def loadExperiment(self, experiment): suite = Suite() suite.parse_opt() suite.parse_cfg() experiment_dir = experiment.split('/')[1] params = suite.items_to_params(suite.cfgparser.items(experiment_dir)) self.params = params predictions = suite.get_history(experiment, 0, 'predictions') truth = suite.get_history(experiment, 0, 'truth') self.iteration = suite.get_history(experiment, 0, 'iteration') self.train = suite.get_history(experiment, 0, 'train') self.truth = np.array(truth, dtype=np.float) if params['output_encoding'] == 'likelihood': from nupic.encoders.scalar import ScalarEncoder as NupicScalarEncoder self.outputEncoder = NupicScalarEncoder(w=1, minval=0, maxval=40000, n=22, forced=True) predictions_np = np.zeros((len(predictions), self.outputEncoder.n)) for i in xrange(len(predictions)): if predictions[i] is not None: predictions_np[i, :] = np.array(predictions[i]) self.predictions = predictions_np else: self.predictions = np.array(predictions, dtype=np.float)
PADDING = 0 KERNEL_SIZE = 5 def computeMaxPool(input_width): """ Compute CNN max pool width """ wout = math.floor((input_width + 2 * PADDING - KERNEL_SIZE) / STRIDE + 1) return int(math.floor(wout / 2.0)) if __name__ == '__main__': suite = PyExperimentSuite() suite.parse_opt() suite.parse_cfg() experiments = suite.options.experiments or suite.cfgparser.sections() paramsTable = [[ "Network", "L1 F", "L1 Sparsity", "L2 F", "L2 Sparsity", "L3 N", "L3 Sparsity", "Wt Sparsity" ]] for name in experiments: # Iterate over experiments, skipping over errors. try: exps = suite.get_exps(suite.get_exp(name)[0]) except: print("Couldn't parse experiment:", name) continue