(self, other): return getval(self) < getval(other) def __gt__(self, other): return getval(self) > getval(other)
def __gt__(self, other): return getval(self) > getval(other) class FloatNode(NumericNode):
def zeros(self):
return {k: zeros_like(v) for k, v in getval(self).iteritems()}
def zeros(self):
return [zeros_like(v) for v in getval(self)]
def __gt__(self, other):
return getval(self) > getval(other)
def __lt__(self, other):
return getval(self) < getval(other)
def gradfun(g):
idxs = np.argmax(getval(x))
return untake(g, np.unravel_index(idxs, x.shape))
def grad_np_concatenate(g):
idxs = np.cumsum([a.shape[axis] for a in getval(arr_list)[:-1]])
return np.split(g, idxs, axis=axis)
import itertools as it
from functools import partial
from core import primitive, getval, untake
P = primitive
# ----- Operator gradients -----
I = lambda x : x # Identity operator
op.neg = P(op.neg, lambda ans, x : [op.neg])
op.add = P(op.add, lambda ans, x, y : unbroadcast(ans, x, y, [I, I]))
op.mul = P(op.mul, lambda ans, x, y : unbroadcast(ans, x, y, [lambda g : y * g, lambda g : x * g]))
op.sub = P(op.sub, lambda ans, x, y : unbroadcast(ans, x, y, [I, op.neg]))
op.div = P(op.div, lambda ans, x, y : unbroadcast(ans, x, y, [lambda g : g / y, lambda g : - g * x / y**2]))
op.pow = P(op.pow, lambda ans, x, y : unbroadcast(ans, x, y, [lambda g : g * y * x ** (y - 1),
lambda g : g * np.log(x) * x ** y]))
isarray = lambda x : isinstance(getval(x), np.ndarray)
isfloat = lambda x : isinstance(getval(x), float)
def unbroadcast(ans, x, y, funs):
return [unbroadcast_fun(ans, x, funs[0]),
unbroadcast_fun(ans, y, funs[1])]
def unbroadcast_fun(ans, x, fun):
if isfloat(x) and isarray(ans):
return lambda g : np.sum(fun(g))
elif isarray(x):
shape = x.shape
def new_fun(g):
result = fun(g)
while result.ndim > len(shape):
result = np.sum(result, axis=0)
