def spaun(dimensions):
"""
Builds the Spaun network.
See [1]_ for more details.
Parameters
----------
dimensions : int
Number of dimensions for vector values
Returns
-------
net : `nengo.Network`
benchmark network
References
----------
.. [1] Chris Eliasmith, Terrence C. Stewart, Xuan Choo, Trevor Bekolay,
Travis DeWolf, Yichuan Tang, and Daniel Rasmussen (2012). A large-scale
model of the functioning brain. Science, 338:1202-1205.
Notes
-----
This network needs to be installed via
.. code-block:: bash
pip install git+https://github.com/drasmuss/spaun2.0.git
"""
# pylint: disable=import-outside-toplevel
from _spaun.configurator import cfg
from _spaun.vocabulator import vocab
from _spaun.experimenter import experiment
from _spaun.modules.stim import stim_data
from _spaun.modules.vision import vis_data
from _spaun.modules.motor import mtr_data
from _spaun.spaun_main import Spaun
vocab.sp_dim = dimensions
cfg.mtr_arm_type = None
cfg.set_seed(1)
experiment.initialize(
"A",
stim_data.get_image_ind,
stim_data.get_image_label,
cfg.mtr_est_digit_response_time,
"",
cfg.rng,
)
vocab.initialize(stim_data.stim_SP_labels, experiment.num_learn_actions,
cfg.rng)
vocab.initialize_mtr_vocab(mtr_data.dimensions, mtr_data.sps)
vocab.initialize_vis_vocab(vis_data.dimensions, vis_data.sps)
return Spaun()
def spaun(dimensions):
"""
Builds the Spaun network from [1]_
Parameters
----------
dimensions : int
Number of dimensions for vector values
Returns
-------
net : `nengo.Network`
benchmark network
References
----------
.. [1] Chris Eliasmith, Terrence C. Stewart, Xuan Choo, Trevor Bekolay,
Travis DeWolf, Yichuan Tang, and Daniel Rasmussen (2012). A large-scale
model of the functioning brain. Science, 338:1202-1205.
Notes
-----
This network needs to be installed via
.. code-block:: bash
pip install git+https://github.com/drasmuss/spaun2.0.git
"""
from _spaun.configurator import cfg
from _spaun.vocabulator import vocab
from _spaun.experimenter import experiment
from _spaun.modules.stim import stim_data
from _spaun.modules.vision import vis_data
from _spaun.modules.motor import mtr_data
from _spaun.spaun_main import Spaun
vocab.sp_dim = dimensions
cfg.mtr_arm_type = None
cfg.set_seed(1)
experiment.initialize(
"A", stim_data.get_image_ind, stim_data.get_image_label,
cfg.mtr_est_digit_response_time, "", cfg.rng)
vocab.initialize(
stim_data.stim_SP_labels, experiment.num_learn_actions, cfg.rng)
vocab.initialize_mtr_vocab(mtr_data.dimensions, mtr_data.sps)
vocab.initialize_vis_vocab(vis_data.dimensions, vis_data.sps)
return Spaun()
def build_spaun(dimensions):
vocab.sp_dim = dimensions
cfg.mtr_arm_type = None
cfg.set_seed(1)
experiment.initialize('A', vis_data.get_image_ind,
vis_data.get_image_label,
cfg.mtr_est_digit_response_time, cfg.rng)
vocab.initialize(experiment.num_learn_actions, cfg.rng)
vocab.initialize_mtr_vocab(mtr_data.dimensions, mtr_data.sps)
vocab.initialize_vis_vocab(vis_data.dimensions, vis_data.sps)
with Spaun() as net:
nengo_dl.configure_settings(
trainable=False,
simplifications=[
graph_optimizer.remove_constant_copies,
graph_optimizer.remove_unmodified_resets,
# graph_optimizer.remove_zero_incs,
graph_optimizer.remove_identity_muls
])
return net
cfg.backend = 'spinn'
print "BACKEND: %s" % cfg.backend.upper()
# ----- Batch runs -----
for n in range(args.n):
print("\n======================== RUN %i OF %i ========================" %
(n + 1, args.n))
# ----- Seeeeeeeed -----
if args.seed < 0:
seed = int(time.time())
else:
seed = args.seed
cfg.set_seed(seed)
print "MODEL SEED: %i" % cfg.seed
# ----- Model Configurations -----
vocab.sp_dim = args.d
cfg.data_dir = args.data_dir
# Parse --config options
if args.config is not None:
print "USING CONFIGURATION OPTIONS: "
for cfg_options in args.config:
cfg_opts = cfg_options.split('=')
cfg_param = cfg_opts[0]
cfg_value = cfg_opts[1]
if hasattr(cfg, cfg_param):
print " * cfg: " + str(cfg_options)
def create_spaun_model(n, args, max_probe_time):
print("\n======================== RUN %i OF %i ========================" %
(n + 1, args.n))
# ----- Seeeeeeeed -----
if args.seed < 0:
seed = int(time.time())
else:
seed = args.seed
cfg.set_seed(seed)
print "MODEL SEED: %i" % cfg.seed
# ----- Model Configurations -----
vocab.sp_dim = args.d
cfg.data_dir = args.data_dir
# Parse --config options
if args.config is not None:
print "USING CONFIGURATION OPTIONS: "
for cfg_options in args.config:
cfg_opts = cfg_options.split('=')
cfg_param = cfg_opts[0]
cfg_value = cfg_opts[1]
if hasattr(cfg, cfg_param):
print " * cfg: " + str(cfg_options)
setattr(cfg, cfg_param, eval(cfg_value))
elif hasattr(experiment, cfg_param):
print " * experiment: " + str(cfg_options)
setattr(experiment, cfg_param, eval(cfg_value))
elif hasattr(vocab, cfg_param):
print " * vocab: " + str(cfg_options)
setattr(vocab, cfg_param, eval(cfg_value))
# ----- Check if data folder exists -----
if not (os.path.isdir(cfg.data_dir) and os.path.exists(cfg.data_dir)):
raise RuntimeError('Data directory "%s"' % (cfg.data_dir) +
' does not exist. Please ensure the correct path' +
' has been specified.')
# ----- Enable debug logging -----
if args.debug:
nengo.log('debug')
# ----- Experiment and vocabulary initialization -----
experiment.initialize(args.s, vis_data.get_image_ind,
vis_data.get_image_label,
cfg.mtr_est_digit_response_time, cfg.rng)
vocab.initialize(experiment.num_learn_actions, cfg.rng)
vocab.initialize_mtr_vocab(mtr_data.dimensions, mtr_data.sps)
vocab.initialize_vis_vocab(vis_data.dimensions, vis_data.sps)
# ----- Configure output log files -----
mpi_savename = None
mpi_saveext = None
probe_cfg = None
probe_anim_cfg = None
anim_probe_data_filename = None
if cfg.use_mpi:
sys.path.append('C:\\Users\\xchoo\\GitHub\\nengo_mpi')
mpi_save = args.mpi_save.split('.')
mpi_savename = '.'.join(mpi_save[:-1])
mpi_saveext = mpi_save[-1]
cfg.probe_data_filename = get_probe_data_filename(mpi_savename,
suffix=args.tag)
else:
cfg.probe_data_filename = get_probe_data_filename(suffix=args.tag)
# ----- Initalize looger and write header data -----
logger.initialize(cfg.data_dir, cfg.probe_data_filename[:-4] + '_log.txt')
cfg.write_header()
experiment.write_header()
vocab.write_header()
logger.flush()
# ----- Raw stimulus seq -----
print "RAW STIM SEQ: %s" % (str(experiment.raw_seq_str))
# ----- Spaun proper -----
model = Spaun()
# ----- Display stimulus seq -----
print "PROCESSED RAW STIM SEQ: %s" % (str(experiment.raw_seq_list))
print "STIMULUS SEQ: %s" % (str(experiment.stim_seq_list))
# ----- Calculate runtime -----
# Note: Moved up here so that we have data to disable probes if necessary
runtime = args.t if args.t > 0 else experiment.get_est_simtime()
# ----- Set up probes -----
make_probes = not args.noprobes
if runtime > max_probe_time and make_probes:
print(">>> !!! WARNING !!! EST RUNTIME > %0.2fs - DISABLING PROBES" %
max_probe_time)
make_probes = False
if make_probes:
print "PROBE FILENAME: %s" % cfg.probe_data_filename
probe_cfg = default_probe_config(model, vocab, cfg.sim_dt,
cfg.data_dir, cfg.probe_data_filename)
# ----- Set up animation probes -----
if args.showanim or args.showiofig or args.probeio:
anim_probe_data_filename = cfg.probe_data_filename[:-4] + '_anim.npz'
print "ANIM PROBE FILENAME: %s" % anim_probe_data_filename
probe_anim_cfg = default_anim_config(model, vocab, cfg.sim_dt,
cfg.data_dir,
anim_probe_data_filename)
# ----- Neuron count debug -----
print "MODEL N_NEURONS: %i" % (get_total_n_neurons(model))
if hasattr(model, 'vis'):
print "- vis n_neurons: %i" % (get_total_n_neurons(model.vis))
if hasattr(model, 'ps'):
print "- ps n_neurons: %i" % (get_total_n_neurons(model.ps))
if hasattr(model, 'bg'):
print "- bg n_neurons: %i" % (get_total_n_neurons(model.bg))
if hasattr(model, 'thal'):
print "- thal n_neurons: %i" % (get_total_n_neurons(model.thal))
if hasattr(model, 'enc'):
print "- enc n_neurons: %i" % (get_total_n_neurons(model.enc))
if hasattr(model, 'mem'):
print "- mem n_neurons: %i" % (get_total_n_neurons(model.mem))
if hasattr(model, 'trfm'):
print "- trfm n_neurons: %i" % (get_total_n_neurons(model.trfm))
if hasattr(model, 'dec'):
print "- dec n_neurons: %i" % (get_total_n_neurons(model.dec))
if hasattr(model, 'mtr'):
print "- mtr n_neurons: %i" % (get_total_n_neurons(model.mtr))
# ----- Connections count debug -----
print "MODEL N_CONNECTIONS: %i" % (len(model.all_connections))
return (model, mpi_savename, mpi_saveext, runtime, make_probes, probe_cfg,
probe_anim_cfg, anim_probe_data_filename)
cfg.backend = 'spinn'
print "BACKEND: %s" % cfg.backend.upper()
# ----- Batch runs -----
for n in range(args.n):
print ("\n======================== RUN %i OF %i ========================" %
(n + 1, args.n))
# ----- Seeeeeeeed -----
if args.seed < 0:
seed = int(time.time())
else:
seed = args.seed
cfg.set_seed(seed)
print "MODEL SEED: %i" % cfg.seed
# ----- Model Configurations -----
vocab.sp_dim = args.d
cfg.data_dir = args.data_dir
# Parse --config options
if args.config is not None:
print "USING CONFIGURATION OPTIONS: "
for cfg_options in args.config:
cfg_opts = cfg_options.split('=')
cfg_param = cfg_opts[0]
cfg_value = cfg_opts[1]
if hasattr(cfg, cfg_param):
print " * cfg: " + str(cfg_options)