def initialize_param(self): sd = math.sqrt(1.0 / self.shape[0]) self.param = tfg.Variable(tff.get_truncated_normal(shape=self.shape, mean=self.mean, sd=sd, low=-sd, upp=sd), name=self.name)
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): init_range = np.sqrt(6.0 / (self.shape[0] + self.shape[1])) self.param = tfg.Variable( (init_range - (-init_range) * np.random.random_sample(size=self.shape) + (-init_range)), 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): sd = math.sqrt(2.0 / (self.shape[1] * self.shape[2] * self.shape[3])) 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(4.0 / (self.shape[0] + self.shape[1])) else: sd = math.sqrt(2.0 / self.shape[0]) self.param = tfg.Variable(np.random.normal(loc=0.0, scale=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=self.mean, 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)
from tensorflux import graph as tfg from tensorflux import session as tfs import networkx as nx import matplotlib.pyplot as plt 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.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: 1.0}) output = session.run(z, {x: 2.0}) print(output)
def initialize_param(self): #print (self.shape[0], self.shape[1] ) self.param = tfg.Variable(np.random.uniform(low=-1, high=1, size=self.shape), name=self.name)
from tensorflux import graph as tfg from tensorflux import session as tfs 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)
# placeholder 생성 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") #z : operation session = tfs.Session() output = session.run(z, {x: 1.0}) print(output) """ # 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") #a, x, b가 행렬이기 때문에 mul이 아니라 matmul # 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()
from tensorflux import graph as tfg # graph 모듈을 객체화 from tensorflux import session as tfs 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")
import tensorflux.graph as tfg import tensorflux.session as tfs apple_price = 100 apple_num = 2 orange_price = 150 orange_num = 3 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")
def initialize_param(self): sd = math.sqrt(1.0 / self.shape[0]) 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 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)
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