# Record spikes during the simulation.
spike_record = torch.zeros((update_interval, int(time / dt), n_neurons),
device=device)
# Neuron assignments and spike proportions.
n_classes = 10
assignments = -torch.ones(n_neurons, device=device)
proportions = torch.zeros((n_neurons, n_classes), device=device)
rates = torch.zeros((n_neurons, n_classes), device=device)
# Sequence of accuracy estimates.
accuracy = {"all": [], "proportion": []}
# Voltage recording for excitatory and inhibitory layers.
som_voltage_monitor = Monitor(network.layers["Y"], ["v"], time=int(time / dt))
network.add_monitor(som_voltage_monitor, name="som_voltage")
# Set up monitors for spikes and voltages
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer],
state_vars=["s"],
time=int(time / dt))
network.add_monitor(spikes[layer], name="%s_spikes" % layer)
voltages = {}
for layer in set(network.layers) - {"X"}:
voltages[layer] = Monitor(network.layers[layer],
state_vars=["v"],
time=int(time / dt))
network.add_monitor(voltages[layer], name="%s_voltages" % layer)
# Sequence of accuracy estimates.
curves = {'all': [], 'proportion': [], 'ngram': []}
predictions = {
'all': torch.Tensor().long(),
'proportion': torch.Tensor().long(),
'ngram': torch.Tensor().long()
}
if train:
best_accuracy = 0
spikes = {}
for layer in set(network.layers) - {'X'}:
spikes[layer] = Monitor(network.layers[layer], state_vars=['s'], time=time)
network.add_monitor(spikes[layer], name='%s_spikes' % layer)
# Train the network.
if train:
print('\nBegin training.\n')
else:
print('\nBegin test.\n')
inpt_axes = None
inpt_ims = None
spike_ims = None
spike_axes = None
weights_im = None
assigns_im = None
perf_ax = None