def get_statistics(self, epoch, domain, data_loader, col, batch_size=600):
log_dict, embed = {}, None
dbis = []
inp = []
label = []
embed2 = []
for clsid in tqdm(range(data_loader.nclass), desc='Get Statistics {}'.format(domain)):
x = data_loader.sample_from_class(clsid, batch_size)
x = np.expand_dims(x, 0)
z = self.sess.run(self.graph['support_z'], feed_dict={self.ph['support']: x, self.ph['query']: x[:, :2, ], self.ph['is_training']: False})
z = z.squeeze()
update_loss(stat.gaussian_test(z), log_dict, False)
update_loss(stat.correlation(z), log_dict, False)
nanasa = np.mean(z, 0, keepdims=True)
dbis.append(np.sqrt(np.mean(np.sum(np.square(z - nanasa), 1), 0)))
embed2.append(nanasa)
update_loss({'mean_std': np.mean(np.std(z, 0))}, log_dict, False)
update_loss(stat.norm(z, 'inclass_'), log_dict, False)
update_loss(stat.pairwise_distance(z[:100,], 'inclass_'), log_dict, False)
inp.append(z[:50, ])
label += [clsid] * 50
embed2 = np.concatenate(embed2, 0)
update_loss(stat.norm(embed2, 'est_'), log_dict, False)
update_loss(stat.pairwise_distance(embed2, 'est_'), log_dict, False)
update_loss(stat.davies_bouldin_index(np.array(dbis), stat.l2_distance(embed2)), log_dict, False)
inputs = np.concatenate(inp, axis=0)
labels = np.array(label)
stat.tsne_visualization(inputs, labels, os.path.join(self.logger.dir,
'epoch{}_{}.png'.format(epoch, domain)), col)
self.logger.print(epoch, domain + '-stat', log_dict)
def get_statistics(self, epoch, domain, data_loader, col, batch_size=600):
log_dict, embed = {}, None
if domain == 'train':
embed = self.sess.run(self.graph['embed'])
update_loss(stat.norm(embed, 'embed_'), log_dict, False)
update_loss(stat.pairwise_distance(embed, 'embed_'), log_dict,
False)
inp = []
label = []
embed2 = []
dbis = []
for clsid in tqdm(range(data_loader.nclass),
desc='Get Statistics {}'.format(domain)):
x = data_loader.sample_from_class(clsid, batch_size)
z = self.sess.run(self.graph['z'],
feed_dict={
self.ph['data']: x,
self.ph['is_training']: False
})
update_loss(stat.gaussian_test(z), log_dict, False)
update_loss(stat.correlation(z), log_dict, False)
nanasa = np.mean(z, 0, keepdims=True)
embed2.append(nanasa)
dbis.append(np.sqrt(np.mean(np.sum(np.square(z - nanasa), 1), 0)))
update_loss({'mean_std': np.mean(np.std(z, 0))}, log_dict, False)
update_loss(stat.norm(z[:100], 'inclass_'), log_dict, False)
update_loss(stat.pairwise_distance(z[:100], 'inclass_'), log_dict,
False)
inp.append(z[:50, ])
label += [clsid] * 50
embed2 = np.concatenate(embed2, 0)
if embed is not None:
update_loss(
{
'mean_div':
np.sqrt(np.sum(np.square(embed - embed2))) / embed.shape[0]
}, log_dict, False)
update_loss(stat.norm(embed2, 'est_'), log_dict, False)
update_loss(stat.pairwise_distance(embed2, 'est_'), log_dict, False)
update_loss(
stat.davies_bouldin_index(np.array(dbis),
stat.l2_distance(embed2)), log_dict,
False)
inputs = np.concatenate(inp, axis=0)
labels = np.array(label)
#stat.tsne_visualization(inputs, labels, os.path.join(self.logger.dir,
# 'epoch{}_{}.png'.format(epoch, domain)), col)
self.logger.print(epoch, domain + '-stat', log_dict)
return self.terminate()