Exemple #1
0
    def run_param_set(self, n_neurons, d, seed, trial):
        spaopt.optimization.SubvectorRadiusOptimizer.Simulator = \
            nengo_spinnaker.Simulator

        rng = np.random.RandomState(seed)

        ctx = nengo.spa.SemanticPointer(d, rng)
        ctx.make_unitary()

        model = nengo.Network(seed=get_seed(rng))
        with model:
            step = SignalGenerator(self.duration).make_step(0, d, .001, rng)
            in_a = nengo.Node(step, size_out=d)

            a = nengo.Node(size_in=d)
            b = nengo.Node(size_in=d)
            c = nengo.Node(size_in=d)
            nengo.Connection(in_a, a)
            nengo.Connection(a, b, synapse=None)
            nengo.Connection(b, c, synapse=None)

            old_repr = nengo.networks.EnsembleArray(n_neurons, d)
            nengo.Connection(in_a, old_repr.input)

            repr_ = spaopt.UnitEA(n_neurons, d, d)
            nengo.Connection(in_a, repr_.input)

            in_probe = nengo.Probe(c, synapse=0.005)
            old_probe = nengo.Probe(old_repr.output, synapse=0.005)
            probe = nengo.Probe(repr_.output, synapse=0.005)

        nengo_spinnaker.add_spinnaker_params(model.config)
        model.config[in_a].function_of_time = True
        sim = nengo_spinnaker.Simulator(model)
        sim.run(self.duration)
        sim.close()

        return {
            't': sim.trange(),
            'default': rmse(sim.data[old_probe], sim.data[in_probe], axis=1),
            'optimized': rmse(sim.data[probe], sim.data[in_probe], axis=1)
        }
import sys

sys.path.insert(0, os.path.dirname(__file__))

import platform

import numpy as np
from psyrun import Param
from psyrun.scheduler import Sqsub

from benchmarks import get_seed, EmpiricalError


rng = np.random.RandomState(29873)
n_trials = 20
seeds = [get_seed(rng) for i in range(n_trials)]


pspace = ((Param(dimensions=64, N=200, n_eval_points=750, reg=[0.01, 0.25], m=1) +
           Param(dimensions=64, N=200, n_eval_points=2000, reg=[0.1], m=1) +
           Param(dimensions=256, N=200, n_eval_points=750, reg=[0.1], m=4)) *
          Param(r=np.linspace(0.01, 1, 11)) *
          Param(seed=seeds, trial=range(n_trials)))
min_items = 64


if platform.node().startswith('bul') or platform.node().startswith('saw'):
    workdir = '/work/jgosmann/spaopt'
    scheduler = Sqsub(workdir)
    scheduler_args = {
        'timelimit': '15m',