Main file simple

This is the main file for the simple classification task

import tensorflow as tf
import numpy as np
import gnn_utils
import GNN as GNN
import Net_Subgraph as n

import networkx as nx
import scipy as sp

import matplotlib.pyplot as plt

##### GPU & stuff config
import os

os.environ['CUDA_VISIBLE_DEVICES'] = "0"
config = tf.ConfigProto()
config.gpu_options.allow_growth = True

############# data creation ################

# GRAPH #1

# List of edges in the first graph - last column is the id of the graph to which the arc belongs
e = [[0, 1, 0], [0,2, 0], [0, 4, 0], [1, 2, 0], [1, 3, 0], [2, 3, 0], [2, 4, 0]]
# undirected graph, adding other direction
e.extend([[i, j, num] for j, i, num in e])
#reorder
e = sorted(e)
E = np.asarray(e)

#number of nodes
edges = 5
# creating node features - simply one-hot values
N = np.eye(edges, dtype=np.float32)

# adding column thta represent the id of the graph to which the node belongs
N = np.concatenate((N, np.zeros((edges,1), dtype=np.float32)),  axis=1 )


# visualization graph
def plot_graph(E, N):
    g = nx.Graph()
    g.add_nodes_from(range(N.shape[0]))
    g.add_edges_from(E[:, :2])
    nx.draw_spring(g, cmap=plt.get_cmap('Set1'), with_labels=True)
    plt.show()


plot_graph(E,N)



# GRAPH #2

# List of edges in the second graph - last column graph-id
e1 = [[0, 2, 1], [0,3,1], [1, 2,1], [1,3,1], [2,3,1]]
# undirected graph, adding other direction
e1.extend([[i, j, num] for j, i, num in e1])
# reindexing node ids based on the dimension of previous graph (using unique ids)
e2 = [[a + N.shape[0], b + N.shape[0], num] for a, b, num in e1]
#reorder
e2 = sorted(e2)
edges_2 = 4


# Plot second graph

E1 = np.asarray(e1)
N1 = np.eye(edges_2,  dtype=np.float32)
N1 = np.concatenate((N1, np.zeros((edges_2,1), dtype=np.float32)),  axis=1 )

plot_graph(E1,N1)

E = np.concatenate((E, np.asarray(e2)), axis=0)
N_tot = np.eye(edges + edges_2,  dtype=np.float32)
N_tot = np.concatenate((N_tot, np.zeros((edges + edges_2,1), dtype=np.float32)),  axis=1 )


# Create Input to GNN

inp, arcnode, graphnode = gnn_utils.from_EN_to_GNN(E, N_tot)
labels = np.random.randint(2, size=(N_tot.shape[0]))

labels = np.eye(max(labels)+1, dtype=np.int32)[labels]  # one-hot encoding of labels

---

# set input and output dim, the maximum number of iterations, the number of epochs and the optimizer
threshold = 0.01
learning_rate = 0.01
state_dim = 5
tf.reset_default_graph()
input_dim = inp.shape[1]
output_dim = labels.shape[1]
max_it = 50
num_epoch = 10000
optimizer = tf.train.AdamOptimizer

# initialize state and output network
net = n.Net(input_dim, state_dim, output_dim)

# initialize GNN
param = "st_d" + str(state_dim) + "_th" + str(threshold) + "_lr" + str(learning_rate)
print(param)

tensorboard = False

g = GNN.GNN(net, input_dim, output_dim, state_dim,  max_it, optimizer, learning_rate, threshold, graph_based=False, param=param, config=config,
            tensorboard=tensorboard)

# train the model
count = 0

######

for j in range(0, num_epoch):
    _, it = g.Train(inputs=inp, ArcNode=arcnode, target=labels, step=count)

    if count % 30 == 0:
        print("Epoch ", count)
        print("Training: ", g.Validate(inp, arcnode, labels, count))

        # end = time.time()
        # print("Epoch {} at time {}".format(j, end-start))
        # start = time.time()

    count = count + 1

# evaluate on the test set
# print("\nEvaluate: \n")
# print(g.Evaluate(inp_test[0], arcnode_test[0], labels_test, nodegraph_test[0])[0])