args = parser.parse_args()
training_seed = args.training_seed
if args.log:
from pnet.vzlog import default as vz
ag.set_verbose(True)
sup_ims = []
sup_labels = []
net = None
layers = [
#pnet.IntensityThresholdLayer(),
pnet.EdgeLayer(k=5, radius=1, spread='orthogonal', minimum_contrast=0.05),
pnet.PartsLayer(100, (6, 6), settings=dict(outer_frame=0,
threshold=40,
samples_per_image=40,
max_samples=1000000,
min_prob=0.005,
)),
pnet.ExtensionPartsLayer(num_parts = 100, num_components = 10, part_shape = (12,12), lowerLayerShape = (6,6)),
pnet.PoolingLayer(shape=(4,4), strides=(4, 4)),
pnet.MixtureClassificationLayer(n_components=1, min_prob=0.0001,block_size=200),
#pnet.SVMClassificationLayer(C=None)
]
net = pnet.PartsNet(layers)
digits = range(10)
ims = ag.io.load_mnist('training', selection=slice(10000), return_labels=False)
net.train(ims)
data = np.load(dataFileName)
training_seed = args.seed
for i in range(11):
print("Inside")
layers = [
pnet.EdgeLayer(k=5,
radius=1,
spread='orthogonal',
minimum_contrast=0.05), #
pnet.PartsLayer(
numParts,
(patchSize, patchSize),
settings=dict(
outer_frame=0,
#em_seed=training_seed,
threshold=40,
samples_per_image=40,
max_samples=1000000,
min_prob=0.005,
)),
pnet.PoolingLayer(shape=(4, 4), strides=(4, 4)),
pnet.SVMClassificationLayer(C=None)
]
net = pnet.PartsNet(layers)
digits = range(10)
print('Extracting subsets...')
ims10k = data[:10000]