Adding network module (bare bones for now).

bindsnet/network/__init__.py

@@ -0,0 +1,113 @@
+import torch
+import os, sys
+import numpy as np
+import pickle as p
+
+sys.path.append(os.path.abspath(os.path.join('..', 'bindsnet', 'network')))
+
+
+def load_network(fname):
+	try:
+		return p.load(open(fname, 'rb'))
+	except FileNotFoundError:
+		print('Network not found on disk.')
+
+
+class Network:
+	'''
+	Combines neuron nodes and connections into a spiking neural network.
+	'''
+	def __init__(self, dt=1):
+		self.dt = dt
+		self.layers = {}
+		self.connections = {}
+		self.monitors = {}
+
+	def add_layer(self, layer, name):
+		self.layers[name] = layer
+
+	def add_connections(self, connections, source, target):
+		self.connections[(source, target)] = connections
+
+	def add_monitor(self, monitor, name):
+		self.monitors[name] = monitor
+
+	def train(self, inputs, targets):
+		pass
+
+	def test(self, inputs):
+		pass
+
+	def evaluate(self, targets, predictions):
+		pass
+
+	def save(self, fname):
+		p.dump(self, open(fname, 'wb'))
+
+	def get_inputs(self):
+		inpts = {}
+		for key in self.connections:
+			weights = self.connections[key].w
+
+			source = self.connections[key].source
+			target = self.connections[key].target
+
+			if not key[1] in inpts:
+				inpts[key[1]] = {}
+
+			inpts[key[1]][key[0]] = source.s.float() @ weights
+
+		return inpts
+
+	def run(self, inpts, time):
+		'''
+		Run network for a single iteration.
+		'''
+		# Record spikes from each population over the iteration.
+		spikes = {}
+		for key in self.nodes:
+			spikes[key] = torch.zeros(int(time / self.dt), self.nodes[key].n)
+
+		for monitor in self.monitors:
+			self.monitors[monitor].reset()
+
+		# Get inputs to all neuron nodes from their parent neuron nodes.
+		inpts.update(self.get_inputs())
+
+		# Simulate neuron and synapse activity for `time` timesteps.
+		for timestep in range(int(time / self.dt)):
+			# Update each layer of nodes in turn.
+			for key in self.nodes:
+				self.nodes[key].step(inpts[key], self.mode, self.dt)
+
+				# Record spikes from this population at this timestep.
+				spikes[key][timestep, :] = self.nodes[key].s
+
+			# Update synapse weights if we're in training mode.
+			if self.train:
+				for synapse in self.connections:
+					if type(self.connections[synapse]) == connections.STDPconnections:
+						self.connections[synapse].update()
+
+			# Get inputs to all neuron nodes from their parent neuron nodes.
+			inpts.update(self.get_inputs())
+
+			for monitor in self.monitors:
+				self.monitors[monitor].record()
+
+		# Normalize synapse weights if we're in training mode.
+		if self.train:
+			for synapse in self.connections:
+				if type(self.connections[synapse]) == connections.STDPconnections:
+					self.connections[synapse].normalize()
+
+		return spikes
+
+	def reset(self, attrs):
+		'''
+		Reset state variables.
+		'''
+		for layer in self.layers:
+			for attr in attrs:
+				if hasattr(self.nodes[layer], attr):
+					self.nodes[layer].reset(attr)