])
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
save_weights = sess.run(
[variables_graph[i] for i in range(len(variables_graph))])
utils_resnet.save_model(save_path + 'model-iteration' + str(nb_cl) +
'-%i.pickle' % itera,
scope='ResNet18',
sess=sess)
# Reset the graph
tf.reset_default_graph()
## Exemplars management part ##
nb_protos_cl = int(np.ceil(
nb_proto * nb_groups * 1. /
(itera + 1))) # Reducing number of exemplars for the previous classes
files_from_cl = files_train[itera]
inits, scores, label_batch, loss_class, file_string_batch, op_feature_map = utils_icarl.reading_data_and_preparing_network(
files_from_cl, gpu, itera, batch_size, train_path, labels_dic, mixing,
nb_groups, nb_cl, save_path)
with tf.Session(config=config) as sess:
if (i+1)%80 == 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
save_weights = sess.run([variables_graph[i] for i in range(len(variables_graph))])
utils_resnet.save_model(save_path+'model-iteration'+str(nb_cl)+'-%i.pickle' % itera, scope='ResNet18', sess=sess)
# Reset the graph
tf.reset_default_graph()
## Exemplars management part ##
nb_protos_cl = int(np.ceil(nb_proto*nb_groups*1./(itera+1))) # Reducing number of exemplars for the previous classes
files_from_cl = files_train[itera]
inits,scores,label_batch,loss_class,file_string_batch,op_feature_map = utils_icarl.reading_data_and_preparing_network(files_from_cl, gpu, itera, batch_size, train_path, labels_dic, mixing, nb_groups, nb_cl, save_path)
with tf.Session(config=config) as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
void3 = sess.run(inits)
# Load the training samples of the current batch of classes in the feature space to apply the herding algorithm