def _oper_gpu(cls, x, dropout_ratio):
mask = get_gpu(x).empty_like_me()
curand_generator().rand_bernoulli(mask, 1 - dropout_ratio)
mask = mask / dropout_ratio
value = get_gpu(x) * mask
ret = cls._create_node(value)
ret.attrs._x = x
ret.attrs._mask = mask
return ret
def _oper_gpu(cls, x, drop_out_ratio):
shape = (x.shape[0], x.shape[1], 1, 1)
mask = GPUValue(shape=shape)
curand_generator().rand_bernoulli(mask, 1 - drop_out_ratio)
mask = mask / drop_out_ratio
mask = mask * get_gpu(x).ones_like_me()
value = get_gpu(x) * get_gpu(mask)
ret = cls._create_node(value)
ret.attrs._x = x
ret.attrs._mask = mask
return ret
def test_rand():
set_cuda_active(True)
x = get_gpu(np.random.rand(2, 2)).empty_like_me()
np.random.seed(2)
curand_generator().rand_bernoulli(x, 0.5)
g1 = x.new_array()
np.random.seed(2)
curand_generator().rand_bernoulli(x, 0.5)
g2 = x.new_array()
assert np.allclose(g1, g2)
def func(node):
if is_cuda_active():
curand_generator().set_seed(seed)
else:
np.random.seed(seed)
return sum(layer(node))
def func(node):
if use_gpu:
curand_generator().set_seed(seed)
else:
np.random.seed(seed)
return sum(layer(node))