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tenmul4.py
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tenmul4.py
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import re, os, sys, math, unittest
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
import random
from random import shuffle, choice
from itertools import product
# from scipy.sparse import csr_matrix
# from scipy.sparse.csgraph import connected_components
import operator
import numpy as np
np.set_printoptions(precision=4)
# from decorator import decorator
import operator
from functools import reduce
# from scipy.io import loadmat
from tensorflow.python.framework.ops import Tensor
def prod(iterable):
return reduce(operator.mul, iterable, 1)
def letter_range(n):
for c in range(97, 97+n):
yield chr(c)
class RealTensor(object):
def __init__(self, name='no_name', from_variable=None, shape=[1, 2, 3, 4], trainable=True,
initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0), identity=None):
if from_variable is not None:
self.name = name
self.identity = identity
self.shape = from_variable.get_shape().as_list()
self.tensor = tf.identity(from_variable, name=self.name)
else:
self.name = name
self.identity = identity
self.shape = shape
self.initializer = initializer
if isinstance(self.initializer, Tensor):
self.tensor = tf.get_variable(name = self.name, initializer = self.initializer, trainable=trainable)
else:
self.tensor = tf.get_variable(name = self.name, shape = self.shape,
initializer = self.initializer, trainable=trainable)
def __call__(self):
return self.tensor
class TensorNetwork(object):
def __init__(self, adj_matrix, name_list=None, initializer_list=None, trainable_list=None, scope='TensorNetwork'):
self.shape = adj_matrix.shape
assert self.shape[0] == self.shape[1], 'adj_matrix must be a square matrix.'
self.dim = self.shape[0]
self.adj_matrix = np.empty(self.shape, dtype=object)
self.scope = scope
self.output_count = 0
self.output_order = []
for i in np.diag(adj_matrix):
if i == 0:
self.output_order.append([])
else:
self.output_order.append([self.output_count])
self.output_count += 1
self.id_matrix = np.empty(self.shape, dtype=object)
tril_idx = np.tril_indices(self.dim, -1)
if np.sum(adj_matrix[tril_idx]) == 0:
adj_matrix[tril_idx] += adj_matrix[(tril_idx[1], tril_idx[0])]
# graph = np.copy(adj_matrix)
# graph[np.diag_indices(self.dim)] = 0
# graph[graph>0] = 1
# graph = csr_matrix(graph)
# n_components = connected_components(csgraph=graph, directed=False, return_labels=False)
# if not n_components == 1:
# print ('The network is seperated by {} parts.'.format(n_components))
for idx in np.ndindex(self.shape):
self.adj_matrix[idx] = [ adj_matrix[idx] ]
if self.adj_matrix[idx][0] == 0:
self.adj_matrix[idx].clear()
# if idx[0] == idx[1]:
# self.adj_matrix[idx].append(1)
if name_list is not None:
assert self.dim == len(name_list), 'Length of name_list does not match number of cores.'
self.name_list = name_list
else:
self.name_list = list(letter_range(self.dim))
if trainable_list is None:
trainable_list = [True] * self.dim
if initializer_list is None:
initializer_list = [tf.random_normal_initializer(mean=0.0, stddev=1.0)] * self.dim
with tf.variable_scope(self.scope):
self.cores = [ RealTensor(name=self.name_list[t], shape=list(filter((0).__ne__, adj_matrix[t].tolist())),
trainable=trainable_list[t], initializer=initializer_list[t]) for t in range(self.dim) ]
def __repr__(self):
return self.reduction()
def __add__(self, TN_b):
return self.reduction() + N_b.reduction()
def __sub_(self, TN_b):
return self.reduction() - N_b.reduction()
def __mul_(self, TN_b):
return __tf_matmul__(self.reduction(), N_b.reduction())
def giff_cores(self):
return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.scope)
def __outter_product__(self):
tar = tf.reshape(self.cores[1](), [1, -1])
des = tf.reshape(self.cores[0](), [-1, 1])
reduced_core = tf.reshape(tf.matmul(des, tar), [-1])
self.adj_matrix[0][0] = self.adj_matrix[0][0] + self.adj_matrix[1][1]
self.output_order[1] = self.output_order[0] + self.output_order[1]
self.output_order.pop(0)
self.adj_matrix = np.delete(self.adj_matrix, 1, 0)
self.adj_matrix = np.delete(self.adj_matrix, 1, 1)
self.dim -= 1
reduced_core_name = self.name_list[1]+self.name_list[0]
self.cores.pop(1)
self.cores.pop(0)
self.cores.insert(0, RealTensor(name=reduced_core_name, from_variable=reduced_core))
self.name_list.pop(1)
self.name_list.pop(0)
self.name_list.insert(0, reduced_core_name)
def __reduce_cores__(self, target_destination):
target, destination = target_destination
for idx in np.ndindex((self.dim, self.dim)):
if len(self.adj_matrix[idx]) > 0:
self.id_matrix[idx] = self.cores[idx[1]].name[0]
else:
self.id_matrix[idx] = ''
for idx, c in enumerate(self.cores):
c.identity = reduce(operator.add, self.id_matrix[idx])
# print (self.id_matrix)
# print (self.adj_matrix)
target_shape = [ int(np.prod(i)) for i in self.adj_matrix[target].tolist() ]
destination_shape = [ int(np.prod(i)) for i in self.adj_matrix[destination].tolist() ]
# print (self.cores[target](), target_shape)
# print (self.cores[destination](), destination_shape)
tar = tf.reshape(self.cores[target](), target_shape)
des = tf.reshape(self.cores[destination](), destination_shape)
tar_trans_list, des_trans_list = list(range(self.dim)), list(range(self.dim))
tar_trans_list = tar_trans_list + [tar_trans_list.pop(destination)]
des_trans_list = [des_trans_list.pop(target)] + des_trans_list
tar, des = tf.transpose(tar, tar_trans_list), tf.transpose(des, des_trans_list)
reduced_core = self.__tf_matmul__(tar, des)
# print (reduced_core)
reduced_trans_list = list(range(self.dim*2-2))
reduce_trans_list_des = reduced_trans_list.pop(destination+self.dim-2)
reduce_trans_list_tar = reduced_trans_list.pop(target)
reduced_trans_list_len = len(reduced_trans_list)//2
reduced_trans_list = [ [ reduced_trans_list[i], reduced_trans_list[i+reduced_trans_list_len] ] for i in range(reduced_trans_list_len)]
reduced_trans_list.insert(destination-1, [reduce_trans_list_tar, reduce_trans_list_des] )
reduced_trans_list = [ k for j in reduced_trans_list for k in j]
# for i in range(self.dim-1):
# reduced_trans_list += [i, i+self.dim-1]
# print (reduced_trans_list)
reduced_core = tf.transpose(reduced_core, reduced_trans_list)
reduced_core = tf.squeeze(reduced_core)
self.adj_matrix[destination, destination] = self.adj_matrix[target, target] + self.adj_matrix[destination, destination]
self.output_order[destination] = self.output_order[target] + self.output_order[destination]
self.output_order.pop(target)
inherit = list(range(self.dim))
inherit.remove(target)
inherit.remove(destination)
# print (self.adj_matrix)
for i in inherit:
self.cores[i].tensor = tf.reshape(self.cores[i](), [ int(np.prod(z)) for z in self.adj_matrix[i].tolist() if int(np.prod(z))>1 ])
self.adj_matrix[destination][i] = self.adj_matrix[target][i] + self.adj_matrix[destination][i]
self.adj_matrix[i][destination] = self.adj_matrix[i][target] + self.adj_matrix[i][destination]
self.id_matrix[i][destination] = self.id_matrix[i][target] + self.id_matrix[i][destination]
self.id_matrix[i][target] = ''
self.adj_matrix = np.delete(self.adj_matrix, target, 1)
# print (self.adj_matrix)
for i in inherit:
if self.cores[i].identity != reduce(operator.add, self.id_matrix[i]):
# print (i, self.cores[i](), self.cores[i].identity, reduce(operator.add, self.id_matrix[i]))
id_tran_tar = list(reduce(operator.add, self.id_matrix[i]))
id_tran_des = list(self.cores[i].identity)
id_tran = [ id_tran_des.index(i) for i in id_tran_tar]
# print (id_tran)
self.cores[i].tensor = tf.transpose(self.cores[i].tensor, id_tran)
# print (i, self.cores[i](), self.cores[i].identity, reduce(operator.add, self.id_matrix[i]))
self.adj_matrix = np.delete(self.adj_matrix, target, 0)
self.id_matrix = np.delete(self.id_matrix, target, 0)
self.id_matrix = np.delete(self.id_matrix, target, 1)
self.dim -= 1
reduced_core_name = self.name_list[target]+self.name_list[destination]
self.cores.pop(destination)
self.cores.pop(target)
self.cores.insert(destination-1, RealTensor(name=reduced_core_name, from_variable=reduced_core))
self.name_list.pop(destination)
self.name_list.pop(target)
self.name_list.insert(destination-1, reduced_core_name)
# print ([ c() for c in self.cores])
# print ('=============')
def __tf_matmul__(self, A, B):
A_shape = A.get_shape().as_list()
B_shape = B.get_shape().as_list()
A = tf.reshape(A, [-1, A_shape[-1]])
B = tf.reshape(B, [B_shape[0], -1])
O_shape = A_shape[:-1] + B_shape[1:]
O = tf.matmul(A, B)
O = tf.reshape(O, O_shape)
return O
def __pre_calculation__(self, target_destination):
target, destination = target_destination
# print (self.adj_matrix)
adj_matrix_k = np.copy(self.adj_matrix)
N_elements = []
for d in range(self.dim):
N_elements.append(np.prod([ np.prod(adj_matrix_k[d][b]) for b in range(self.dim) ]))
N_destination = N_elements.pop(destination)
N_target = N_elements.pop(target)
N_target_destination = N_destination*N_target/np.square(np.prod(adj_matrix_k[target][destination]))
return np.sum(N_elements)+N_target_destination
def reduction(self, random=True):
while len(self.cores) > 1:
triu_indices = np.triu_indices(self.dim, 1)
if len(np.sum(self.adj_matrix[triu_indices])) == 0:
self.__outter_product__()
else:
if random:
connctions = []
for i in range(triu_indices[0].shape[0]):
if len(self.adj_matrix[(triu_indices[0][i], triu_indices[1][i])]) > 0:
connctions.append((triu_indices[0][i], triu_indices[1][i]))
c = choice(connctions)
self.__reduce_cores__(c)
else:
c, cc = None, None
for i in range(triu_indices[0].shape[0]):
if len(self.adj_matrix[(triu_indices[0][i], triu_indices[1][i])]) > 0:
N = self.__pre_calculation__((triu_indices[0][i], triu_indices[1][i]))
if c is None:
c = (triu_indices[0][i], triu_indices[1][i])
if cc is None:
cc = N
if N < cc:
c = (triu_indices[0][i], triu_indices[1][i])
cc = N
self.__reduce_cores__(c)
output = tf.reshape(self.cores[0](), self.adj_matrix[0][0])
# print (self.output_order)
self.output_order = self.output_order[0]
output_trans = np.zeros((self.output_count,), dtype=int)
for i in range(self.output_count):
output_trans[self.output_order[i]] = i
# print(output_trans)
output = tf.transpose(output, output_trans)
output = tf.squeeze(output)
return tf.identity(output, name='output')
def opt_opeartions(self, opt, loss):
return opt.minimize(loss, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES))
if __name__ == '__main__':
# An example of TR
# TR_A = np.array([ [ 2, 6, 0, 0, 0, 0, 0],
# [ 0, 3, 0, 0, 0, 0, 0],
# [ 0, 0, 5, 6, 0, 0, 0],
# [ 0, 0, 0, 7, 0, 0, 0],
# [ 0, 0, 0, 0, 11, 6, 0],
# [ 0, 0, 0, 0, 0, 13, 6],
# [ 0, 0, 0, 0, 0, 0, 17] ])
# tr_a = TensorNetwork(TR_A)
# output_a = tr_a.reduction(False)
# goal_a = tf.random_normal(shape=output_a.get_shape().as_list(), seed=100)
# mse_loss = tf.reduce_mean(tf.square(output_a - goal_a))
# step = tr_a.opt_opeartions(tf.train.AdamOptimizer(0.0001), mse_loss)
# sess = tf.Session()
# sess.run(tf.global_variables_initializer())
# for i in range(10000):
# _, loss = sess.run([step, mse_loss])
# if (i+1)%100 == 0:
# print(loss)
# An exmple of random tensor network
# def generate_random_TN(output_shape, num_cores, max_connection, max_dim):
# matrix = np.diag(output_shape + [0]*(num_cores - len(output_shape)))
# idx = random.choices(np.array(np.triu_indices(num_cores, 1)).transpose(), k=random.randint(2, max_connection))
# for i in idx:
# i = tuple(i)
# print(i)
# matrix[i] = random.randint(2, max_dim)
# return matrix
# a = generate_random_TN([2,3,5,7], 7, 15, 10)
# print (a)
# CP CP CP
def generate_CPTucker(output_shape, num_cores, dim):
if isinstance(output_shape, list):
output_shape = [0] + output_shape
else:
output_shape = [0] + [output_shape] * num_cores
if isinstance(dim, list):
dim = np.array([0] + dim)
else:
dim = np.array([0] + [dim] * num_cores)
matrix = np.diag(output_shape)
matrix[0] = dim
return matrix
# cp = loadmat('data_CP.mat')
# U, X = cp['U'][0], cp['X'][0]
# sess = tf.Session()
# for i in range(48):
# ground_turth = X[i]
# num_cores = U[i].shape[1]
# output_shape = U[i][0][0].shape[0]
# dim = U[i][0][0].shape[1]
# adj_matrix = generate_CPTucker(output_shape, num_cores, dim)
# cp_diag = np.zeros([dim]*num_cores, dtype=np.float32)
# np.fill_diagonal(cp_diag, 1)
# # init_np = [cp_diag] + [ np.array(U[i][0][n]).transpose() for n in range(num_cores)]
# # init_tf = [ tf.convert_to_tensor(i) for i in init_np ]
# init_tf = [ tf.convert_to_tensor(cp_diag) ] + [tf.random_normal_initializer(mean=0.0, stddev=0.1)]*num_cores
# tn = TensorNetwork(adj_matrix, initializer_list=init_tf, scope='TensorNetwork_{}'.format(i),
# trainable_list = [False] + [True] * num_cores)
# output = tn.reduction(False)
# gt = tf.convert_to_tensor(ground_turth)
# mse_loss = tf.losses.mean_squared_error(gt, output)
# step = tn.opt_opeartions(tf.train.AdamOptimizer(0.001), mse_loss)
# sess.run(tf.global_variables_initializer())
# for i in range(100000):
# _, loss = sess.run([step, mse_loss])
# if loss < 1e-7:
# break
# print (i, loss)
# o = sess.run(output)
# diff = np.mean(np.square(ground_turth - o))
# print (diff)
# print (o)
# print (ground_turth)
# print (adj_matrix)
# Tucker Tucker
# tucker = loadmat('data_Tucker.mat')
# G, U, X = tucker['G'][0], tucker['U'][0], tucker['X'][0]
# sess = tf.Session()
# for i in range(22,23):
# ground_turth = X[i]
# num_cores = U[i].shape[1]
# output_shape = U[i][0][0].shape[0]
# dim = U[i][0][0].shape[1]
# adj_matrix = generate_CPTucker(output_shape, num_cores, dim)
# # print (adj_matrix)
# init_np = [G[i]] + [ np.array(U[i][0][n]).transpose() for n in range(num_cores)]
# init_tf = [ tf.convert_to_tensor(i) for i in init_np ]
# # init_tf = [tf.random_normal_initializer(mean=0.0, stddev=0.1)]*(num_cores+1)
# # tn = TensorNetwork(adj_matrix, initializer_list=init_tf, scope='TensorNetwork_{}'.format(i),
# # trainable_list = [True]*(num_cores+1))
# tn = TensorNetwork(adj_matrix, initializer_list=init_tf, scope='TensorNetwork_{}'.format(i))
# output = tn.reduction(False)
# gt = tf.convert_to_tensor(ground_turth)
# mse_loss = tf.losses.mean_squared_error(gt, output)
# step = tn.opt_opeartions(tf.train.AdamOptimizer(0.001), mse_loss)
# sess.run(tf.global_variables_initializer())
# # for i in range(10000):
# # _, loss = sess.run([step, mse_loss])
# # if loss < 1e-7:
# # break
# # print (i, loss)
# o = sess.run(output)
# diff = np.mean(np.square(ground_turth - o))
# print (diff)
# from itertools import permutations
# for idx in permutations(range(3)):
# o = np.transpose(o, idx)
# diff = np.mean(np.square(ground_turth - o))
# print (diff)
# OUTTER PRODUCT
# outter = loadmat('rank1.mat')
# U, X = outter['U'][0], outter['X'][0]
# sess = tf.Session()
# for i in range(9):
# ground_turth = X[i]
# num_cores = len(X[i].shape)
# adj_matrix = np.diag(X[i].shape)
# init_np = [ np.squeeze(np.array(U[i][0][n])) for n in range(num_cores)]
# init_tf = [ tf.convert_to_tensor(i) for i in init_np ]
# tn = TensorNetwork(adj_matrix, initializer_list=init_tf, scope='TensorNetwork_{}'.format(i))
# output = tn.reduction(True)
# sess.run(tf.global_variables_initializer())
# o = sess.run(output)
# diff = np.mean(np.square(ground_turth - o))
# print (diff)
sess = tf.Session()
adjm = np.array([[0,2,2,2,2,0,0,0,0],
[0,11,2,2,2,2,2,2,0],
[0,0,12,2,2,0,0,2,0],
[0,0,0,0,2,2,0,0,2],
[0,0,0,0,0,0,2,2,2],
[0,0,0,0,0,0,0,2,2],
[0,0,0,0,0,0,13,2,2],
[0,0,0,0,0,0,0,14,2],
[0,0,0,0,0,0,0,0,15]] , dtype=int)
TN = TensorNetwork(adjm)
ot = TN.reduction(False)
sess.run(tf.global_variables_initializer())
sess.run(ot)
# opt = tf.train.AdamOptimizer(0.001)
# # opt = tf.train.GradientDescentOptimizer(0.5)
# goal = tf.convert_to_tensor(evoluation_goal)
# goal_square_norm = tf.convert_to_tensor(evoluation_goal_square_norm)
# indv_mse_loss = tf.reduce_mean(tf.square(ot - goal)) / goal_square_norm
# step = opt.minimize(indv_mse_loss)