# Reference: http://www.deepideas.net/deep-learning-from-scratch-i-computational-graphs/
import HW2.graph as tfg
import HW2.networks as tfn
import HW2.enums as tfe
import datasource.simple as simple_data
n = tfn.Two_Neurons_Network(input_size=2, output_size=1)
x = tfg.Placeholder(name="x")
target = tfg.Placeholder(name="target")
n.set_data(x, target)
n.initialize_param(initializer=tfe.Initializer.Point_One.value)
n.layering(activator=tfe.Activator.ReLU.value)
n.set_optimizer(optimizer=tfe.Optimizer.SGD.value, learning_rate=0.01)
#n.draw_and_show()
data = simple_data.And_Gate_Data()
n.print_feed_forward(num_data=data.num_train_data,
input_data=data.train_input,
target_data=data.train_target,
verbose=False)
#n.learning(max_epoch=200, data=data, bp=False, print_period=10, verbose=False)
n.learning(max_epoch=200, data=data, bp=True, print_period=10, verbose=False)
n.print_feed_forward(num_data=data.num_test_data,
input_data=data.test_input,
target_data=data.test_target,
verbose=False)
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)