DSC-Net-L2-UMIST.py

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
import os

from AEnet_13 import ConvAE
from AEutils import  *
import traceback
# SELECT GPU
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"

def test_face(Img, Label, CAE, num_class,lr2=5e-4):

    d = 5
    alpha = 8
    ro = 0.08
    
    acc_= []
    for i in range(0,21-num_class):
        face_10_subjs = np.array(Img[24*i:24*(i+num_class),:])
        face_10_subjs = face_10_subjs.astype(float)        
        label_10_subjs = np.array(Label[24*i:24*(i+num_class)])
        label_10_subjs = label_10_subjs - label_10_subjs.min() + 1
        label_10_subjs = np.squeeze(label_10_subjs) 
                     
        CAE.initlization()        
        CAE.restore() # restore from pre-trained model
        
        max_step = 200#50 + num_class*25# 100+num_class*20
        display_step = 500#max_step/20#10
        # fine-tune network
        epoch = 0
        COLD = None
        lastr = 1.0
        while epoch < max_step:
            epoch = epoch + 1
            cost, Coef,dd,dt = CAE.partial_fit(face_10_subjs, lr2, mode = 'fine')  #
            if epoch % display_step == 0:
                print("epoch: %.1d" % epoch, "cost: %.8f" % (cost[0]/float(batch_size))   )
                print(cost)
                for posti in range(1):
                    display(Coef, label_10_subjs, d, alpha, ro)


            if COLD is not None:
                normc = np.linalg.norm(COLD, ord='fro')
                normcd = np.linalg.norm(Coef - COLD, ord='fro')
                r = normcd/normc
                #print(epoch,r)
                if r < 1.0e-8 and lastr < 1.0e-8:
                    print("early stop")
                    print("epoch: %.1d" % epoch, "cost: %.8f" % (cost[0] / float(batch_size)))
                    print(cost)
                    for posti in range(1):
                        display(Coef, label_10_subjs, d, alpha, ro)
                    break
                lastr = r
            COLD = Coef

        for posti in range(1):
            drawC(Coef)
            acc_x,L,y_pre = display(Coef, label_10_subjs, d, alpha, ro)
            acc_.append(acc_x)
        acc_.append(acc_x)

        # for sd in [4,5,6]:
        #     for sa in [7,8,9]:
        #         for sr in [0.06,0.08]:
        #             print(sd, sa, sr)
        #             display(Coef, label_10_subjs, sd, sa, sr)
    
    acc_ = np.array(acc_)
    mm = np.max(acc_)

    print("%d subjects:" % num_class)    
    print("Max: %.4f%%" % ((1-mm)*100))
    print(acc_) 
    
    return (1-mm)
    
   
        
    
if __name__ == '__main__':
    
    # load face images and labels
    data = sio.loadmat('./Data/umist-32-32.mat')
    Img = data['img']
    Label = data['label']

    model_path = './models/model-32x32-umist.ckpt'
    restore_path = './models/model-32x32-umist.ckpt'
    logs_path = './logs'

    # face image clustering
    n_input = [32, 32]
    kernel_size = [5,3,3]
    n_hidden = [15, 10, 5]
    
    Img = np.reshape(Img,[Img.shape[0],n_input[0],n_input[1],1]) 
    
    all_subjects = [20]
    reg1 = 1.0
    reg02 = 2
    reg03 = 1e-1

    mm = 0
    mreg = [0,0,0,0]

    startfrom = [0, 0, 0]
    mm = 0

    for reg2 in [1e-3,1e-2,1e-1,1,10,100,1e3]:
        for reg3 in [1e-3,1e-2,0.1,0.6,1,10,100]:
            for reg4 in [1e-3,1e-2,1e-1,1,10,100,1e3]:
                for lr2 in [1e-4]:
                    try:
                        print("reg:", reg2, reg3,reg4, lr2)
                        avg = []
                        med = []
                        iter_loop = 0
                        while iter_loop < len(all_subjects):
                            num_class = all_subjects[iter_loop]
                            batch_size = num_class * 24

                            tf.reset_default_graph()
                            CAE = ConvAE(n_input=n_input, n_hidden=n_hidden, reg_constant1=reg1, re_constant2=reg2, re_constant3=reg3, re_constant4=reg4,ds=num_class,\
                                         kernel_size=kernel_size, batch_size=batch_size, model_path=model_path, restore_path=restore_path, logs_path=logs_path)

                            avg_i = test_face(Img, Label, CAE, num_class,lr2)
                            avg.append(avg_i)
                            iter_loop = iter_loop + 1
                            #visualize(Img,Label,CAE)
                        iter_loop = 0

                        if 1-avg[0] > mm:
                            mreg= [reg2,reg3,reg4,lr2]
                            mm = 1-avg[0]
                        print("max:", mreg, mm)

                    except:
                        print("error in ", reg2, reg3, lr2)
                        traceback.print_exc()
                    finally:
                        try:
                            CAE.sess.close()
                        except:
                            ''