def test_repeat():
assert repeat(None, length=4) == [None, None, None, None]
assert repeat([], length=4) == []
assert repeat((), length=4) == []
assert repeat(4, length=4) == [4, 4, 4, 4]
assert repeat("check", length=3) == ["check", "check", "check"]
assert repeat([2, 3, 4], length=4) == [2, 3, 4, 4]
assert repeat([1, 2, 3, 4], length=4) == [1, 2, 3, 4]
assert repeat([1, 2, 3, 4, 5], length=4) == [1, 2, 3, 4]
def build(self, hids):
hids = gf.repeat(hids)
weights, w_velocities = [], []
pre_hid = self.num_attrs
for hid in hids + [self.num_classes]:
shape = (pre_hid, hid)
w = Parameter(torch.zeros(shape).to(self.device))
w_velocity = torch.zeros(shape).to(self.device)
weights.append(w)
w_velocities.append(w_velocity)
pre_hid = hid
self.weights, self.w_velocities = weights, w_velocities
self._initialize()
def build(self, hids):
hids = gf.repeat(hids)
weights = []
zeros_initializer = zeros()
pre_hid = self.num_attrs
for hid in hids + [self.num_classes]:
shape = (pre_hid, hid)
# use zeros_initializer temporary to save time
weight = tf.Variable(zeros_initializer(shape=shape, dtype=self.floatx))
weights.append(weight)
pre_hid = hid
self.weights = weights
self.adj_grad_sum = tf.Variable(tf.zeros_like(self.adj_tensor))
self.x_grad_sum = tf.Variable(tf.zeros_like(self.x_tensor))
self.optimizer = Adam(self.lr, epsilon=1e-8)
def build(self, hids):
hids = gf.repeat(hids)
weights, w_velocities = [], []
zeros_initializer = zeros()
pre_hid = self.num_attrs
for hid in hids + [self.num_classes]:
shape = (pre_hid, hid)
# use zeros_initializer temporary to save time
weight = tf.Variable(zeros_initializer(shape=shape, dtype=self.floatx))
w_velocity = tf.Variable(zeros_initializer(shape=shape, dtype=self.floatx))
weights.append(weight)
w_velocities.append(w_velocity)
pre_hid = hid
self.weights, self.w_velocities = weights, w_velocities