def BindsNET_cpu(n_neurons, time):
t0 = t()
torch.set_default_tensor_type("torch.FloatTensor")
t1 = t()
network = Network()
network.add_layer(Input(n=n_neurons), name="X")
network.add_layer(LIFNodes(n=n_neurons), name="Y")
network.add_connection(
Connection(source=network.layers["X"], target=network.layers["Y"]),
source="X",
target="Y",
)
data = {"X": poisson(datum=torch.rand(n_neurons), time=time)}
network.run(inputs=data, time=time)
return t() - t0, t() - t1
# Build network.
network = Network(dt=dt)
# Layers of neurons.
inpt = Input(shape=(80, 80), traces=True) # Input layer
exc = LIFNodes(n=n_neurons, refrac=0, traces=True) # Excitatory layer
readout = LIFNodes(n=16, refrac=0, traces=True) # Readout layer
layers = {"X": inpt, "E": exc, "R": readout}
# Connections between layers.
# Input -> excitatory.
w = 0.01 * torch.rand(layers["X"].n, layers["E"].n)
input_exc_conn = Connection(
source=layers["X"],
target=layers["E"],
w=0.01 * torch.rand(layers["X"].n, layers["E"].n),
wmax=0.02,
norm=0.01 * layers["X"].n,
)
# Excitatory -> readout.
exc_readout_conn = Connection(
source=layers["E"],
target=layers["R"],
w=0.01 * torch.rand(layers["E"].n, layers["R"].n),
update_rule=Hebbian,
nu=[1e-2, 1e-2],
norm=0.5 * layers["E"].n,
)
# Spike recordings for all layers.
nu=[1e-4, 1e-2],
wmax=1.0,
)
w = torch.zeros(n_filters, conv_size, conv_size, n_filters, conv_size,
conv_size)
for fltr1 in range(n_filters):
for fltr2 in range(n_filters):
if fltr1 != fltr2:
for i in range(conv_size):
for j in range(conv_size):
w[fltr1, i, j, fltr2, i, j] = -100.0
w = w.view(n_filters * conv_size * conv_size,
n_filters * conv_size * conv_size)
recurrent_conn = Connection(conv_layer, conv_layer, w=w)
network.add_layer(input_layer, name="X")
network.add_layer(conv_layer, name="Y")
network.add_connection(conv_conn, source="X", target="Y")
network.add_connection(recurrent_conn, source="Y", target="Y")
# Voltage recording for excitatory and inhibitory layers.
voltage_monitor = Monitor(network.layers["Y"], ["v"], time=time)
network.add_monitor(voltage_monitor, name="output_voltage")
if gpu:
network.to("cuda")
# Load MNIST data.
train_dataset = MNIST(
from bindsnet_qa.encoding import bernoulli
from bindsnet_qa.network.topology import Connection
from bindsnet_qa.environment import GymEnvironment
from bindsnet_qa.network.nodes import Input, LIFNodes
from bindsnet_qa.pipeline.action import select_softmax
# Build network.
network = Network(dt=1.0)
# Layers of neurons.
inpt = Input(n=80 * 80, shape=[80, 80], traces=True)
middle = LIFNodes(n=100, traces=True)
out = LIFNodes(n=4, refrac=0, traces=True)
# Connections between layers.
inpt_middle = Connection(source=inpt, target=middle, wmin=0, wmax=1e-1)
middle_out = Connection(source=middle, target=out, wmin=0, wmax=1)
# Add all layers and connections to the network.
network.add_layer(inpt, name="Input Layer")
network.add_layer(middle, name="Hidden Layer")
network.add_layer(out, name="Output Layer")
network.add_connection(inpt_middle,
source="Input Layer",
target="Hidden Layer")
network.add_connection(middle_out,
source="Hidden Layer",
target="Output Layer")
# Load the Breakout environment.
environment = GymEnvironment("BreakoutDeterministic-v4")
# Sets up Gpu use
if gpu and torch.cuda.is_available():
torch.cuda.set_device(device_id)
# torch.set_default_tensor_type('torch.cuda.FloatTensor')
else:
torch.manual_seed(seed)
network = Network(dt=dt)
inpt = Input(784, shape=(1, 28, 28))
network.add_layer(inpt, name="I")
output = LIFNodes(n_neurons,
thresh=-52 + np.random.randn(n_neurons).astype(float))
network.add_layer(output, name="O")
C1 = Connection(source=inpt,
target=output,
w=0.5 * torch.randn(inpt.n, output.n))
C2 = Connection(source=output,
target=output,
w=0.5 * torch.randn(output.n, output.n))
network.add_connection(C1, source="I", target="O")
network.add_connection(C2, source="O", target="O")
spikes = {}
for l in network.layers:
spikes[l] = Monitor(network.layers[l], ["s"], time=time)
network.add_monitor(spikes[l], name="%s_spikes" % l)
voltages = {"O": Monitor(network.layers["O"], ["v"], time=time)}
network.add_monitor(voltages["O"], name="O_voltages")
from bindsnet_qa.encoding import bernoulli
from bindsnet_qa.network.topology import Connection
from bindsnet_qa.environment import GymEnvironment
from bindsnet_qa.network.nodes import Input, LIFNodes
from bindsnet_qa.pipeline.action import select_softmax
# Build network.
network = Network(dt=1.0)
# Layers of neurons.
inpt = Input(n=80 * 80, shape=[80, 80], traces=True)
middle = LIFNodes(n=100, traces=True)
out = LIFNodes(n=4, refrac=0, traces=True)
# Connections between layers.
inpt_middle = Connection(source=inpt, target=middle, wmin=0, wmax=1e-1)
middle_out = Connection(
source=middle,
target=out,
wmin=0,
wmax=1,
update_rule=MSTDP,
nu=1e-1,
norm=0.5 * middle.n,
)
# Add all layers and connections to the network.
network.add_layer(inpt, name="Input Layer")
network.add_layer(middle, name="Hidden Layer")
network.add_layer(out, name="Output Layer")
network.add_connection(inpt_middle,