import HW2.graph as tfg
import HW2.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
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(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):
self.param = tfg.Variable(np.ones(shape=self.shape) * 0.1, 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.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)
# Reference: http://www.deepideas.net/deep-learning-from-scratch-i-computational-graphs/
import networkx as nx
import matplotlib.pyplot as plt
import HW2.graph as tfg
import HW2.session as tfs
import HW2.functions as tff
# Create a new 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, feed_dict={x: 1.0})
print(output)
import HW2.graph as tfg import HW2.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")
