def __init__(self, **kwargs):
self.config = kwargs.pop(str('config'), None)
if self.config is None:
from snntoolbox.bin.utils import load_config
# Todo: Enable loading config here. Needed when trying to load a
# converted SNN from disk. For now we specify a dummy path.
try:
self.config = load_config('wdir/log/gui/test/.config')
except FileNotFoundError:
raise NotImplementedError
self.layer_type = self.class_name
self.dt = self.config.getfloat('simulation', 'dt')
self.duration = self.config.getint('simulation', 'duration')
self.tau_refrac = self.config.getfloat('cell', 'tau_refrac')
self._v_thresh = self.config.getfloat('cell', 'v_thresh')
self.v_thresh = None
self.time = None
self.mem = self.spiketrain = self.impulse = self.spikecounts = None
self.mem_input = None # Used in MaxPooling layers
self.refrac_until = self.max_spikerate = None
if clamp_var:
self.spikerate = self.var = None
from snntoolbox.utils.utils import get_abs_path
path, filename = \
get_abs_path(self.config.get('paths', 'filename_clamp_indices'),
self.config)
if filename != '':
filepath = os.path.join(path, filename)
assert os.path.isfile(filepath), \
"File with clamp indices not found at {}.".format(filepath)
self.filename_clamp_indices = filepath
self.clamp_idx = None
self.payloads = None
self.payloads_sum = None
self.online_normalization = self.config.getboolean(
'normalization', 'online_normalization')
allowed_kwargs = {
'input_shape',
'batch_input_shape',
'batch_size',
'dtype',
'name',
'trainable',
'weights',
'input_dtype', # legacy
}
for kwarg in kwargs.copy():
if kwarg not in allowed_kwargs:
kwargs.pop(kwarg)
Layer.__init__(self, **kwargs)
self.stateful = True
self._floatx = tf.keras.backend.floatx()
def __init__(self, **kwargs):
self.config = kwargs.pop(str('config'), None)
self.layer_type = self.class_name
self.dt = self.config.getfloat('simulation', 'dt')
self.duration = self.config.getint('simulation', 'duration')
self.tau_refrac = self.config.getfloat('cell', 'tau_refrac')
# if 'v_thresh' in kwargs:
# self._v_thresh = kwargs['v_thresh']
# else:
# self._v_thresh = self.config.getfloat('cell', 'v_thresh')
self._v_thresh = self.config.getfloat('cell', 'v_thresh')
self.v_thresh = None
self.time = None
self.mem = self.spiketrain = self.impulse = self.spikecounts = None
self.refrac_until = self.max_spikerate = None
if bias_relaxation:
self.b0 = None
if clamp_var:
self.spikerate = self.var = None
import os
from snntoolbox.utils.utils import get_abs_path
path, filename = \
get_abs_path(self.config.get('paths', 'filename_clamp_indices'),
self.config)
if filename != '':
filepath = os.path.join(path, filename)
assert os.path.isfile(filepath), \
"File with clamp indices not found at {}.".format(filepath)
self.filename_clamp_indices = filepath
self.clamp_idx = None
self.payloads = self.config.getboolean('cell', 'payloads')
self.payloads_sum = None
self.online_normalization = self.config.getboolean(
'normalization', 'online_normalization')
allowed_kwargs = {'input_shape',
'batch_input_shape',
'batch_size',
'dtype',
'name',
'trainable',
'weights',
'input_dtype', # legacy
}
for kwarg in kwargs.copy():
if kwarg not in allowed_kwargs:
kwargs.pop(kwarg)
Layer.__init__(self, **kwargs)
self.stateful = True