def add_layer(self, layer):
# This implementation of ZeroPadding layers assumes symmetric single
# padding ((1, 1), (1, 1)).
# Todo: Generalize for asymmetric padding or arbitrary size.
if 'ZeroPadding' in layer.__class__.__name__:
# noinspection PyUnresolvedReferences
padding = layer.padding
if set(padding).issubset((1, (1, 1))):
self.change_padding = True
return
else:
raise NotImplementedError(
"Border_mode {} not supported.".format(padding))
# Latest Keras versions need special permutation after Flatten layers.
if 'Flatten' in layer.__class__.__name__ and \
self.config.get('input', 'model_lib') == 'keras':
self.flatten_shapes.append(
(layer.name, get_shape_from_label(self.layers[-1].label)))
return
self.layers.append(
self.sim.Population(np.prod(layer.output_shape[1:],
dtype=np.int).item(),
self.sim.IF_curr_exp,
self.cellparams,
label=layer.name))
self.layers[-1].initialize(v=self.layers[-1].get('v_rest'))
def add_layer(self, layer):
# Latest Keras versions need special permutation after Flatten layers.
if 'Flatten' in layer.__class__.__name__ and \
self.config.get('input', 'model_lib') == 'keras':
self.flatten_shapes.append(
(layer.name, get_shape_from_label(self.layers[-1].label)))
return
self.layers.append(
self.sim.NeuronGroup(np.prod(layer.output_shape[1:]),
model=self.eqs,
method='linear',
reset=self.v_reset,
threshold=self.threshold,
dt=self._dt * self.sim.ms))
self.connections.append(
self.sim.Synapses(self.layers[-2],
self.layers[-1],
'w:1',
on_pre='v+=w',
dt=self._dt * self.sim.ms))
self.layers[-1].add_attribute('label')
self.layers[-1].label = layer.name
if 'spiketrains' in self._plot_keys \
or 'spiketrains_n_b_l_t' in self._log_keys:
self.spikemonitors.append(self.sim.SpikeMonitor(self.layers[-1]))
if 'v_mem' in self._plot_keys or 'mem_n_b_l_t' in self._log_keys:
self.statemonitors.append(
self.sim.StateMonitor(self.layers[-1], 'v', True))
def add_layer(self, layer):
# Latest Keras versions need special permutation after Flatten layers.
if 'Flatten' in layer.__class__.__name__ and \
self.config.get('input', 'model_lib') == 'keras':
self.flatten_shapes.append(
(layer.name, get_shape_from_label(self.layers[-1].label)))
return
self.layers.append(
self.sim.Population(np.prod(layer.output_shape[1:],
dtype=np.int).item(),
self.sim.IF_curr_exp,
self.cellparams,
label=layer.name))
self.layers[-1].initialize(v=self.layers[-1].get('v_rest'))
def get_layer_shapes(self):
from snntoolbox.simulation.utils import get_shape_from_label
return [get_shape_from_label(label) if label not in ["InputLayer", "corr_pop"] else self.input_layer_shape for label in self.layer_names]