print k, opt_params[k].shape
opt_params = np.load(
'./chkpt-ipython/DMM_lr-0_0008-dh-40-ds-2-nl-relu-bs-200-ep-40-rs-80-rd-0_1-infm-R-tl-2-el-2-ar-2_0-use_p-approx-rc-lstm-uid-EP30-params.npz'
)
for k in opt_params:
print k, opt_params[k].shape
import glob, os, sys, time
sys.path.append('../')
from utils.misc import getConfigFile, readPickle, displayTime
start_time = time.time()
from model_th.dmm import DMM
import model_th.learning as DMM_learn
import model_th.evaluate as DMM_evaluate
displayTime('importing DMM', start_time, time.time())
#This is the prefix we will use
DIR = './chkpt-ipython/'
prefix = 'DMM_lr-0_0008-dh-40-ds-2-nl-relu-bs-200-ep-40-rs-80-rd-0_1-infm-R-tl-2-el-2-ar-2_0-use_p-approx-rc-lstm-uid'
pfile = os.path.join(DIR, prefix + '-config.pkl')
print 'Hyperparameters in: ', pfile, 'Found: ', os.path.exists(pfile)
#The hyperparameters are saved in a pickle file - lets load them here
params = readPickle(pfile, quiet=True)[0]
#Reload the model at Epoch 30
EP = '-EP30'
#File containing model paramters
reloadFile = os.path.join(DIR, prefix + EP + '-params.npz')
print 'Model parameters in: ', reloadFile
""" Add dataset and NADE parameters to "params"
which will become part of the model
"""
for k in ['dim_observations', 'data_type']:
params[k] = dataset[k]
mapPrint('Options: ', params)
if params['use_nade']:
params['data_type'] = 'binary_nade'
"""
import DKF + learn/evaluate functions
"""
start_time = time.time()
from stinfmodel.dkf import DKF
import stinfmodel.learning as DKF_learn
import stinfmodel.evaluate as DKF_evaluate
displayTime('import DKF', start_time, time.time())
dkf = None
#Remove from params
start_time = time.time()
removeIfExists('./NOSUCHFILE')
reloadFile = params.pop('reloadFile')
""" Reload parameters if reloadFile exists otherwise setup model from scratch
and initialize parameters randomly.
"""
if os.path.exists(reloadFile):
pfile = params.pop('paramFile')
""" paramFile is set inside the BaseClass in theanomodels
to point to the pickle file containing params"""
assert os.path.exists(pfile), pfile + ' not found. Need paramfile'
print 'Reloading trained model from : ', reloadFile
#Setup VAE Model (or reload from existing savefile)
start_time = time.time()
from optvaemodels.vae import VAE as Model
import optvaemodels.vae_learn as Learn
import optvaemodels.vae_evaluate as Evaluate
import optvaemodels.evaluate_vecs as EVECS
additional_attrs = {}
if params['data_type'] == 'bow':
additional_attrs = {}
tfidf = TfidfTransformer(norm=None)
tfidf.fit(dataset['train'])
#Get normalized idf vectors
additional_attrs['idf'] = tfidf.idf_
displayTime('import vae', start_time, time.time())
vae = None
#Remove from params
start_time = time.time()
removeIfExists('./NOSUCHFILE')
reloadFile = params.pop('reloadFile')
if os.path.exists(reloadFile):
pfile = params.pop('paramFile')
assert os.path.exists(pfile), pfile + ' not found. Need paramfile'
print 'Reloading trained model from : ', reloadFile
print 'Assuming ', pfile, ' corresponds to model'
model = Model(params,
paramFile=pfile,
reloadFile=reloadFile,
additional_attrs=additional_attrs)
else:
dataset = load(params['dataset'])
params['savedir'] += '-' + params['dataset']
createIfAbsent(params['savedir'])
for k in ['dim_observations', 'data_type']:
params[k] = dataset[k]
mapPrint('Options: ', params)
"""
Import files for learning
"""
start_time = time.time()
from model_th.dmm import DMM
import model_th.learning as DMM_learn
import model_th.evaluate as DMM_evaluate
displayTime('import DMM', start_time, time.time())
dmm = None
"""
Reload from savefile or train new model
"""
start_time = time.time()
removeIfExists('./NOSUCHFILE')
reloadFile = params.pop('reloadFile')
if os.path.exists(reloadFile):
pfile = params.pop('paramFile')
assert os.path.exists(pfile), pfile + ' not found. Need paramfile'
print 'Reloading trained model from : ', reloadFile
print 'Assuming ', pfile, ' corresponds to model'
dmm = DMM(params, paramFile=pfile, reloadFile=reloadFile)
else:
pfile = params['savedir'] + '/' + params['unique_id'] + '-config.pkl'
if params['dataset'] == '':
params['dataset'] = 'jsb'
dataset = loadDataset(params['dataset'])
params['savedir'] += '-' + params['dataset']
createIfAbsent(params['savedir'])
#Saving/loading
for k in ['dim_observations', 'data_type']:
params[k] = dataset[k]
mapPrint('Options: ', params)
#Setup VAE Model (or reload from existing savefile)
start_time = time.time()
from models.lstm import LSTM
displayTime('import LSTM', start_time, time.time())
lstm = None
#Remove from params
start_time = time.time()
removeIfExists('./NOSUCHFILE')
reloadFile = params.pop('reloadFile')
if os.path.exists(reloadFile):
pfile = params.pop('paramFile')
assert os.path.exists(pfile), pfile + ' not found. Need paramfile'
print 'Reloading trained model from : ', reloadFile
print 'Assuming ', pfile, ' corresponds to model'
lstm = LSTM(params, paramFile=pfile, reloadFile=reloadFile)
else:
pfile = params['savedir'] + '/' + params['unique_id'] + '-config.pkl'
print 'Training model from scratch. Parameters in: ', pfile
