def build_association(self):
tau_rc = self.assoc_params.tau_rc
tau_ref = self.assoc_params.tau_ref
radius = self.assoc_params.radius
eval_points = self.assoc_params.eval_points
intercepts = self.assoc_params.intercepts
# Add a nengo.Node which calls out to a GPU library for
# simulating the associative memory
self.assoc_memory = \
AssociativeMemoryGPU(self.gpus, self.index_vectors,
self.stored_vectors,
threshold=self.threshold,
neurons_per_item=self.neurons_per_item,
tau_ref=tau_ref, tau_rc=tau_rc,
eval_points=eval_points,
intercepts=intercepts,
radius=radius, do_print=False,
identical=self.identical,
probe_keys=self.probe_keys,
seed=self.seed,
num_steps=self.timesteps)
self.assoc_output = np.zeros((1, self.dimension))
def build_association(self):
tau_rc = self.assoc_params.tau_rc
tau_ref = self.assoc_params.tau_ref
radius = self.assoc_params.radius
eval_points = self.assoc_params.eval_points
intercepts = self.assoc_params.intercepts
# Add a nengo.Node which calls out to a GPU library for
# simulating the associative memory
self.assoc_memory = \
AssociativeMemoryGPU(self.gpus, self.index_vectors,
self.stored_vectors,
threshold=self.threshold,
neurons_per_item=self.neurons_per_item,
tau_ref=tau_ref, tau_rc=tau_rc,
eval_points=eval_points,
intercepts=intercepts,
radius=radius, do_print=False,
identical=self.identical,
probe_keys=self.probe_keys,
seed=self.seed,
num_steps=self.timesteps)
self.assoc_output = np.zeros((1, self.dimension))
class FastNeuralExtractor(NeuralExtractor):
_type = "Neural"
def __init__(self, *args, **kwargs):
"""
A GPU neural associative memory with a unique simulation strategy.
Instead of making one big model with all the necessary components in
it, we make multiple models, and simulate each completely before
simulating the next one. The main benefit of this is that the GPU won't
have to pause every time step to load data on and off. This is made
possible by the fact that the network is completely feedforward, and so
earlier components can be simulated without requiring input from later
components.
index_vectors and stored_vectors are both dictionaries mapping from
tuples of the form (POS, number), indicating a synset, to numpy
ndarrays containing the assigned vector
"""
super(FastNeuralExtractor, self).__init__(*args, **kwargs)
def setup_simulator(self):
self.unbind_model = nengo.Network(label="Unbind", seed=self.seed)
self.build_unbind(self.unbind_model)
self.build_association()
self.output_model = nengo.Network(label="Output", seed=self.seed)
self.build_output(self.output_model)
print "Building simulators"
self.unbind_simulator = self.build_simulator(self.unbind_model)
self.output_simulator = self.build_simulator(self.output_model)
self.simulator = self.output_simulator
def build_association(self):
tau_rc = self.assoc_params.tau_rc
tau_ref = self.assoc_params.tau_ref
radius = self.assoc_params.radius
eval_points = self.assoc_params.eval_points
intercepts = self.assoc_params.intercepts
# Add a nengo.Node which calls out to a GPU library for
# simulating the associative memory
self.assoc_memory = \
AssociativeMemoryGPU(self.gpus, self.index_vectors,
self.stored_vectors,
threshold=self.threshold,
neurons_per_item=self.neurons_per_item,
tau_ref=tau_ref, tau_rc=tau_rc,
eval_points=eval_points,
intercepts=intercepts,
radius=radius, do_print=False,
identical=self.identical,
probe_keys=self.probe_keys,
seed=self.seed,
num_steps=self.timesteps)
self.assoc_output = np.zeros((1, self.dimension))
def build_output(self, model):
assoc_probes = OrderedDict()
threshold_probes = OrderedDict()
assoc_spike_probes = OrderedDict()
synapse = self.assoc_params.synapse
with model:
input = nengo.Node(output=self.assoc_output_func)
output = EnsembleArray(n_neurons=self.neurons_per_dim,
n_ensembles=self.dimension,
label="output",
radius=self.radius)
nengo.Connection(input, output.input, synapse=synapse)
self.output_probe = nengo.Probe(output.output,
'output',
synapse=0.02)
for k in self.probe_keys:
n = nengo.Node(output=self.assoc_memory.probe_func(k))
probe = nengo.Probe(n, synapse=synapse)
threshold_probes[k] = probe
node = nengo.Node(output=self.assoc_memory.spike_func(k))
assoc_spike_probes[k] = nengo.Probe(node, synapse=None)
self.assoc_probes = assoc_probes
self.threshold_probes = threshold_probes
self.assoc_spike_probes = assoc_spike_probes
def extract(self, item, query, *args, **kwargs):
self.print_instance_difficulty(item, query)
then = datetime.datetime.now()
self.A_input_vector = item
self.B_input_vector = query
print "Simulating unbind model"
self.unbind_simulator.run(self.timesteps * self.dt)
assoc_input = self.unbind_simulator.data[self.D_probe]
print "Simulating associative memory"
self.assoc_output = self.assoc_memory.multi_step(assoc_input)
self.output_index = 0
print "Simulating output"
self.output_simulator.run(self.timesteps * self.dt)
now = datetime.datetime.now()
self.write_to_runtime_file(now - then, "unbind")
# for plotting
self.data = {k: v for (k, v) in self.output_simulator.data.iteritems()}
self.data[self.D_probe] = self.unbind_simulator.data[self.D_probe]
self.data[self.input_probe] = \
self.unbind_simulator.data[self.input_probe]
if self.plot:
self.plot_simulation()
vector = self.data[self.output_probe][-1, :]
return [vector]
def assoc_output_func(self, t):
index = int(t / self.dt)
return self.assoc_output[index]
def reset_nodes(self):
pass
def write_to_runtime_file(self, delta, label=''):
to_print = [
self.dimension, self.num_items, self.neurons_per_item,
self.neurons_per_dim, self.timesteps, "OCL: " + str(self.ocl),
"GPUS: " + str(self.gpus), "fast", delta
]
print >> self.runtimes_file, label, \
": " ",".join([str(tp) for tp in to_print])
def print_config(self, output_file):
super(FastNeuralExtractor, self).print_config(output_file)
output_file.write("Fast neural extractor config:\n")
def build_association(self, model):
tau_rc = self.assoc_params.tau_rc
tau_ref = self.assoc_params.tau_ref
synapse = self.assoc_params.synapse
radius = self.assoc_params.radius
eval_points = self.assoc_params.eval_points
intercepts = self.assoc_params.intercepts
neurons_per_item = self.neurons_per_item
threshold = self.threshold
assoc_probes = OrderedDict()
threshold_probes = OrderedDict()
assoc_spike_probes = OrderedDict()
with model:
if self.gpus:
if not self.identical:
raise NotImplementedError(
"Currently, can only use gpu if --identical "
"is also specified")
# Add a nengo.Node which calls out to a GPU library for
# simulating the associative memory
self.assoc_mem = \
AssociativeMemoryGPU(self.gpus, self.index_vectors,
self.stored_vectors,
threshold=threshold,
neurons_per_item=neurons_per_item,
tau_ref=tau_ref, tau_rc=tau_rc,
eval_points=eval_points,
intercepts=intercepts,
radius=radius, do_print=False,
identical=self.identical,
probe_keys=self.probe_keys,
seed=self.seed,
collect_spikes=self.collect_spikes)
def gpu_function(t, input_vector):
output_vector = self.assoc_mem.step(input_vector)
return output_vector
assoc = nengo.Node(output=gpu_function,
size_in=self.dimension,
size_out=self.dimension)
nengo.Connection(self.D_output, assoc, synapse=synapse)
nengo.Connection(assoc, self.output.input, synapse=synapse)
for k in self.probe_keys:
node = nengo.Node(output=self.assoc_mem.probe_func(k))
probe = nengo.Probe(node, synapse=synapse)
threshold_probes[k] = probe
node = nengo.Node(output=self.assoc_mem.spike_func(k))
assoc_spike_probes[k] = nengo.Probe(node, synapse=None)
else:
self.assoc_mem = AssociativeMemory(
input_vocab=np.array(self.index_vectors.values()),
output_vocab=np.array(self.stored_vectors.values()),
threshold=self.threshold,
neuron_type=nengo.LIF(tau_rc=tau_rc, tau_ref=tau_ref),
n_neurons_per_ensemble=neurons_per_item)
nengo.Connection(self.D_output,
self.assoc_mem.input,
synapse=synapse)
nengo.Connection(self.assoc_mem.output,
self.output.input,
synapse=synapse)
assoc_ensembles = (
self.assoc_mem.thresholded_ens_array.ea_ensembles)
for ens, k in zip(assoc_ensembles, self.index_vectors):
if k in self.probe_keys:
assoc_probes[k] = nengo.Probe(ens,
'decoded_output',
synapse=synapse)
assoc_spike_probes[k] = nengo.Probe(ens.neurons,
'spikes',
synapse=None)
self.assoc_probes = assoc_probes
self.threshold_probes = threshold_probes
self.assoc_spike_probes = assoc_spike_probes
def build_association(self, model):
tau_rc = self.assoc_params.tau_rc
tau_ref = self.assoc_params.tau_ref
synapse = self.assoc_params.synapse
radius = self.assoc_params.radius
eval_points = self.assoc_params.eval_points
intercepts = self.assoc_params.intercepts
neurons_per_item = self.neurons_per_item
threshold = self.threshold
assoc_probes = OrderedDict()
threshold_probes = OrderedDict()
assoc_spike_probes = OrderedDict()
with model:
if self.gpus:
if not self.identical:
raise NotImplementedError(
"Currently, can only use gpu if --identical "
"is also specified")
# Add a nengo.Node which calls out to a GPU library for
# simulating the associative memory
self.assoc_mem = \
AssociativeMemoryGPU(self.gpus, self.index_vectors,
self.stored_vectors,
threshold=threshold,
neurons_per_item=neurons_per_item,
tau_ref=tau_ref, tau_rc=tau_rc,
eval_points=eval_points,
intercepts=intercepts,
radius=radius, do_print=False,
identical=self.identical,
probe_keys=self.probe_keys,
seed=self.seed,
collect_spikes=self.collect_spikes)
def gpu_function(t, input_vector):
output_vector = self.assoc_mem.step(input_vector)
return output_vector
assoc = nengo.Node(output=gpu_function,
size_in=self.dimension,
size_out=self.dimension)
nengo.Connection(self.D_output, assoc, synapse=synapse)
nengo.Connection(assoc, self.output.input, synapse=synapse)
for k in self.probe_keys:
node = nengo.Node(output=self.assoc_mem.probe_func(k))
probe = nengo.Probe(node, synapse=synapse)
threshold_probes[k] = probe
node = nengo.Node(output=self.assoc_mem.spike_func(k))
assoc_spike_probes[k] = nengo.Probe(node, synapse=None)
else:
#print(self.index_vectors)
#print(self.stored_vectors)
'''
index_vocab = Vocabulary(128)
stored_vocab = Vocabulary(128)
#index_vocab = Vocabulary(len(self.index_vectors.values()))
#stored_vocab = Vocabulary(len(self.stored_vectors.values()))
import string
chars = list(string.ascii_uppercase)
import random
for idx, vector in enumerate(self.index_vectors.values()):
generated_key = ''.join([chars[i] for i in random.sample(range(len(chars)), len(chars)-1)])
print('index_len: {}'.format(np.shape(vector)))
print(vector)
index_vocab.add(generated_key, vector)
for idx, vector in enumerate(self.stored_vectors.values()):
generated_key = ''.join([chars[i] for i in random.sample(range(len(chars)), len(chars)-1)])
print('stored_len: {}'.format(np.shape(vector)))
stored_vocab.add(generated_key, vector)
'''
self.assoc_mem = AssociativeMemory(
input_vectors=self.index_vectors.values(),
output_vectors=self.stored_vectors.values(),
threshold=self.threshold,
n_neurons=neurons_per_item)
#neuron_type=nengo.LIF(tau_rc=tau_rc, tau_ref=tau_ref),
#n_neurons_per_ensemble=neurons_per_item)
self.assoc_mem.add_threshold_to_outputs(neurons_per_item)
#self.assoc_mem = AssociativeMemory(
# input_vocab=index_vocab,
# output_vocab=stored_vocab,
# threshold=self.threshold,
# )
#neuron_type=nengo.LIF(tau_rc=tau_rc, tau_ref=tau_ref),
#n_neurons_per_ensemble=neurons_per_item)
nengo.Connection(self.D_output,
self.assoc_mem.input,
synapse=synapse)
nengo.Connection(self.assoc_mem.output,
self.output.input,
synapse=synapse)
assoc_ensembles = (self.assoc_mem.thresh_ens.ensembles)
#self.assoc_mem.thresholded_ens_array.ea_ensembles)
for ens, k in zip(assoc_ensembles, self.index_vectors):
if k in self.probe_keys:
assoc_probes[k] = nengo.Probe(ens,
'decoded_output',
synapse=synapse)
assoc_spike_probes[k] = nengo.Probe(ens.neurons,
'spikes',
synapse=None)
self.assoc_probes = assoc_probes
self.threshold_probes = threshold_probes
self.assoc_spike_probes = assoc_spike_probes
class FastNeuralExtractor(NeuralExtractor):
_type = "Neural"
def __init__(self, *args, **kwargs):
"""
A GPU neural associative memory with a unique simulation strategy.
Instead of making one big model with all the necessary components in
it, we make multiple models, and simulate each completely before
simulating the next one. The main benefit of this is that the GPU won't
have to pause every time step to load data on and off. This is made
possible by the fact that the network is completely feedforward, and so
earlier components can be simulated without requiring input from later
components.
index_vectors and stored_vectors are both dictionaries mapping from
tuples of the form (POS, number), indicating a synset, to numpy
ndarrays containing the assigned vector
"""
super(FastNeuralExtractor, self).__init__(*args, **kwargs)
def setup_simulator(self):
self.unbind_model = nengo.Network(label="Unbind", seed=self.seed)
self.build_unbind(self.unbind_model)
self.build_association()
self.output_model = nengo.Network(label="Output", seed=self.seed)
self.build_output(self.output_model)
print "Building simulators"
self.unbind_simulator = self.build_simulator(self.unbind_model)
self.output_simulator = self.build_simulator(self.output_model)
self.simulator = self.output_simulator
def build_association(self):
tau_rc = self.assoc_params.tau_rc
tau_ref = self.assoc_params.tau_ref
radius = self.assoc_params.radius
eval_points = self.assoc_params.eval_points
intercepts = self.assoc_params.intercepts
# Add a nengo.Node which calls out to a GPU library for
# simulating the associative memory
self.assoc_memory = \
AssociativeMemoryGPU(self.gpus, self.index_vectors,
self.stored_vectors,
threshold=self.threshold,
neurons_per_item=self.neurons_per_item,
tau_ref=tau_ref, tau_rc=tau_rc,
eval_points=eval_points,
intercepts=intercepts,
radius=radius, do_print=False,
identical=self.identical,
probe_keys=self.probe_keys,
seed=self.seed,
num_steps=self.timesteps)
self.assoc_output = np.zeros((1, self.dimension))
def build_output(self, model):
assoc_probes = OrderedDict()
threshold_probes = OrderedDict()
assoc_spike_probes = OrderedDict()
synapse = self.assoc_params.synapse
with model:
input = nengo.Node(output=self.assoc_output_func)
output = EnsembleArray(n_neurons=self.neurons_per_dim,
n_ensembles=self.dimension,
label="output",
radius=self.radius)
nengo.Connection(input, output.input, synapse=synapse)
self.output_probe = nengo.Probe(output.output, 'output',
synapse=0.02)
for k in self.probe_keys:
n = nengo.Node(output=self.assoc_memory.probe_func(k))
probe = nengo.Probe(n, synapse=synapse)
threshold_probes[k] = probe
node = nengo.Node(output=self.assoc_memory.spike_func(k))
assoc_spike_probes[k] = nengo.Probe(node, synapse=None)
self.assoc_probes = assoc_probes
self.threshold_probes = threshold_probes
self.assoc_spike_probes = assoc_spike_probes
def extract(self, item, query, *args, **kwargs):
self.print_instance_difficulty(item, query)
then = datetime.datetime.now()
self.A_input_vector = item
self.B_input_vector = query
print "Simulating unbind model"
self.unbind_simulator.run(self.timesteps * self.dt)
assoc_input = self.unbind_simulator.data[self.D_probe]
print "Simulating associative memory"
self.assoc_output = self.assoc_memory.multi_step(assoc_input)
self.output_index = 0
print "Simulating output"
self.output_simulator.run(self.timesteps * self.dt)
now = datetime.datetime.now()
self.write_to_runtime_file(now - then, "unbind")
# for plotting
self.data = {k: v for (k, v) in self.output_simulator.data.iteritems()}
self.data[self.D_probe] = self.unbind_simulator.data[self.D_probe]
self.data[self.input_probe] = \
self.unbind_simulator.data[self.input_probe]
if self.plot:
self.plot_simulation()
vector = self.data[self.output_probe][-1, :]
return [vector]
def assoc_output_func(self, t):
index = int(t / self.dt)
return self.assoc_output[index]
def reset_nodes(self):
pass
def write_to_runtime_file(self, delta, label=''):
to_print = [self.dimension, self.num_items,
self.neurons_per_item, self.neurons_per_dim,
self.timesteps, "OCL: "+str(self.ocl),
"GPUS: "+str(self.gpus), "fast", delta]
print >> self.runtimes_file, label, \
": " ",".join([str(tp) for tp in to_print])
def print_config(self, output_file):
super(FastNeuralExtractor, self).print_config(output_file)
output_file.write("Fast neural extractor config:\n")
