def setUp(self):
self.x_shape = (4, 3, 2)
self.dtype = numpy.float32
self.link = links.Swish(None)
self.link.cleargrads()
self.x = numpy.random.uniform(-1, 1, self.x_shape).astype(self.dtype)
self.gy = numpy.random.uniform(-1, 1, self.x_shape).astype(self.dtype)
def setUp(self):
self.x_shape = (4, 3, 2)
self.dtype = numpy.float32
self.link = links.Swish(())
beta = self.link.beta.data
beta[...] = numpy.random.uniform(-1, 1, beta.shape)
self.link.cleargrads()
self.x = numpy.random.uniform(-1, 1, self.x_shape).astype(self.dtype)
self.gy = numpy.random.uniform(-1, 1, self.x_shape).astype(self.dtype)
def __init__(self,
n_in=75,
n_units=500,
n_units2=400,
n_out=75,
train=True):
super(LSTM, self).__init__()
with self.init_scope():
self.l1 = L.LSTM(
in_size=n_in,
out_size=n_units,
lateral_init=chainer.initializers.Normal(scale=0.01))
self.l2 = L.Linear(
in_size=n_units,
out_size=n_out,
initialW=chainer.initializers.Normal(scale=0.01))
self.l3 = L.Swish(beta_shape=n_out)
self.train = train
def __init__(self, function, inputs, outputs):
super(ConvertSwish, self).__init__()
with self.init_scope():
self.f = L.Swish(beta_shape=function.params['beta'].shape,
beta_init=function.params['beta'])