import tensorflow as tf
import numpy as np
import scipy.io as scio
from utils.Net_ae import Net_ae
from utils.Net_dg import Net_dg
from utils.next_batch import next_batch
import math
from sklearn.utils import shuffle
import timeit
from keras.layers import *
from utils.print_result import print_result
from keras.models import Model
from utils.Dataset import Dataset
data = Dataset('handwritten_2views')
x1, x2, gt = data.load_data()
x1 = data.normalize(x1, 0)
x2 = data.normalize(x2, 0)
n_clusters = len(set(gt))
def xavier_init(fan_in, fan_out, constant=1):
low = -constant * np.sqrt(6.0 / (fan_in + fan_out))
high = constant * np.sqrt(6.0 / (fan_in + fan_out))
return tf.random_uniform((fan_in, fan_out),
minval=low, maxval=high,
dtype=tf.float32)
class dualModel:
def __init__(self,epochs):
self.epochs=epochs
def train_model(self,X1, X2, gt, para_lambda, dims, act, lr, epochs, batch_size):
err_total = list()
start = timeit.default_timer()
from utils.Dataset import Dataset
from model import model_multi_view
from utils.cluster import cluster
import csv
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
print(os.environ['CUDA_VISIBLE_DEVICES'])
'''
each net has its own learning_rate(lr_xx), activation_function(act_xx), nodes_of_layers(dims_xx)
ae net need pretraining before the whole optimizatoin
'''
if __name__ == '__main__':
num = 10
data = Dataset('COIL20_3views')
X, gt = data.load_data()
acc_H_all = np.zeros(num)
nmi_H_all = np.zeros(num)
RI_H_all = np.zeros(num)
f1_H_all = np.zeros(num)
para_lambda = 1
batch_size = X['0'].shape[0]
lr_pre = 1.0e-3
lr_ae = 1.0e-3
lr_dg = 1.0e-3
lr_h = 1.0e-2
epochs_pre = 300
epochs_total = 100
epochs_h = 20
