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
