def px(pae_dict):
saver = tf.train.Saver()
# P(x) Session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
# Restore pretrained model
saver.restore(sess, pxfinestr.split('.meta')[0])
if cp.get('Experiment', 'PREFIX') == 'MNIST':
# Save hidden/output layer results for pipeline training
px_Z_latent = utils.run_OOM(sess,
pae_dict['conv_in'],
XX_full,
pae_dict['conv_z'],
batch_size=batch_size)
else:
px_Z_latent = utils.run_OOM(sess,
pae_dict['sda_in'],
XX_full,
pae_dict['sda_hidden'],
batch_size=batch_size)
# Print clustering ACC
utils.log_accuracy(cp, YY_full, px_Z_latent, 'PX - ACC FULL', SEED)
# Print clustering NMI
utils.log_NMI(cp, YY_full, px_Z_latent, 'PX - NMI FULL', SEED)
# Print clustering CHS score
utils.log_CHS(cp, XX_full, px_Z_latent, 'PX - CHS FULL', SEED)
sess.close()
def evitram(evitramd):
saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
# Restore pretrained model
saver.restore(sess, evitramfinestr.split('.meta')[0])
if cp.get('Experiment', 'PREFIX') == 'MNIST':
# Save hidden/output layer results for pipeline training
px_Z_latent = utils.run_OOM(sess,
evitram_dict['conv_in'],
XX_full,
evitram_dict['conv_z'],
batch_size=batch_size)
# Save latent space
utils.save_OOM(sess,
evitram_dict['conv_in'],
XX_full,
evitram_dict['conv_z'],
path='COND_' + cp.get('Experiment', 'PX_Z_FULL'),
batch_size=batch_size)
# Save reconstruction
utils.save_OOM(sess,
evitram_dict['conv_in'],
XX_full,
evitram_dict['conv_out'],
path='COND_' +
cp.get('Experiment', 'PX_XREC_TRAIN'),
batch_size=batch_size)
else:
px_Z_latent = utils.run_OOM(sess,
evitram_dict['sda_in'],
XX_full,
evitram_dict['sda_hidden'],
batch_size=batch_size)
utils.save_OOM(sess,
evitram_dict['sda_in'],
XX_full,
evitram_dict['sda_hidden'],
path='COND_' + cp.get('Experiment', 'PX_Z_FULL'),
batch_size=batch_size)
# Print clustering ACC
utils.log_accuracy(cp, YY_full, px_Z_latent, 'COND - ACC FULL', SEED)
# Print clustering NMI
utils.log_NMI(cp, YY_full, px_Z_latent, 'COND - NMI FULL', SEED)
# Print clustering CHS score
utils.log_CHS(cp, XX_full, px_Z_latent, 'COND - CHS FULL', SEED)
sess.close()
def evitram(evitramd):
saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
# Restore pretrained model
saver.restore(sess, evitramfinestr.split('.meta')[0])
if cp.get('Experiment', 'PREFIX') == 'MNIST' or \
cp.get('Experiment', 'PREFIX') == 'AMNIST':
# Save hidden/output layer results for pipeline training
px_Z_latent = utils.run_OOM(sess, evitram_dict['conv_in'], XX_full,
evitram_dict['conv_z'],
batch_size=batch_size)
elif cp.get('Experiment', 'PREFIX') == 'WEATHER':
# Save hidden/output layer results for pipeline training
px_Z_latent_tr = utils.run_OOM(sess, evitram_dict['conv_in'], XX_full,
evitram_dict['conv_z'],
batch_size=batch_size)
px_Z_latent_te = utils.run_OOM(sess, evitram_dict['conv_in'], XX_test,
evitram_dict['conv_z'],
batch_size=batch_size)
else:
px_Z_latent_tr = utils.run_OOM(sess, evitram_dict['sda_in'], XX_full,
evitram_dict['sda_hidden'],
batch_size=batch_size)
if not(np.array_equal(XX_test, np.zeros(shape=(1,1)))):
px_Z_latent_te = utils.run_OOM(sess, evitram_dict['sda_in'], XX_test,
evitram_dict['sda_hidden'],
batch_size=batch_size)
if 'WEATHER' in cp.get('Experiment', 'PREFIX'):
# Print clustering ACC
utils.log_accuracy_isof(cp, YY_full, px_Z_latent_tr,
'COND - ACC FULL (Train)', SEED)
if not(np.array_equal(XX_test, np.zeros(shape=(1,1)))):
utils.log_accuracy_isof(cp, YY_test, px_Z_latent_te,
'COND - ACC FULL (Test)', SEED)
utils.log_anomalyPRF_isof(cp, YY_full, px_Z_latent_tr,
'COND - PRF FULL (Test)', SEED)
if not(np.array_equal(XX_test, np.zeros(shape=(1,1)))):
utils.log_anomalyPRF_isof(cp, YY_test, px_Z_latent_te,
'COND - PRF FULL (Test)', SEED)
else:
# Print clustering ACC
utils.log_accuracy(cp, YY_full, px_Z_latent,
'PX - ACC FULL', SEED)
# Print clustering NMI
utils.log_NMI(cp, YY_full, px_Z_latent,
'PX - NMI FULL', SEED)
# Print clustering CHS score
utils.log_CHS(cp, XX_full, px_Z_latent,
'PX - CHS FULL', SEED)
sess.close()