inh=17.5,
nu=[0, 1e-2],
norm=78.4)
# Specify dataset wrapper environment.
environment = DatasetEnvironment(dataset=CIFAR10(path='../../data/CIFAR10'),
train=True)
# Build pipeline from components.
pipeline = Pipeline(network=network,
environment=environment,
encoding=poisson,
time=50,
plot_interval=1)
# Train the network.
labels = environment.labels
for i in range(60000):
# Choose an output neuron to clamp to spiking behavior.
c = choice(10, size=1, replace=False)
c = 10 * labels[i].long() + Tensor(c).long()
clamp = torch.zeros(pipeline.time, network.n_neurons, dtype=torch.uint8)
clamp[:, c] = 1
clamp_v = torch.zeros(pipeline.time, network.n_neurons, dtype=torch.float)
clamp_v[:, c] = network.layers['Ae'].thresh + network.layers['Ae'].theta[
c] + 10
# Run a step of the pipeline with clamped neuron.
pipeline.step(clamp={'Ae': clamp}, clamp_v={'Ae': clamp_v})
network.reset_()
if layer in voltages:
network.add_monitor(voltages[layer], name='%s_voltages' % layer)
# Load SpaceInvaders environment.
environment = GymEnvironment('Asteroids-v0')
environment.reset()
pipeline = Pipeline(network, environment, encoding=bernoulli, time=1, history=5, delta=10, plot_interval=plot_interval,
print_interval=print_interval, render_interval=render_interval, action_function=select_multinomial,
output='R')
total = 0
rewards = []
avg_rewards = []
lengths = []
avg_lengths = []
i = 0
try:
while i < n:
pipeline.step()
if pipeline.done:
pipeline.reset_()
i += 1
except KeyboardInterrupt:
environment.close()
n_neurons=100,
dt=1.0,
exc=22.5,
inh=17.5,
nu_pre=0,
nu_post=1e-2,
norm=78.4)
# Specify dataset wrapper environment.
environment = DatasetEnvironment(dataset=CIFAR10(path='../../data/CIFAR10',
download=True),
train=True)
# Build pipeline from components.
pipeline = Pipeline(network=network,
environment=environment,
encoding=poisson,
time=50,
plot_interval=1)
# Train the network.
labels = environment.labels
for i in range(50000):
# Choose an output neuron to clamp to spiking behavior.
c = choice(10, size=1, replace=False)
clamp = {'Ae': 10 * labels[i].long() + Tensor(c).long()}
# Run a step of the pipeline with clamped neuron.
pipeline.step(clamp=clamp)
network._reset()
nu=[0, 1e-2],
norm=78.4,
)
# Specify dataset wrapper environment.
environment = DatasetEnvironment(dataset=CIFAR10(path="../../data/CIFAR10"),
train=True)
# Build pipeline from components.
pipeline = Pipeline(network=network,
environment=environment,
encoding=poisson,
time=50,
plot_interval=1)
# Train the network.
labels = environment.labels
for i in range(60000):
# Choose an output neuron to clamp to spiking behavior.
c = choice(10, size=1, replace=False)
c = 10 * labels[i].long() + Tensor(c).long()
clamp = torch.zeros(pipeline.time, network.n_neurons, dtype=torch.uint8)
clamp[:, c] = 1
clamp_v = torch.zeros(pipeline.time, network.n_neurons, dtype=torch.float)
clamp_v[:, c] = network.layers["Ae"].thresh + network.layers["Ae"].theta[
c] + 10
# Run a step of the pipeline with clamped neuron.
pipeline.step(clamp={"Ae": clamp}, clamp_v={"Ae": clamp_v})
network.reset_()
def main(seed=0,
n_neurons=100,
n_train=60000,
n_test=10000,
inhib=100,
lr=0.01,
lr_decay=1,
time=350,
dt=1,
theta_plus=0.05,
theta_decay=1e-7,
progress_interval=10,
update_interval=250,
plot=False,
train=True,
gpu=False):
assert n_train % update_interval == 0 and n_test % update_interval == 0, \
'No. examples must be divisible by update_interval'
params = [
seed, n_neurons, n_train, inhib, lr_decay, time, dt, theta_plus,
theta_decay, progress_interval, update_interval
]
model_name = '_'.join([str(x) for x in params])
np.random.seed(seed)
if gpu:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.manual_seed_all(seed)
else:
torch.manual_seed(seed)
n_examples = n_train if train else n_test
n_classes = 10
# Build network.
if train:
network = Network(dt=dt)
input_layer = RealInput(n=784, traces=True, trace_tc=5e-2)
network.add_layer(input_layer, name='X')
output_layer = DiehlAndCookNodes(n=n_classes,
rest=0,
reset=1,
thresh=1,
decay=1e-2,
theta_plus=theta_plus,
theta_decay=theta_decay,
traces=True,
trace_tc=5e-2)
network.add_layer(output_layer, name='Y')
w = torch.rand(784, n_classes)
input_connection = Connection(source=input_layer,
target=output_layer,
w=w,
update_rule=MSTDPET,
nu=lr,
wmin=0,
wmax=1,
norm=78.4,
tc_e_trace=0.1)
network.add_connection(input_connection, source='X', target='Y')
else:
network = load_network(os.path.join(params_path, model_name + '.pt'))
network.connections['X', 'Y'].update_rule = NoOp(
connection=network.connections['X', 'Y'],
nu=network.connections['X', 'Y'].nu)
network.layers['Y'].theta_decay = 0
network.layers['Y'].theta_plus = 0
# Load MNIST data.
environment = MNISTEnvironment(dataset=MNIST(path=data_path,
download=True),
train=train,
time=time)
# Create pipeline.
pipeline = Pipeline(network=network,
environment=environment,
encoding=repeat,
action_function=select_spiked,
output='Y',
reward_delay=None)
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer],
state_vars=('s', ),
time=time)
network.add_monitor(spikes[layer], name='%s_spikes' % layer)
network.add_monitor(
Monitor(network.connections['X', 'Y'].update_rule,
state_vars=('e_trace', ),
time=time), 'X_Y_e_trace')
# Train the network.
if train:
print('\nBegin training.\n')
else:
print('\nBegin test.\n')
spike_ims = None
spike_axes = None
weights_im = None
elig_axes = None
elig_ims = None
start = t()
for i in range(n_examples):
if i % progress_interval == 0:
print(f'Progress: {i} / {n_examples} ({t() - start:.4f} seconds)')
start = t()
if i > 0 and train:
network.connections['X', 'Y'].update_rule.nu[1] *= lr_decay
# Run the network on the input.
for j in range(time):
pipeline.step(a_plus=1, a_minus=0)
if plot:
_spikes = {layer: spikes[layer].get('s') for layer in spikes}
w = network.connections['X', 'Y'].w
square_weights = get_square_weights(w.view(784, n_classes), 4, 28)
spike_ims, spike_axes = plot_spikes(_spikes,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_weights(square_weights, im=weights_im)
elig_ims, elig_axes = plot_voltages(
{
'Y':
network.monitors['X_Y_e_trace'].get('e_trace').view(
-1, time)[1500:2000]
},
plot_type='line',
ims=elig_ims,
axes=elig_axes)
plt.pause(1e-8)
pipeline.reset_() # Reset state variables.
network.connections['X', 'Y'].update_rule.e_trace = torch.zeros(
784, n_classes)
print(f'Progress: {n_examples} / {n_examples} ({t() - start:.4f} seconds)')
if train:
print('\nTraining complete.\n')
else:
print('\nTest complete.\n')
