def SAE_evidence(sae_dict, cp2, ae_ids):
# Init variables and parameters
init = tf.global_variables_initializer()
saver = tf.train.Saver()
# Initialize model save string
saemodelstr = saefinestr.split('.meta')[0]
# Load evidence
K = utils.load_evidence(cp2.get('Experiment', 'EVIDENCEDATAPATH'))
# _full = np.concatenate((K.train.one, K.test.one))
_full = K.train.one
# p = utils.get_perm(perm_str, _full)
# EV = _full[p]
EV = _full
batch_size = cp2.getint('Hyperparameters', 'BatchSize')
# Start Session (Layerwise training)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
# Initialize graph variables
init.run()
ev_dict = {
'cp2': cp2,
'sess': sess,
'saver': saver,
'EV': EV,
'savestr': saemodelstr,
'ae_ids': ae_ids,
'argv2': sys.argv[2]
}
EviAE.train(ev_dict, sae_dict)
# Save hidden/output layer results for pipeline training
utils.save_OOM(sess,
sae_dict['sda_in'],
EV,
sae_dict['sda_hidden'],
path=cp2.get('Experiment', 'PX_Z_TRAIN'),
batch_size=batch_size)
utils.save_OOM(sess,
sae_dict['sda_in'],
EV,
sae_dict['sda_out'],
path=cp2.get('Experiment', 'PX_XREC'),
batch_size=batch_size)
sess.close()
def evitram():
# Restore pretrained model
restorestr = pxfinestr.split('.meta')[0]
# Save model str
evitramstr = evitramfinestr.split('.meta')[0]
# Load pretrained evidence representations for all sources
K = []
for e in sys.argv[3:]:
cp2 = utils.load_config(e)
K.append(cp2.get('Experiment', 'PX_Z_TRAIN'))
sect = 'Experiment'
ev_paths = [cp.get(sect, i) for i in cp.options(sect) if 'evidence' in i]
if cp.get('Experiment', 'PREFIX') == 'MNIST' or \
cp.get('Experiment', 'PREFIX') == 'AMNIST':
evitram_dict = ConvAE.build_EviTRAM(cp, SEED)
elif cp.get('Experiment', 'PREFIX') == 'WEATHER-TEMPO':
evitram_dict = WConvAE.build_EviTRAM(cp, SEED)
else:
# Layerwise autoencoder number
ae_ids = [str(i) for i in xrange(cp.getint('Experiment', 'AENUM'))]
evitram_dict = SAE.build_EviTRAM(cp, ae_ids, SEED)
# Get variables to restore from pretrained model P(x) Encoder
var_list = tf.trainable_variables()
for ev_path_id, ev_path in enumerate(ev_paths):
if cp.get('Experiment', 'PREFIX') == 'MNIST' or \
cp.get('Experiment', 'PREFIX') == 'AMNIST' or \
cp.get('Experiment', 'PREFIX') == 'WEATHER-TEMPO':
# Prepare "restore" variable list
for v in var_list:
if v.name == 'Pre_Q' + str(ev_path_id) + '/kernel:0':
var_list.remove(v)
for v in var_list:
if v.name == 'Pre_Q' + str(ev_path_id) + '/bias:0':
var_list.remove(v)
else:
# Prepare "restore" variable list
for v in var_list:
if v.name == 'Pre_Q' + str(ev_path_id) + '/kernel:0':
var_list.remove(v)
for v in var_list:
if v.name == 'Pre_Q' + str(ev_path_id) + '/bias:0':
var_list.remove(v)
for v in var_list:
if v.name == 'Pre_Comp_Q' + str(ev_path_id) + '/kernel:0':
var_list.remove(v)
for v in var_list:
if v.name == 'Pre_Comp_Q' + str(ev_path_id) + '/bias:0':
var_list.remove(v)
##########################################################
# Tensorboard (comment / uncomment)
##########################################################
from datetime import datetime
now = datetime.utcnow().strftime("%m-%d_%H-%M:%S")
root_logdir = cp.get('Experiment', 'ModelOutputPath')
logdir = "{}/{}{}-{}/".format(
root_logdir,
cp.get('Experiment', 'PREFIX') + '_' +
cp.get('Experiment', 'Enumber') + '_cond', sys.argv[2], now)
tf.summary.scalar(name='cond loss', tensor=evitram_dict['evitram_loss'])
tf.summary.scalar(name='recon loss', tensor=evitram_dict['px_mse'])
summary = tf.summary.merge_all()
file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())
##########################################################
# Initialize & restore P(x) AE weights
init = tf.global_variables_initializer()
saver = tf.train.Saver(var_list)
saverCOND = tf.train.Saver()
# Task outcomes
# EV = [np.load(i) for i in K]
K = utils.load_evidence(cp2.get('Experiment', 'EVIDENCEDATAPATH'))
if cp.get('Experiment', 'PREFIX') == 'WEATHER-TEMPO':
from sklearn.preprocessing import OneHotEncoder as OHE
EV = K.train.labels
EV = np.repeat(EV, cp.getint('Input', 'Frames'))
EV = OHE(sparse=False).fit_transform(EV.reshape(len(EV), 1))
EV = [EV]
else:
EV = K.train.one
# EV = EV[p]
EV = [EV]
# Start Session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
# Init values
init.run()
# Restore finetuned model
saver.restore(sess, restorestr)
train_dict = {
'cp': cp,
'sess': sess,
'data': XX_full,
'sumr': summary,
'savestr': evitramstr,
'saver': saverCOND,
'fw': file_writer,
'EV': EV,
'ev_paths': ev_paths
}
if cp.get('Experiment', 'PREFIX') == 'MNIST' or \
cp.get('Experiment', 'PREFIX') == 'AMNIST':
ConvAE.evitram_train(train_dict, evitram_dict)
elif cp.get('Experiment', 'PREFIX') == 'WEATHER-TEMPO':
WConvAE.evitram_train(train_dict, evitram_dict)
else:
SAE.evitram_train(train_dict, evitram_dict)
# Get batch size for batch output save
batch_size = train_dict['cp'].getint('Hyperparameters', 'BatchSize')
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-TEMPO':
px_Z_latent = utils.run_OOM(sess,
evitram_dict['conv_in'],
XX_full,
evitram_dict['conv_z'],
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, pae_dict['conv_in'], XX_full,
# pae_dict['conv_out'],
# path=cp.get('Experiment', 'PX_XREC_TRAIN'),
# 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)
sess.close()