def associate_data2dataDBN(cache=False):
print "Testing Joint DBN which tries to learn even-oddness of numbers"
# project set-up
data_manager = store.StorageManager('associative_dbn_test', log=True)
# Load mnist hand digits, class label is already set to binary
train, valid, test = m_loader.load_digits(n=[500, 100, 100], digits=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
pre={'binary_label': True})
train_x, train_y = train
test_x, test_y = test
train_x01 = m_loader.sample_image(train_y)
dataset01 = m_loader.load_digits(n=[500, 100, 100], digits=[0, 1])
# Initialise RBM parameters
# fixed base train param
base_tr = RBM.TrainParam(learning_rate=0.01,
momentum_type=RBM.CLASSICAL,
momentum=0.5,
weight_decay=0.0005,
sparsity_constraint=False,
epochs=20)
# top layer parameters
tr = RBM.TrainParam(learning_rate=0.1,
find_learning_rate=True,
momentum_type=RBM.NESTEROV,
momentum=0.5,
weight_decay=0.001,
sparsity_constraint=False,
epochs=20)
tr_top = RBM.TrainParam(learning_rate=0.1,
find_learning_rate=True,
momentum_type=RBM.CLASSICAL,
momentum=0.5,
weight_decay=0.001,
sparsity_constraint=False,
epochs=20)
# Layer 1
# Layer 2
# Layer 3
topology = [784, 500, 500, 100]
config = associative_dbn.DefaultADBNConfig()
config.topology_left = [784, 500, 500, 100]
config.topology_right = [784, 500, 500, 100]
config.reuse_dbn = False
config.top_rbm_params = tr_top
config.base_rbm_params = [base_tr, tr, tr]
for cd_type in [RBM.CLASSICAL, RBM.PERSISTENT]:
for n_ass in [100, 250, 500, 750, 1000]:
config.n_association = n_ass
config.top_cd_type = cd_type
# Construct DBN
assoc_dbn = associative_dbn.AssociativeDBN(config=config, data_manager=data_manager)
# Train
assoc_dbn.train(train_x, train_x01, cache=cache, optimise=True)
for n_recall in [1, 3, 5, 7, 10]:
for n_think in [0, 1, 3, 5, 7, 10]: # 1, 3, 5, 7, 10]:
# Reconstruct
sampled = assoc_dbn.recall(test_x, n_recall, n_think)
# Sample from top layer to generate data
sample_n = 100
utils.save_images(sampled, image_name='reconstruced_{}_{}_{}.png'.format(n_ass, n_recall, n_think),
shape=(sample_n / 10, 10))
dataset01[2] = (theano.shared(sampled), test_y)
def associate_data2dataDBN(cache=False):
print "Testing Associative DBN which tries to learn even-oddness of numbers"
# project set-up
data_manager = store.StorageManager('Kanade/associative_dbn_test',
log=True)
# Load mnist hand digits, class label is already set to binary
dataset = loader.load_kanade(n=500,
emotions=['anger', 'sadness', 'happy'],
pre={'scale2unit': True})
train_x, train_y = dataset
train_x01 = loader.sample_image(train_y)
dataset01 = loader.load_kanade(n=500)
# Initialise RBM parameters
# fixed base train param
base_tr = RBM.TrainParam(learning_rate=0.001,
momentum_type=RBM.CLASSICAL,
momentum=0.5,
weight_decay=0.0005,
sparsity_constraint=False,
epochs=20)
# top layer parameters
tr = RBM.TrainParam(
learning_rate=0.001,
# find_learning_rate=True,
momentum_type=RBM.NESTEROV,
momentum=0.5,
weight_decay=0.001,
sparsity_constraint=False,
epochs=20)
tr_top = RBM.TrainParam(
learning_rate=0.001,
# find_learning_rate=True,
momentum_type=RBM.CLASSICAL,
momentum=0.5,
weight_decay=0.001,
sparsity_constraint=False,
epochs=20)
# Layer 1
# Layer 2
# Layer 3
# topology = [784, 500, 500, 100]
config = associative_dbn.DefaultADBNConfig()
config.topology_left = [625, 500, 500, 100]
config.topology_right = [625, 500, 500, 100]
config.reuse_dbn = False
config.top_rbm_params = tr_top
config.base_rbm_params = [base_tr, tr, tr]
count = 0
for cd_type in [RBM.CLASSICAL, RBM.PERSISTENT]:
for n_ass in [100, 250, 500, 750, 1000]:
config.n_association = n_ass
config.top_cd_type = cd_type
# Construct DBN
ass_dbn = associative_dbn.AssociativeDBN(config=config,
data_manager=data_manager)
# Train
for trainN in xrange(0, 5):
ass_dbn.train(train_x, train_x01, cache=cache)
for n_recall in [1, 3, 10]:
for n_think in [0, 1, 3, 5, 10]: # 1, 3, 5, 7, 10]:
# Reconstruct
sampled = ass_dbn.recall(train_x, n_recall, n_think)
# Sample from top layer to generate data
sample_n = 100
utils.save_images(
sampled,
image_name='{}_reconstruced_{}_{}_{}.png'.format(
count, n_ass, n_recall, n_think),
shape=(sample_n / 10, 10),
img_shape=(25, 25))
count += 1