def test_serialization(self):
lin1 = links.SimplifiedDropconnect(None, self.out_size)
x = chainer.Variable(self.x)
# Must call the link to initialize weights.
lin1(x)
w1 = lin1.W.data
fd, temp_file_path = tempfile.mkstemp()
os.close(fd)
npz.save_npz(temp_file_path, lin1)
lin2 = links.SimplifiedDropconnect(None, self.out_size)
npz.load_npz(temp_file_path, lin2)
w2 = lin2.W.data
self.assertEqual((w1 == w2).all(), True)
def setUp(self):
in_size = numpy.prod(self.in_shape)
self.link = links.SimplifiedDropconnect(
in_size,
self.out_size,
initialW=chainer.initializers.Normal(1, self.W_dtype),
initial_bias=chainer.initializers.Normal(1, self.x_dtype))
self.link.cleargrads()
x_shape = (4, ) + self.in_shape
self.x = numpy.random.uniform(-1, 1, x_shape).astype(self.x_dtype)
self.gy = numpy.random.uniform(-1, 1,
(4, self.out_size)).astype(self.x_dtype)
W = self.link.W.data
b = self.link.b.data
mask_shape = (4, ) + self.link.W.shape
self.mask = gen_mask(self.ratio, mask_shape)
W = (W * self.mask) * (1. / (1 - self.ratio))
x = self.x.reshape(4, -1)
# numpy 1.9 does not support matmul.
# So we use numpy.einsum instead of numpy.matmul.
self.y_expect = numpy.einsum('ijk,ikl->ijl', W, x[:, :, None]).reshape(
4, -1) + b
self.check_forward_options = {}
self.check_backward_options = {}
if self.x_dtype == numpy.float16:
self.check_forward_options = {'atol': 1e-3, 'rtol': 1e-2}
self.check_backward_options = {'atol': 1e-2, 'rtol': 5e-2}
elif self.W_dtype == numpy.float16:
self.check_backward_options = {'atol': 1e-3, 'rtol': 1e-2}
def __init__(self, in_size: int):
super(Model, self).__init__()
with self.init_scope():
self.l1 = L.SimplifiedDropconnect(None,
64,
nobias=True,
ratio=0.95)
def setUp(self):
in_size = numpy.prod(self.in_shape)
self.link = links.SimplifiedDropconnect(
in_size, self.out_size,
initialW=chainer.initializers.Normal(1, numpy.float32),
initial_bias=chainer.initializers.Normal(1, numpy.float32))
self.link.cleargrads()
x_shape = (4,) + self.in_shape
self.x = numpy.ones(x_shape).astype(numpy.float32)
self.W = self.link.W.data
self.b = self.link.b.data
def setUp(self):
self.link = links.SimplifiedDropconnect(self.in_size_or_none,
self.out_size)
temp_x = numpy.random.uniform(-1, 1,
(4, self.in_size)).astype(numpy.float32)
self.link(chainer.Variable(temp_x))
W = self.link.W.data
W[...] = numpy.random.uniform(-1, 1, W.shape)
b = self.link.b.data
b[...] = numpy.random.uniform(-1, 1, b.shape)
self.link.cleargrads()
mask_shape = (4, self.out_size, self.in_size)
self.mask = gen_mask(self.ratio, mask_shape)
x_shape = (4, ) + self.in_shape
self.x = numpy.random.uniform(-1, 1, x_shape).astype(numpy.float32)
self.gy = numpy.random.uniform(-1, 1, (4, self.out_size)).astype(
numpy.float32)
W = (W * self.mask) * (1. / (1 - self.ratio))
# numpy 1.9 does not support matmul.
# So we use numpy.einsum instead of numpy.matmul.
self.y_expect = numpy.einsum('ijk,ikl->ijl', W,
self.x[:, :, None]).reshape(4, -1) + b
def test_invalid_mask_size2(self):
link = links.SimplifiedDropconnect(3, 2)
x = numpy.random.uniform(-1, 1, (4, 3)).astype(numpy.float32)
mask = numpy.random.uniform(-1, 1, (4, 3, 2)).astype(numpy.float32)
with self.assertRaises(type_check.InvalidType):
link(chainer.Variable(x), use_batchwise_mask=False, mask=mask)
def test_invalid_input_size(self):
link = links.SimplifiedDropconnect(3, 2)
x = numpy.random.uniform(-1, 1, (4, 1, 2)).astype(numpy.float32)
with self.assertRaises(type_check.InvalidType):
link(chainer.Variable(x))
def setUp(self):
self.link = links.SimplifiedDropconnect(3, 2)
self.x = numpy.random.uniform(-1, 1, (4, 1, 2)).astype(numpy.float32)
