def dropout(x, p=0):
if p==0:
return x
else:
mask = cgt.greater(cgt.rand(*cgt.shape(x)), p)
x = x * mask
x = x /(1.0-p)
return x
def dropout(x, p=0):
if p == 0:
return x
else:
mask = cgt.greater(cgt.rand(*cgt.shape(x)), p)
x = x * mask
x = x / (1.0 - p)
return x
def sample(self, p, shape=None, numeric=False):
""" Element-wise sampling for each component of p """
# TODO_TZ maybe cgt has mechanism to eval an expr
if not numeric:
p = core.as_node(p)
shape = shape or cgt.shape(p)
return cgt.rand(*shape) <= p
else:
assert isinstance(p, np.ndarray)
return np.array(nr.rand(*p.shape) <= p, dtype="i2")
def sample(self, p, shape=None):
p = core.as_node(p)
shape = shape or cgt.shape(p)
return cgt.rand(*shape) <= p
def uniform(size=(), low=0.0, high=1.0, ndim=None):
return cgt.rand(*size) * (high - low) + low
def binomial(size=(), n=1, p=0.5, ndim=None, dtype='int64'):
return cgt.less(cgt.rand(*size), p)
def sample(self, p, shape=None):
p = core.as_node(p)
shape = shape or cgt.shape(p)
return cgt.rand(*shape) <= p
