def test_mnist_pipeline(self):
network = DiehlAndCook2015(n_inpt=784, n_neurons=400, exc=22.5, inh=17.5, dt=1.0, norm=78.4)
environment = DatasetEnvironment(dataset=MNIST(path='../data/MNIST', download=True), train=True, intensity=0.25)
pipeline = Pipeline(network=network, environment=environment, encoding=poisson, time=350)
assert pipeline.network == network
assert pipeline.env == environment
assert pipeline.encoding == poisson
assert pipeline.time == 350
assert pipeline.history_length is None
from bindsnet.encoding import poisson
from bindsnet.pipeline import Pipeline
from bindsnet.models import DiehlAndCook2015
from bindsnet.environment import DatasetEnvironment
# Build network.
network = DiehlAndCook2015(n_input=32 * 32 * 3,
n_neurons=100,
dt=1.0,
exc=22.5,
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)
n_sqrt = int(np.ceil(np.sqrt(n_neurons)))
path = os.path.join('..', '..', 'data', 'CIFAR10')
# Build network.
network = DiehlAndCook2015(n_inpt=32 * 32 * 3,
n_neurons=n_neurons,
exc=exc,
inh=inh,
dt=dt,
nu_pre=2e-5,
nu_post=2e-3,
norm=10.0)
# Initialize data "environment".
environment = DatasetEnvironment(dataset=CIFAR10(path=path, download=True),
train=train,
time=time,
intensity=intensity)
# Specify data encoding.
encoding = poisson
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)
voltages = {}
for layer in set(network.layers) - {'X'}:
voltages[layer] = Monitor(network.layers[layer],
state_vars=['v'],
time=time)
from bindsnet.encoding import poisson from bindsnet.pipeline import Pipeline from bindsnet.models import DiehlAndCook2015 from bindsnet.environment import DatasetEnvironment # Build Diehl & Cook 2015 network. network = DiehlAndCook2015(n_inpt=784, n_neurons=400, exc=22.5, inh=17.5, dt=1.0, norm=78.4) # Specify dataset wrapper environment. environment = DatasetEnvironment(dataset=MNIST(path='../../data/MNIST'), train=True, download=True, intensity=0.25) # Build pipeline from components. pipeline = Pipeline(network=network, environment=environment, encoding=poisson, time=350, plot_interval=1) # Train the network. for i in range(60000): pipeline.step() network._reset()
from bindsnet.encoding import poisson
from bindsnet.pipeline import Pipeline
from bindsnet.models import DiehlAndCook2015
from bindsnet.environment import DatasetEnvironment
# Build Diehl & Cook 2015 network.
network = DiehlAndCook2015(n_inpt=32 * 32 * 3,
n_neurons=400,
exc=22.5,
inh=17.5,
dt=1.0,
norm=78.4)
# Specify dataset wrapper environment.
environment = DatasetEnvironment(dataset=CIFAR10(path='../../data/CIFAR10',
download=True),
train=True,
intensity=0.25)
# Build pipeline from components.
pipeline = Pipeline(network=network,
environment=environment,
encoding=poisson,
time=350,
plot_interval=1)
# Train the network.
for i in range(60000):
pipeline.step()
network._reset()