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Copy pathDSC-Net-L2-COIL20.py
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DSC-Net-L2-COIL20.py
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# Code Authors: Pan Ji, University of Adelaide, [email protected]
# Tong Zhang, Australian National University, [email protected]
# Copyright Reserved!
from __future__ import division, print_function, absolute_import
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.contrib import layers
from sklearn import cluster
from munkres import Munkres
import scipy.io as sio
from scipy.sparse.linalg import svds
from sklearn.preprocessing import normalize
from tensorflow.examples.tutorials.mnist import input_data
class ConvAE(object):
def __init__(self, n_input, kernel_size, n_hidden, reg_const1 = 1.0, reg_const2 = 1.0, reg = None, batch_size = 256,\
denoise = False, model_path = None, logs_path = '/home/pan/workspace-eclipse/deep-subspace-clustering/COIL20CodeModel/new/pretrain/logs'):
#n_hidden is a arrary contains the number of neurals on every layer
self.n_input = n_input
self.n_hidden = n_hidden
self.reg = reg
self.model_path = model_path
self.kernel_size = kernel_size
self.iter = 0
self.batch_size = batch_size
weights = self._initialize_weights()
# model
self.x = tf.placeholder(tf.float32, [None, self.n_input[0], self.n_input[1], 1])
self.learning_rate = tf.placeholder(tf.float32, [])
if denoise == False:
x_input = self.x
latent, shape = self.encoder(x_input, weights)
else:
x_input = tf.add(self.x, tf.random_normal(shape=tf.shape(self.x),
mean = 0,
stddev = 0.2,
dtype=tf.float32))
latent,shape = self.encoder(x_input, weights)
self.z_conv = tf.reshape(latent,[batch_size, -1])
self.z_ssc, Coef = self.selfexpressive_moduel(batch_size)
self.Coef = Coef
latent_de_ft = tf.reshape(self.z_ssc, tf.shape(latent))
self.x_r_ft = self.decoder(latent_de_ft, weights, shape)
self.saver = tf.train.Saver([v for v in tf.trainable_variables() if not (v.name.startswith("Coef"))])
self.cost_ssc = 0.5*tf.reduce_sum(tf.pow(tf.subtract(self.z_conv,self.z_ssc), 2))
self.recon_ssc = tf.reduce_sum(tf.pow(tf.subtract(self.x_r_ft, self.x), 2.0))
self.reg_ssc = tf.reduce_sum(tf.pow(self.Coef,2))
tf.summary.scalar("ssc_loss", self.cost_ssc)
tf.summary.scalar("reg_lose", self.reg_ssc)
self.loss_ssc = self.cost_ssc*reg_const2 + reg_const1*self.reg_ssc + self.recon_ssc
self.merged_summary_op = tf.summary.merge_all()
self.optimizer_ssc = tf.train.AdamOptimizer(learning_rate = self.learning_rate).minimize(self.loss_ssc)
self.init = tf.global_variables_initializer()
self.sess = tf.InteractiveSession()
self.sess.run(self.init)
self.summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
def _initialize_weights(self):
all_weights = dict()
all_weights['enc_w0'] = tf.get_variable("enc_w0", shape=[self.kernel_size[0], self.kernel_size[0], 1, n_hidden[0]],
initializer=layers.xavier_initializer_conv2d(),regularizer = self.reg)
all_weights['enc_b0'] = tf.Variable(tf.zeros([self.n_hidden[0]], dtype = tf.float32))
all_weights['dec_w0'] = tf.get_variable("dec_w0", shape=[self.kernel_size[0], self.kernel_size[0],1, n_hidden[0]],
initializer=layers.xavier_initializer_conv2d(),regularizer = self.reg)
all_weights['dec_b0'] = tf.Variable(tf.zeros([1], dtype = tf.float32))
return all_weights
# Building the encoder
def encoder(self,x, weights):
shapes = []
# Encoder Hidden layer with relu activation #1
shapes.append(x.get_shape().as_list())
layer1 = tf.nn.bias_add(tf.nn.conv2d(x, weights['enc_w0'], strides=[1,2,2,1],padding='SAME'),weights['enc_b0'])
layer1 = tf.nn.relu(layer1)
return layer1, shapes
# Building the decoder
def decoder(self,z, weights, shapes):
# Encoder Hidden layer with relu activation #1
shape_de1 = shapes[0]
layer1 = tf.add(tf.nn.conv2d_transpose(z, weights['dec_w0'], tf.stack([tf.shape(self.x)[0],shape_de1[1],shape_de1[2],shape_de1[3]]),\
strides=[1,2,2,1],padding='SAME'),weights['dec_b0'])
layer1 = tf.nn.relu(layer1)
return layer1
def selfexpressive_moduel(self,batch_size):
Coef = tf.Variable(1.0e-8 * tf.ones([self.batch_size, self.batch_size],tf.float32), name = 'Coef')
z_ssc = tf.matmul(Coef, self.z_conv)
return z_ssc, Coef
def finetune_fit(self, X, lr):
C,l1_cost, l2_cost, summary, _ = self.sess.run((self.Coef, self.reg_ssc, self.cost_ssc, self.merged_summary_op, self.optimizer_ssc), \
feed_dict = {self.x: X, self.learning_rate: lr})
self.summary_writer.add_summary(summary, self.iter)
self.iter = self.iter + 1
return C, l1_cost,l2_cost
def initlization(self):
tf.reset_default_graph()
self.sess.run(self.init)
def transform(self, X):
return self.sess.run(self.z_conv, feed_dict = {self.x:X})
def save_model(self):
save_path = self.saver.save(self.sess,self.model_path)
print ("model saved in file: %s" % save_path)
def restore(self):
self.saver.restore(self.sess, self.model_path)
print ("model restored")
def best_map(L1,L2):
#L1 should be the labels and L2 should be the clustering number we got
Label1 = np.unique(L1)
nClass1 = len(Label1)
Label2 = np.unique(L2)
nClass2 = len(Label2)
nClass = np.maximum(nClass1,nClass2)
G = np.zeros((nClass,nClass))
for i in range(nClass1):
ind_cla1 = L1 == Label1[i]
ind_cla1 = ind_cla1.astype(float)
for j in range(nClass2):
ind_cla2 = L2 == Label2[j]
ind_cla2 = ind_cla2.astype(float)
G[i,j] = np.sum(ind_cla2 * ind_cla1)
m = Munkres()
index = m.compute(-G.T)
index = np.array(index)
c = index[:,1]
newL2 = np.zeros(L2.shape)
for i in range(nClass2):
newL2[L2 == Label2[i]] = Label1[c[i]]
return newL2
def thrC(C,ro):
if ro < 1:
N = C.shape[1]
Cp = np.zeros((N,N))
S = np.abs(np.sort(-np.abs(C),axis=0))
Ind = np.argsort(-np.abs(C),axis=0)
for i in range(N):
cL1 = np.sum(S[:,i]).astype(float)
stop = False
csum = 0
t = 0
while(stop == False):
csum = csum + S[t,i]
if csum > ro*cL1:
stop = True
Cp[Ind[0:t+1,i],i] = C[Ind[0:t+1,i],i]
t = t + 1
else:
Cp = C
return Cp
def post_proC(C, K, d, alpha):
# C: coefficient matrix, K: number of clusters, d: dimension of each subspace
n = C.shape[0]
C = 0.5*(C + C.T)
C = C - np.diag(np.diag(C)) + np.eye(n,n) # for sparse C, this step will make the algorithm more numerically stable
r = d*K + 1
U, S, _ = svds(C,r,v0 = np.ones(n))
U = U[:,::-1]
S = np.sqrt(S[::-1])
S = np.diag(S)
U = U.dot(S)
U = normalize(U, norm='l2', axis = 1)
Z = U.dot(U.T)
Z = Z * (Z>0)
L = np.abs(Z ** alpha)
L = L/L.max()
L = 0.5 * (L + L.T)
spectral = cluster.SpectralClustering(n_clusters=K, eigen_solver='arpack', affinity='precomputed', assign_labels='discretize')
spectral.fit(L)
grp = spectral.fit_predict(L) + 1
return grp, L
def err_rate(gt_s, s):
c_x = best_map(gt_s,s)
err_x = np.sum(gt_s[:] != c_x[:])
missrate = err_x.astype(float) / (gt_s.shape[0])
return missrate
data = sio.loadmat('COIL20.mat')
Img = data['fea']
Label = data['gnd']
Img = np.reshape(Img,(Img.shape[0],32,32,1))
n_input = [32,32]
kernel_size = [3]
n_hidden = [15]
batch_size = 20*72
model_path = '/home/pan/workspace-eclipse/deep-subspace-clustering/COIL20CodeModel/new/pretrain/model.ckpt'
ft_path = '/home/pan/workspace-eclipse/deep-subspace-clustering/COIL20CodeModel/new/pretrain/model.ckpt'
logs_path = '/home/pan/workspace-eclipse/deep-subspace-clustering/COIL20CodeModel/new/pretrain/logs'
num_class = 20 #how many class we sample
num_sa = 72
batch_size_test = num_sa * num_class
iter_ft = 0
ft_times = 30
display_step = ft_times
alpha = 0.04
learning_rate = 1e-3
reg1 = 1.0
reg2 = 150.0
CAE = ConvAE(n_input = n_input, n_hidden = n_hidden, reg_const1 = reg1, reg_const2 = reg2, kernel_size = kernel_size, \
batch_size = batch_size_test, model_path = model_path, logs_path= logs_path)
acc_= []
for i in range(0,1):
coil20_all_subjs = Img
coil20_all_subjs = coil20_all_subjs.astype(float)
label_all_subjs = Label
label_all_subjs = label_all_subjs - label_all_subjs.min() + 1
label_all_subjs = np.squeeze(label_all_subjs)
CAE.initlization()
CAE.restore()
for iter_ft in range(ft_times):
iter_ft = iter_ft+1
C,l1_cost,l2_cost = CAE.finetune_fit(coil20_all_subjs,learning_rate)
if iter_ft % display_step == 0:
print ("epoch: %.1d" % iter_ft, "cost: %.8f" % (l1_cost/float(batch_size_test)))
C = thrC(C,alpha)
y_x, CKSym_x = post_proC(C, num_class, 12 ,8)
missrate_x = err_rate(label_all_subjs,y_x)
acc = 1 - missrate_x
print ("experiment: %d" % i,"acc: %.4f" % acc)
acc_.append(acc)
acc_ = np.array(acc_)
m = np.mean(acc_)
me = np.median(acc_)
print(m)
print(me)
print(acc_)