def trainGaussian():
training_seed = 1
layers = [
pnet.OrientedGaussianPartsLayer(32,4,(5,5),settings=dict(
seed=training_seed,
n_init = 2,
samples_per_image=40,
max_samples=100000,
channel_mode='together'
#covariance_type = ''
)),
pnet.PoolingLayer(shape=(8,8),strides=(2,2)),
pnet.SVMClassificationLayer(C=1.0)
]
net = pnet.PartsNet(layers)
trainingData, trainingLabel, testingData, testingLabel = load_mean_cifar10()
net.train(np.rollaxis(trainingData,1,4)/255.0, trainingLabel)
return net
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]
print('Done.')
start0 = time.time()
print('Training unsupervised...')
net.train(ims10k)
print('Done.')
end0 = time.time()
ims10k = data[:10000]
print('Done.')
start0 = time.time()
print('Training unsupervised...')
net.train(ims10k)
print('Done.')
end0 = time.time()
error_rates = []
test_ims, test_labels = ag.io.load_mnist('testing',return_labels=True)
for i in range(11):
clnet = pnet.PartsNet([net] + [pnet.PoolingLayer(shape=(4,4),strides=(4,4)), pnet.SVMClassificationLayer(C=None)])
digits = range(10)
sup_ims = []
sup_labels = []
rs = np.random.RandomState(i)
for d in digits:
ims0 = ag.io.load_mnist('training',[d],return_labels=False)
#indices = [k for k in range(len(label)) if label[k] in [d]]
indices = np.arange(ims0.shape[0])
print(indices[:10])
rs.shuffle(indices)
print(indices[:10])
sup_ims.append(ims0[indices[:10]])
sup_labels.append(d * np.ones(10,dtype=np.int64))
sup_ims = np.concatenate(sup_ims, axis = 0)
sup_labels = np.concatenate(sup_labels, axis = 0)
