def initialize_param(self):
self.param = tfg.Variable(tff.get_truncated_normal(shape=self.shape,
mean=self.mean,
sd=self.sd,
low=self.low,
upp=self.upp),
name=self.name)
def initialize_param(self):
sd = math.sqrt(1.0 / self.shape[0])
self.param = tfg.Variable(tff.get_truncated_normal(shape=self.shape,
mean=0.0,
sd=sd,
low=-sd,
upp=sd),
name=self.name)
def initialize_param(self):
if len(self.shape) == 2:
sd = math.sqrt(2.0 / (self.shape[0] + self.shape[1]))
else:
sd = math.sqrt(2.0 / self.shape[0])
self.param = tfg.Variable(np.random.uniform(low=-sd,
high=sd,
size=self.shape),
name=self.name)
def initialize_param(self):
if len(self.shape) == 2:
sd = math.sqrt(2.0 / (self.shape[0] + self.shape[1]))
else:
sd = math.sqrt(2.0 / self.shape[0])
self.param = tfg.Variable(tff.get_truncated_normal(shape=self.shape,
mean=0.0,
sd=sd,
low=-sd,
upp=sd),
name=self.name)
def initialize_param(self):
self.param = tfg.Variable(np.random.random(size=self.shape),
name=self.name)
def initialize_param(self):
self.param = tfg.Variable(np.random.normal(loc=self.mean,
scale=self.sd,
size=self.shape),
name=self.name)
def initialize_param(self):
self.param = tfg.Variable(np.ones(shape=self.shape) * 0.1,
name=self.name)
def initialize_param(self):
self.param = tfg.Variable(np.random.randn(self.shape[0],
self.shape[1]),
name=self.name)
def initialize_param(self):
self.param = tfg.Variable(np.zeros(shape=self.shape), name=self.name)
def initialize_param(self):
self.param = tfg.Variable(self.value, name=self.name)
def initialize_param(self):
sd = math.sqrt(2.0 / (self.shape[1] * self.shape[2] * self.shape[3]))
self.param = tfg.Variable(np.random.uniform(low=-sd,
high=sd,
size=self.shape),
name=self.name)
from Tensorflux import graph as tfg
from Tensorflux import session as tfs
import networkx_test as nx
import matplotlib.pyplot as plt
g = tfg.Graph() #그래프 객체 생성
g.initialize() #생성한 그래프 객체 초기화
# Create variables
A = tfg.Variable([[1, 0], [0, -1]], name="A")
b = tfg.Variable([1, 1], name="b")
# Create placeholder
x = tfg.Placeholder(name="x")
# Create hidden node y
y = tfg.Matmul(A, x, name="y")
# Create output node z
z = tfg.Add(y, b, name="z")
nx.draw_networkx(g, with_labels=True)
plt.show(block=True)
session = tfs.Session()
output = session.run(z, {x: [1, 2]})
print(output)