print "Denominator: %2.10e nats" % np.log(den)
print "Resulting P: %2.10e nats" % (num - np.log(den))
end_time = time.clock()
fn = os.path.join(cfg.basename, "info-0-%s.pickle" % cfg.idx)
if os.path.exists(fn):
with open(fn, "r") as f:
stats = cPickle.load(f)
else:
stats = {}
stats["weights2"] = np.sum(W ** 2)
stats["nats"] = num - np.log(den)
stats["nats_num"] = num
stats["nats_den"] = np.log(den)
stats["dev"] = cfg.device
stats["basename"] = cfg.basename
stats["bindex"] = cfg.idx
stats["cpf_host"] = os.uname()[1]
stats["cpf_duration"] = end_time - start_time
stats["cpf_time"] = strftime("%Y-%m-%d %H:%M:%S")
with open(fn, "wb") as f:
cPickle.dump(stats, f)
cp.exitCUDA()
if __name__ == "__main__":
main()
def tearDownModule():
cp.exitCUDA()
pymlp = MLP(cfg, weights, biases)
pymlp.preEpochHook = lambda mlp, epoch: epoch % 10 == 0 and mlp.runMLP(
mbp_test, cfg.test_batchsize, epoch)
try:
pymlp.train(mbp, cfg.finetune_epochs, cfg.finetune_batch_size,
cfg.finetune_rprop)
except KeyboardInterrupt:
pass
map(lambda x: x.alloc(), rbmstack.layers)
map(lambda x: x.allocPChain(), rbmstack.layers)
rbmstack.saveAllLayers("-finetune")
pymlp.saveLastLayer()
if cfg.headless:
cp.exitCUDA()
sys.exit(0)
PLT_NUM = 1
import matplotlib.pyplot as plt
#### calculate maps_bottom into py. yeah it's a dirty hack, i know
px = cfg.px
py = cfg.py
if "projection_results" in rbmstack.__dict__:
for layernum in rbmstack.projection_results.keys():
filters = rbmstack.projection_results[layernum].T
print "Saving projections from layer %d (%d x %d)" % (
layernum, filters.shape[0], filters.shape[1])
img_name = make_img_name("filter_layer%d.png" % (layernum))
visualize_rows(PLT_NUM,
def tearDownModule():
cp.exitCUDA()
