人工智能-项目实践-增量学习-基于优选保留集的增量学习.zip

import tensorflow as tf import pickle import utils_cifar import sys import os import time import string import random import numpy as np import utils_data import set_data_path try: import cPickle except: import pickle as cPickle config = tf.ConfigProto() config.gpu_options.allow_growth = True ##############Incremental Learning Setting###################### gpu = '0' batch_size = 72 # Batch size n = 5 # Set the depth of the architecture: n = 5 -> 32 layers (See He et al. paper) nb_val = 0 # Validation samples per class nb_cl = 10 # Classes per group nb_groups = int(100/nb_cl) nb_protos = 10 # Number of prototypes per class at the end: total protoset memory/ total number of classes epochs = 50 # Total number of epochs lr_old = 0.05 # Initial learning rate lr_strat = [30, 40] # Epochs where learning rate gets decreased lr_factor = 5. # Learning rate decrease factor wght_decay = 0.00001 # Weight Decay nb_runs = 1 # 总的执行次数 Number of runs (random ordering of classes at each run)10*10=100类 np.random.seed(1993) # Fix the random seed config = tf.ConfigProto() config.gpu_options.allow_growth = True Cifar_train_file, Cifar_test_file, save_path = set_data_path.get_data_path() ################################################################ #loading dataset print("\n") # Initialization dictionary_size = 500-nb_val #top1_acc_list_cumul = np.zeros((100/nb_cl,3,nb_runs)) #top1_acc_list_ori = np.zeros((100/nb_cl,3,nb_runs)) #执行多次................................. for step_classes in [2,5,10,20,50]: save_model_path = save_path + 'step_' + str(step_classes) + '_classes' + '/NCM/' nb_cl = step_classes # Classes per group nb_groups = int(100 / nb_cl) loss_batch = [] class_means = np.zeros((128, 100, 2, nb_groups)) files_protoset = [] for i in range(100): files_protoset.append([]) for itera in range(nb_groups):#100/nb_cl if itera == 0:#第一次迭代增加批次 后面网络被初始化 效率提高 epochs = 80 else: epochs = 50 """ 1、先构建网络，定义一些变量 2、构建损失函数 3、构建循环网络 4、筛选保留集样本 5、先实现残差网络　再实现增量学习 6、实现简单的残差网络 """ # Select the order for the class learning order = np.load('./order.npy',encoding='latin1') # Create neural network model print('Run {0} starting ...'.format(itera)) print("Building model and compiling functions...") image_train, label_train,image_test, label_test = utils_data.load_data(Cifar_train_file, Cifar_test_file) #next batch image_batch, label_batch_0, file_protoset_batch = utils_data.Prepare_train_data_batch(image_train,label_train,files_protoset,itera,order,nb_cl,batch_size) label_batch = tf.one_hot(label_batch_0, 100) #初次训练 if itera == 0: #不需要蒸馏 variables_graph, variables_graph2, scores, scores_stored = utils_cifar.prepareNetwork(gpu,image_batch) with tf.device('/gpu:0'): scores = tf.concat(scores,0) l2_reg = wght_decay * tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES, scope='ResNet34')) loss_class = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=label_batch, logits=scores)) loss = loss_class + l2_reg learning_rate = tf.placeholder(tf.float32, shape=[]) opt = tf.train.MomentumOptimizer(learning_rate, 0.9)#需要修改下下 train_step = opt.minimize(loss,var_list=variables_graph) elif itera >0: #知识蒸馏 variables_graph, variables_graph2, scores, scores_stored = utils_cifar.prepareNetwork(gpu, image_batch) #将上一次网络的输出作为软标签 op_assign = [(variables_graph2[i]).assign(variables_graph[i]) for i in range(len(variables_graph))] with tf.device('/gpu:0'): scores = tf.concat(scores, 0) scores_stored = tf.concat(scores_stored, 0) old_cl = (order[range(itera * nb_cl)]).astype(np.int32) new_cl = (order[range(itera * nb_cl, nb_groups * nb_cl)]).astype(np.int32) # ？￥￥￥￥￥￥￥￥￥￥￥￥￥￥￥ label_old_classes = tf.sigmoid(tf.stack([scores_stored[:, i] for i in old_cl], axis=1)) label_new_classes = tf.stack([label_batch[:, i] for i in new_cl], axis=1) pred_old_classes = tf.stack([scores[:, i] for i in old_cl], axis=1) pred_new_classes = tf.stack([scores[:, i] for i in new_cl], axis=1) l2_reg = wght_decay * tf.reduce_sum( tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES, scope='ResNet34')) loss_class = tf.reduce_mean(tf.concat( [tf.nn.sigmoid_cross_entropy_with_logits(labels=label_old_classes, logits=pred_old_classes), tf.nn.sigmoid_cross_entropy_with_logits(labels=label_new_classes, logits=pred_new_classes)], 1)) loss = loss_class + l2_reg learning_rate = tf.placeholder(tf.float32, shape=[]) opt = tf.train.MomentumOptimizer(learning_rate, 0.9) train_step = opt.minimize(loss, var_list=variables_graph) with tf.Session(config=config) as sess: #Launch the data reader coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) sess.run(tf.global_variables_initializer()) lr = lr_old # Run the loading of the weights for the learning network and the copy network if itera > 0: void0 = sess.run([(variables_graph[i]).assign(save_weights[i]) for i in range(len(variables_graph))]) void1 = sess.run(op_assign) print('training*****************************************************') print("Batch of classes {} out of {} batches".format(itera, 100 / nb_cl)) for epoch in range(epochs): # 训练模型 print('Epoch %i' % epoch) # print(len(files_from_cl)) for i in range(int(np.ceil(500*nb_cl/ batch_size))): # 5000/128 loss_class_val, _, sc, lab = sess.run([loss_class, train_step, scores, label_batch_0], feed_dict={learning_rate: lr}) loss_batch.append(loss_class_val) # Plot the training error every 10 batches if len(loss_batch) == 10: print("Training error:") print(np.mean(loss_batch)) loss_batch = [] # Plot the training top 1 accuracy every 80 batches # print('i=', i) if (i + 1) % 20 == 0: stat = [] stat += ([ll in best for ll, best in zip(lab, np.argsort(sc, axis=1)[:, -1:])]) stat = np.average(stat) print('Training accuracy %f' % stat) # Decrease the learning by 5 every 10 epoch after 20 epochs at the first learning rate if epoch in lr_strat: lr /= lr_factor coord.request_stop() coord.join(threads) # copy weights to store network print('saving model') save_weights = sess.run([variables_graph[i] for i in range(len(variables_graph))]) utils_cifar.save_model(save_model_path + 'model-iterati