]
net = pnet.PartsNet(layers)
digits = range(10)
ims = ag.io.load_mnist('training', selection=slice(10000), return_labels=False)
#print(net.sizes(X[[0]]))
net.train(ims)
sup_ims = []
sup_labels = []
# Load supervised training data
for d in digits:
ims0 = ag.io.load_mnist('training', [d], selection=slice(100), return_labels=False)
sup_ims.append(ims0)
sup_labels.append(d * np.ones(len(ims0), dtype=np.int64))
sup_ims = np.concatenate(sup_ims, axis=0)
sup_labels = np.concatenate(sup_labels, axis=0)
net.train(sup_ims, sup_labels)
net.save(args.model)
if args.log:
net.infoplot(vz)
vz.finalize()
def train(self, X_n, Y, OriginalX = None):
X = X_n[0]
num_parts = X_n[1]
if(len(X_n) == 3):
num_orientations = X_n[2]
else:
num_orientations = 1
num_true_parts = num_parts // num_orientations
self._extra['training_comp'] = []
K = Y.max() + 1
mm_models = []
print(X.shape)
for k in xrange(K):
Xk = X[Y == k]
assert Xk.shape[-1] == 1
from pnet.cyfuncs import index_map_pooling_multi, orientation_pooling
# Rotate all the Xk samples
print('A')
XB = index_map_pooling_multi(Xk, num_parts, (1, 1), (1, 1))
print('B')
XB = XB.reshape(XB.shape[:-1] + (num_true_parts, num_orientations))
blocks = []
print('C')
for ori in xrange(0, self._n_orientations):
angle = ori / self._n_orientations * 360
# Rotate all images, apply rotational spreading, then do pooling
if 0:
print(ori, 'R{')
rots = np.asarray([rotate_patch_map(XB[i], angle) for i in xrange(XB.shape[0])])
print(ori, 'R}')
print(ori, 'P{')
yy = orientation_pooling(rots,
self._pooling_settings['shape'],
self._pooling_settings['strides'],
self._pooling_settings.get('rotation_spreading_radius', 0))
print(ori, 'P}')
from pnet.cyfuncs import rotate_index_map_pooling
if num_orientations !=1:
yy1 = rotate_index_map_pooling(Xk[...,0], angle, self._pooling_settings.get('rotation_spreading_radius', 0),
num_orientations,
num_parts,
self._pooling_settings['shape'])
else:
from pnet.cyfuncs import index_map_pooling_multi as poolf
print(Xk.shape)
yy1 = poolf(Xk,num_parts,self._pooling_settings.get('shape'), self._pooling_settings.get('strides'))
print(yy1.shape, num_orientations, num_true_parts)
yy = yy1.reshape(yy1.shape[:3] + (num_orientations, num_true_parts))
blocks.append(yy)#.reshape(yy.shape[:-2] + (-1,)))
blocks = np.asarray(blocks).transpose((1, 0, 2, 3, 4, 5))
print('D')
if 0:
from pnet.vzlog import default as vz
import gv
for i in xrange(self._n_orientations):
gv.img.save_image(vz.generate_filename(), blocks[0,i,:,:,0].sum(-1))
vz.finalize()
shape = blocks.shape[2:4] + (np.prod(blocks.shape[4:]),)
# Flatten
blocks = blocks.reshape(blocks.shape[:2] + (-1,))
n_init = self._settings.get('n_init', 1)
n_iter = self._settings.get('n_iter', 10)
seed = self._settings.get('em_seed', 0)
ORI = self._n_orientations
POL = 1
P = ORI * POL
def cycles(X):
return np.asarray([np.concatenate([X[i:], X[:i]]) for i in xrange(len(X))])
RR = np.arange(ORI)
PP = np.arange(POL)
II = [list(itr.product(PPi, RRi)) for PPi in cycles(PP) for RRi in cycles(RR)]
lookup = dict(zip(itr.product(PP, RR), itr.count()))
permutations = np.asarray([[lookup[ii] for ii in rows] for rows in II])
print('E')
if 0:
mm = PermutationMM(n_components=self._n_components,
permutations=permutations,
n_iter=n_iter,
n_init=n_init,
random_state=seed,
min_probability=self._min_prob)
mm.fit(blocks)
mu = mm.means_.reshape((self._n_components,)+shape)
else:
num_angle = self._n_orientations
d = np.prod(shape)
permutation = np.empty((num_angle, num_angle * d), dtype=np.int_)
for a in range(num_angle):
if a == 0:
permutation[a] = np.arange(num_angle * d)
else:
permutation[a] = np.roll(permutation[a-1], -d)
from pnet.bernoulli import em
XX = blocks.reshape((blocks.shape[0], -1))
print('F')
ret = em(XX, self._n_components, n_iter,
permutation=permutation, numpy_rng=seed,
verbose=True)
print('G')
self._extra['training_comp'].append(ret[3])
mu = ret[1].reshape((self._n_components * self._n_orientations,) + shape)
mm_models.append(mu)
print('H')
self._models = np.asarray(mm_models)
