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)
import tensorflux2.graph as tfg
import tensorflux2.session as tfs
apple_price = 100
apple_num = 2
tax = 1.1
g = tfg.Graph()
# Create variables
a = tfg.Variable(apple_price, name="a")
b = tfg.Variable(apple_num, name="b")
c = tfg.Variable(tax, name="c")
# Create Mul operation node
d = tfg.Mul(a, b, name="d")
# Create Mul operation node
e = tfg.Mul(d, c, name="e")
session = tfs.Session()
# forward
total_apple_price = session.run(d, verbose=False)
print("total_apple_price: {:f}".format(float(total_apple_price)))
final_price = session.run(e, verbose=False)
print("final_price: {:f}".format(float(final_price)))
print()
#backward
d_in = 1
from tensorflux2 import graph as tfg
from tensorflux2 import session as tfs
g = tfg.Graph()
g.initialize()
# Create variables
w = tfg.Variable(5.0, name="a")
b = tfg.Variable(-1.0, name="b")
# Create placeholder
x = tfg.Placeholder(name="x")
# Create hidden node y
y = tfg.Mul(w, x, name="y")
# Create output node z
z = tfg.Add(y, b, name="z")
session = tfs.Session()
output = session.run(z, {x: 1.0})
print(output)
output = session.run(z, {x: 2.0})
print(output)
output = session.run(z, {x: 3.0})
print(output)
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=0.0,
scale=0.1,
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)