def get_sample_experiment():
from hyperopt.pyll.stochastic import sample
from pylearn2.config import yaml_parse
from os.path import join
import sys
sys.path.append('..')
from hyperopt_api.parser import build
from yaml_parser import yaml_parser as yp
from hyperopt_api.search_space import get_search_space
import configuration.model as config
from utils.common import get_timestamp
# prepare all variables that don't need to be updated with each iteration
spa = get_search_space() # define search space over possible models
path = config.data_path
# obtain the yaml skelton
with open(config.yaml_skelton_path) as f:
default_string = f.read()
samp = sample(spa) # generate sample (will give a description of a model)
mod = build(samp) # based on description generated build an object that will fit into yaml_paser
# define weight decay parameters. They depend on the number of layers (there is one parameter fo each layer)
weight_decay_coeffs = yp.parse_weight_decay(mod)
# generate a filename to store the best model
pkl_filename = join(config.path_for_storing, get_timestamp() + "_best.pkl")
# create dictionary with hyper parameters
hyper_params = {'model': yp.parse_to_yaml(mod), 'path': yp.parse_to_yaml(path),
'weight_decay_coeffs': weight_decay_coeffs, 'pkl_filename': pkl_filename}
# fill the yaml skelton with hyperparameters
yaml_string = default_string % hyper_params
network = yaml_parse.load(yaml_string)
return network
from hyperopt.pyll.stochastic import sample
from parser import build
from pylearn2.config import yaml_parse
from os.path import join
import traceback
import sys
sys.path.append('..')
from yaml_parser import yaml_parser as yp
from utils.common import notify, get_timestamp
from hyperopt_api.search_space import get_search_space
from blessings import Terminal
t = Terminal()
import configuration.model as config
# prepare all variables that don't need to be updated with each iteration
spa = get_search_space() # define search space over possible models
# define data paths
path = config.data_path
# obtain the yaml skelton
with open(config.yaml_skelton_path) as f:
default_string = f.read()
# for each sample that will be generated from search space space
for i in xrange(20):
timestamp = get_timestamp()
print t.bold_red('ITERATION:'), t.bold_red(str(i)), "started at: ", timestamp
samp = sample(spa) # generate sample (will give a description of a model)