import networkx as nx
import matplotlib.pyplot as plt
g = tfg.Graph()
g.initialize()
#--------------
# Create variables
a = 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(a, x, name="y")
# Create output node z
z = tfg.Add(y, b, name="z")
session = tfs.Session()
output = session.run(z, {x: 1.0}) # z:operation
print(output)
print()
print("-------------------")
#--------------
# Create variables
# Not scalar, list
A = tfg.Variable([[1, 0], [0, -1]], name="A")
b = tfg.Variable([1, 1], name="b")
import networkx as nx
import matplotlib.pyplot as plt
# g= graph.Graph()
g = tfg.Graph()
g.initialize()
# Create variables
a = 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(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: 1.0})
print(output)
print(z.input_nodes[0], z.input_nodes[1])
print(z.output)
print(z.consumers)
print(y.consumers[0])
import tensorflux.graph as tfg
import tensorflux.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
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(orange_price, name="c")
d = tfg.Variable(orange_num, name="d")
e = tfg.Variable(tax, name="e")
# Create Mul operation node
f = tfg.Mul(a, b, name="f")
g = tfg.Mul(c, d, name="g")
# Create Add operation node
h = tfg.Add(f, g, name="h")
# Create Mul operation node
i = tfg.Mul(h, e, name="i")
session = tfs.Session()
# forward
final_price = session.run(i, verbose=False)
print("final_price: {:f}".format(float(final_price)))
print()
def __init__(self, weight_node, input_node, bias_node):
# Create hidden node y
y = tfg.Mul(weight_node, input_node) #곱 연산 클래스를 생성
# Create output node z
z = tfg.Add(y, bias_node) #합 연산 클래스를 생성
#import networkx as nx
#import matplotlib.pyplot as plt
g = tfg.Graph() # 컴퓨테이션 그래프 클래스를 생성
#g.initialize() # 컴퓨테이션 그래프 클래스를 초기화
#"""
# Create variables
a = tfg.Variable(5.0, name="a") #변수 클래스 a를 생성하고 초기화
b = tfg.Variable(-1.0, name="b") #변수 클래스 b를 생성하고 초기화
# Create placeholder
x = tfg.Placeholder(name="x") #플레이스홀더는 이후 입력할 값으로 현재는 값까지는 초기화하지 않음
# Create hidden node y
y = tfg.Mul(a, x, name="y") #곱 연산 클래스를 생성
# Create output node z
z = tfg.Add(y, b, name="z") #합 연산 클래스를 생성
#nx.draw_networkx(g, with_labels=True)
#plt.show(block=True)
# 수행~~~~~~~~~~~~~~~~~~~~~~
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 __init__(self, weight_node, input_node, bias_node):
y = tfg.Mul(weight_node, input_node)
z = tfg.Add(y, bias_node)