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()