def fit(self, X, y, sample_weight=None):
global g_seed
global g_loop
if sample_weight is None:
sample_weight = np.ones(X.shape[0], np.float32)
print("Calling fit with sample_weight None")
else:
sample_weight *= X.shape[0]
print("Calling fit with sample_weight sum", sample_weight.sum())
# sample_weight = np.ones(X.shape[0], np.float32)
# II = sample_weight > 0
# X = X[II]
# y = y[II]
# sample_weight = sample_weight[II]
# sample_weight = np.ones(X.shape[0])
w = sample_weight
# sample_weight[:10] = 0.0
# w[:1000] = 0.0
# w = sample_weight
# w0 = w / w.sum()
# print('Weight entropy:', -np.sum(w0 * np.log2(w0)))
print("Weight max:", w.max())
print("Weight min:", w.min())
# import sys; sys.exit(0)
self.classes_ = np.unique(y)
self.n_classes_ = len(self.classes_)
# Set up weighted database
create_weighted_db(X, y, sample_weight)
# steps = [(0.001, 2000, 2000)]
steps = [(0.001, 0.004, 60000), (0.0001, 0.004, 5000), (0.00001, 0.004, 5000)]
# steps = [(0.00001, 10000, 10000), (0.000001, 5000, 15000), (0.0000001, 5000, 20000)]
# steps = [(0.001, 10000, 10000)]
# steps = [(0.001, 200, 1000)]
name = os.path.join(CONF_DIR, "adaboost_{}_loop{}".format(g_rnd, g_loop))
bare_conf_fn = os.path.join(CONF_DIR, "boost_bare.prototxt")
conf_fn = os.path.join(CONF_DIR, "solver.prototxt.template")
# bare_conf_fn = 'regaug_bare.prototxt'
# conf_fn = 'regaug_solver.prototxt.template'
net, info = train_model(name, conf_fn, bare_conf_fn, steps, seed=g_seed, device_id=DEVICE_ID)
loss_fn = "info/info_{}_loop{}.h5".format(g_rnd, g_loop)
dd.io.save(loss_fn, info)
print("Saved to", loss_fn)
g_loop += 1
print("Classifier set up")
self.net_ = net
if 0:
# Just load a pre-calculated version instead
model_fn = base + '.caffemodel'
net = caffe.Classifier(bare_conf_fn, model_fn, image_dims=(32, 32))
net.set_phase_test()
net.set_mode_gpu()
net.set_device(DEVICE_ID)
all_fmj = dd.io.load('all_fmj0_eps_inf.h5')
all_te_fmj = dd.io.load('all_te_fmj0_eps_inf.h5')
else:
#warmstart_fn = base + '.solverstate'
#warmstart_fn = 'models/regression100_6916_loop0_iter_70000.solverstate'
#warmstart_fn = 'models/adaboost100_35934_loop0_iter_70000.solverstate'
warmstart_fn = None
net, info = train_model(name, solver_conf_fn, conf_fn, bare_conf_fn, steps, seed=g_seed, logfile=logfile, device_id=DEVICE_ID, warmstart=warmstart_fn)
all_fmj = net.forward_all(data=X).values()[0].squeeze(axis=(2,3))
all_te_fmj = net.forward_all(data=te_X).values()[0].squeeze(axis=(2,3))
all_fmj *= zstd
all_te_fmj *= zstd
g_seed += 1
#if loop == 0:
#dd.io.save('all_fmj0_eps_inf.h5', all_fmj)
#dd.io.save('all_te_fmj0_eps_inf.h5', all_te_fmj)
fs = (K - 1) / K * (all_fmj - ag.apply_once(np.mean, all_fmj, [1]))
te_fs = (K - 1) / K * (all_te_fmj - ag.apply_once(np.mean, all_te_fmj, [1]))
def fit(self, X, y, sample_weight=None):
global g_seed
global g_loop
if sample_weight is None:
sample_weight = np.ones(X.shape[0], np.float32)
print('Calling fit with sample_weight None')
else:
sample_weight *= X.shape[0]
print('Calling fit with sample_weight sum', sample_weight.sum())
#sample_weight = np.ones(X.shape[0], np.float32)
#II = sample_weight > 0
#X = X[II]
#y = y[II]
#sample_weight = sample_weight[II]
#sample_weight = np.ones(X.shape[0])
w = sample_weight
#sample_weight[:10] = 0.0
#w[:1000] = 0.0
#w = sample_weight
#w0 = w / w.sum()
#print('Weight entropy:', -np.sum(w0 * np.log2(w0)))
print('Weight max:', w.max())
print('Weight min:', w.min())
#import sys; sys.exit(0)
self.classes_ = np.unique(y)
self.n_classes_ = len(self.classes_)
# Set up weighted database
create_weighted_db(X, y, sample_weight)
#steps = [(0.001, 2000, 2000)]
steps = [(0.001, 0.004, 60000), (0.0001, 0.004, 5000),
(0.00001, 0.004, 5000)]
#steps = [(0.00001, 10000, 10000), (0.000001, 5000, 15000), (0.0000001, 5000, 20000)]
#steps = [(0.001, 10000, 10000)]
#steps = [(0.001, 200, 1000)]
name = os.path.join(CONF_DIR,
'adaboost_{}_loop{}'.format(g_rnd, g_loop))
bare_conf_fn = os.path.join(CONF_DIR, 'boost_bare.prototxt')
conf_fn = os.path.join(CONF_DIR, 'solver.prototxt.template')
#bare_conf_fn = 'regaug_bare.prototxt'
#conf_fn = 'regaug_solver.prototxt.template'
net, info = train_model(name,
conf_fn,
bare_conf_fn,
steps,
seed=g_seed,
device_id=DEVICE_ID)
loss_fn = 'info/info_{}_loop{}.h5'.format(g_rnd, g_loop)
dd.io.save(loss_fn, info)
print('Saved to', loss_fn)
g_loop += 1
print('Classifier set up')
self.net_ = net
if 0:
# Just load a pre-calculated version instead
model_fn = base + '.caffemodel'
net = caffe.Classifier(bare_conf_fn, model_fn, image_dims=(32, 32))
net.set_phase_test()
net.set_mode_gpu()
net.set_device(DEVICE_ID)
all_fmj = dd.io.load('all_fmj0_eps_inf.h5')
all_te_fmj = dd.io.load('all_te_fmj0_eps_inf.h5')
else:
#warmstart_fn = base + '.solverstate'
#warmstart_fn = 'models/regression100_6916_loop0_iter_70000.solverstate'
#warmstart_fn = 'models/adaboost100_35934_loop0_iter_70000.solverstate'
warmstart_fn = None
net, info = train_model(name, solver_conf_fn, conf_fn, bare_conf_fn, steps, seed=g_seed, logfile=logfile, device_id=DEVICE_ID, warmstart=warmstart_fn)
all_fmj = net.forward_all(data=X).values()[0].squeeze(axis=(2,3))
all_te_fmj = net.forward_all(data=te_X).values()[0].squeeze(axis=(2,3))
all_fmj *= zstd
all_te_fmj *= zstd
g_seed += 1
#if loop == 0:
#dd.io.save('all_fmj0_eps_inf.h5', all_fmj)
#dd.io.save('all_te_fmj0_eps_inf.h5', all_te_fmj)
fs = (K - 1) / K * (all_fmj - ag.apply_once(np.mean, all_fmj, [1]))
te_fs = (K - 1) / K * (all_te_fmj - ag.apply_once(np.mean, all_te_fmj, [1]))
