def KanadeAssociativeRBM(cache=False, train_further=False):
print "Testing Associative RBM which tries to learn the ID map "
# print "Testing Associative RBM which tries to learn the following mapping: {anger, saddness, disgust} -> {sadness}, {contempt, happy, surprise} -> {happy}"
# project set-up
data_manager = store.StorageManager('Kanade/OptMFSparse0.01RBMTest',
log=True)
# data_manager = store.StorageManager('Kanade/OptAssociativeRBMTest', log=True)
shape = 25
dataset_name = 'sharp_equi{}_{}'.format(shape, shape)
# Load kanade database
mapping = None # id map
# mapping = {'anger': 'sadness', 'contempt': 'happy', 'disgust': 'sadness', 'fear': 'sadness', 'happy': 'happy',
# 'sadness': 'sadness', 'surprise': 'happy'}
train, valid, test = loader.load_kanade(pre={'scale': True},
set_name=dataset_name)
train_x, train_y = train
test_x, test_y = test
# Sample associated image
train_x_mapped, train_y_mapped = loader.sample_image(train_y,
mapping=mapping,
pre={'scale': True},
set_name=dataset_name)
test_x_mapped, test_y_mapped = loader.sample_image(test_y,
mapping=mapping,
pre={'scale': True},
set_name=dataset_name)
# Concatenate images
concat1 = T.concatenate([train_x, train_x_mapped], axis=1)
# concat2 = T.concatenate([train_x_mapped, train_x], axis=1)
# concat = T.concatenate([concat1, concat2], axis=0)
# train_tX = theano.function([], concat)()
train_tX = theano.function([], concat1)()
train_X = theano.shared(train_tX)
# Train classifier to be used for classifying reconstruction associated image layer
# mapped_data = loader.load_kanade(#emotions=['sadness', 'happy'],
# pre={'scale': True},
# set_name=dataset_name) # Target Image
# clf_orig = SimpleClassifier('logistic', mapped_data[0][0], mapped_data[0][1])
clf_orig = SimpleClassifier('logistic', train_x, train_y)
# Initialise RBM
tr = rbm_config.TrainParam(learning_rate=0.0001,
momentum_type=rbm_config.NESTEROV,
momentum=0.9,
weight_decay=0.0001,
sparsity_constraint=True,
sparsity_target=0.01,
sparsity_cost=100,
sparsity_decay=0.9,
batch_size=10,
epochs=10)
n_visible = shape * shape * 2
n_hidden = 500
config = rbm_config.RBMConfig()
config.v_n = n_visible
config.h_n = n_hidden
config.v_unit = rbm_units.GaussianVisibleUnit
# config.h_unit = rbm_units.ReLUnit
config.progress_logger = rbm_logger.ProgressLogger(img_shape=(shape * 2,
shape))
config.train_params = tr
rbm = RBM(config)
print "... initialised RBM"
# Load RBM (test)
loaded = data_manager.retrieve(str(rbm))
if loaded:
rbm = loaded
else:
rbm.set_initial_hidden_bias()
rbm.set_hidden_mean_activity(train_X)
# Train RBM - learn joint distribution
# rbm.pretrain_lr(train_x, train_x01)
for i in xrange(0, 10):
if not cache or train_further:
rbm.train(train_X)
data_manager.persist(rbm)
print "... reconstruction of associated images"
# Get reconstruction with train data to get 'mapped' images to train classifiers on
reconstruction = rbm.reconstruct(train_X,
1,
plot_n=100,
plot_every=1,
img_name='recon_train')
reconstruct_assoc_part = reconstruction[:, (shape**2):]
# Get associated images of test data
nsamples = np.random.normal(0, 1,
test_x.get_value(True).shape).astype(
np.float32)
initial_y = theano.shared(nsamples, name='initial_y')
utils.save_images(nsamples[0:100], 'initialisation.png', (10, 10),
(25, 25))
test_x_associated = rbm.reconstruct_association_opt(
test_x,
initial_y,
5,
0.,
plot_n=100,
plot_every=1,
img_name='recon_test_gibbs')
mf_recon = rbm.mean_field_inference_opt(test_x,
y=initial_y,
sample=False,
k=10,
img_name='recon_test_mf_raw')
# Concatenate images
test_MFX = theano.function([], T.concatenate([test_x, mf_recon],
axis=1))()
test_MF = theano.shared(test_MFX)
reconstruction = rbm.reconstruct(test_MF,
1,
plot_n=100,
plot_every=1,
img_name='recon_test_mf_recon')
mf_recon = reconstruction[:, (shape**2):]
print "... reconstructed"
# Classify the reconstructions
# 1. Train classifier on original images
score_orig = clf_orig.get_score(test_x_associated,
test_y_mapped.eval())
score_orig_mf = clf_orig.get_score(test_x_associated,
test_y_mapped.eval())
# 2. Train classifier on reconstructed images
clf_recon = SimpleClassifier('logistic', reconstruct_assoc_part,
train_y_mapped.eval())
score_retrain = clf_recon.get_score(test_x_associated,
test_y_mapped.eval())
score_retrain_mf = clf_recon.get_score(mf_recon, test_y_mapped.eval())
out_msg = '{} (orig, retrain):{},{}'.format(rbm, score_orig,
score_retrain)
out_msg2 = '{} (orig, retrain):{},{}'.format(rbm, score_orig_mf,
score_retrain_mf)
print out_msg
print out_msg2
def associate_data2data(cache=False, train_further=True):
print "Testing Associative RBM which tries to learn even-oddness of numbers"
# project set-up
data_manager = store.StorageManager('EvenOddP', log=True)
train_n = 10000
test_n = 1000
# Load mnist hand digits, class label is already set to binary
dataset = m_loader.load_digits(n=[train_n, 100, test_n],
digits=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
pre={'binary_label': True})
tr_x, tr_y = dataset[0]
te_x, te_y = dataset[2]
tr_x01 = m_loader.sample_image(tr_y)
te_x01 = m_loader.sample_image(te_y)
ones = m_loader.load_digits(n=[test_n, 0, 0], digits=[1])[0][0]
zeroes = m_loader.load_digits(n=[test_n, 0, 0], digits=[0])[0][0]
concat1 = theano.function([], T.concatenate([tr_x, tr_x01], axis=1))()
# concat2 = theano.function([], T.concatenate([tr_x01, tr_x], axis=1))()
# c = np.concatenate([concat1, concat2], axis=0)
# np.random.shuffle(c)
# tr_concat_x = theano.shared(c, name='tr_concat_x')
tr_concat_x = theano.shared(concat1, name='tr_concat_x')
tr = TrainParam(learning_rate=0.001,
momentum_type=NESTEROV,
momentum=0.5,
weight_decay=0.1,
sparsity_constraint=True,
sparsity_target=0.1,
sparsity_decay=0.9,
sparsity_cost=0.1,
dropout=True,
dropout_rate=0.5,
epochs=1)
# Even odd test
k = 1
n_visible = 784 * 2
n_visible2 = 0
n_hidden = 300
print "number of hidden nodes: %d" % n_hidden
config = RBMConfig(v_n=n_visible,
v2_n=n_visible2,
h_n=n_hidden,
cd_type=CLASSICAL,
cd_steps=k,
train_params=tr,
progress_logger=ProgressLogger(img_shape=(28 * 2, 28)))
rbm = RBM(config=config)
# Load RBM (test)
loaded = store.retrieve_object(str(rbm))
if loaded and cache:
rbm = loaded
print "... loaded precomputed rbm"
errors = []
for i in xrange(0, 10):
# Train RBM
if not loaded or train_further:
rbm.train(tr_concat_x)
# Save RBM
data_manager.persist(rbm)
# Reconstruct using RBM
recon_x = rbm.reconstruct_association_opt(te_x, k=10, bit_p=0)
clf = SimpleClassifier('logistic', te_x.get_value(), te_y.eval())
orig = te_y.eval()
error = clf.get_score(recon_x, orig)
print error
errors.append(error)
print errors
def KanadeAssociativeRBM(cache=False, train_further=False):
print "Testing Associative RBM which tries to learn the ID map "
# print "Testing Associative RBM which tries to learn the following mapping: {anger, saddness, disgust} -> {sadness}, {contempt, happy, surprise} -> {happy}"
# project set-up
data_manager = store.StorageManager('Kanade/OptMFSparse0.01RBMTest', log=True)
# data_manager = store.StorageManager('Kanade/OptAssociativeRBMTest', log=True)
shape = 25
dataset_name = 'sharp_equi{}_{}'.format(shape, shape)
# Load kanade database
mapping = None # id map
# mapping = {'anger': 'sadness', 'contempt': 'happy', 'disgust': 'sadness', 'fear': 'sadness', 'happy': 'happy',
# 'sadness': 'sadness', 'surprise': 'happy'}
train, valid, test = loader.load_kanade(pre={'scale': True}, set_name=dataset_name)
train_x, train_y = train
test_x, test_y = test
# Sample associated image
train_x_mapped, train_y_mapped = loader.sample_image(train_y, mapping=mapping, pre={'scale': True},
set_name=dataset_name)
test_x_mapped, test_y_mapped = loader.sample_image(test_y, mapping=mapping, pre={'scale': True},
set_name=dataset_name)
# Concatenate images
concat1 = T.concatenate([train_x, train_x_mapped], axis=1)
# concat2 = T.concatenate([train_x_mapped, train_x], axis=1)
# concat = T.concatenate([concat1, concat2], axis=0)
# train_tX = theano.function([], concat)()
train_tX = theano.function([], concat1)()
train_X = theano.shared(train_tX)
# Train classifier to be used for classifying reconstruction associated image layer
# mapped_data = loader.load_kanade(#emotions=['sadness', 'happy'],
# pre={'scale': True},
# set_name=dataset_name) # Target Image
# clf_orig = SimpleClassifier('logistic', mapped_data[0][0], mapped_data[0][1])
clf_orig = SimpleClassifier('logistic', train_x, train_y)
# Initialise RBM
tr = rbm_config.TrainParam(learning_rate=0.0001,
momentum_type=rbm_config.NESTEROV,
momentum=0.9,
weight_decay=0.0001,
sparsity_constraint=True,
sparsity_target=0.01,
sparsity_cost=100,
sparsity_decay=0.9,
batch_size=10,
epochs=10)
n_visible = shape * shape * 2
n_hidden = 500
config = rbm_config.RBMConfig()
config.v_n = n_visible
config.h_n = n_hidden
config.v_unit = rbm_units.GaussianVisibleUnit
# config.h_unit = rbm_units.ReLUnit
config.progress_logger = rbm_logger.ProgressLogger(img_shape=(shape * 2, shape))
config.train_params = tr
rbm = RBM(config)
print "... initialised RBM"
# Load RBM (test)
loaded = data_manager.retrieve(str(rbm))
if loaded:
rbm = loaded
else:
rbm.set_initial_hidden_bias()
rbm.set_hidden_mean_activity(train_X)
# Train RBM - learn joint distribution
# rbm.pretrain_lr(train_x, train_x01)
for i in xrange(0, 10):
if not cache or train_further:
rbm.train(train_X)
data_manager.persist(rbm)
print "... reconstruction of associated images"
# Get reconstruction with train data to get 'mapped' images to train classifiers on
reconstruction = rbm.reconstruct(train_X, 1,
plot_n=100,
plot_every=1,
img_name='recon_train')
reconstruct_assoc_part = reconstruction[:, (shape ** 2):]
# Get associated images of test data
nsamples = np.random.normal(0, 1, test_x.get_value(True).shape).astype(np.float32)
initial_y = theano.shared(nsamples, name='initial_y')
utils.save_images(nsamples[0:100], 'initialisation.png', (10, 10), (25, 25))
test_x_associated = rbm.reconstruct_association_opt(test_x, initial_y,
5,
0.,
plot_n=100,
plot_every=1,
img_name='recon_test_gibbs')
mf_recon = rbm.mean_field_inference_opt(test_x, y=initial_y,
sample=False,
k=10,
img_name='recon_test_mf_raw')
# Concatenate images
test_MFX = theano.function([], T.concatenate([test_x, mf_recon], axis=1))()
test_MF = theano.shared(test_MFX)
reconstruction = rbm.reconstruct(test_MF, 1,
plot_n=100,
plot_every=1,
img_name='recon_test_mf_recon')
mf_recon = reconstruction[:, (shape ** 2):]
print "... reconstructed"
# Classify the reconstructions
# 1. Train classifier on original images
score_orig = clf_orig.get_score(test_x_associated, test_y_mapped.eval())
score_orig_mf = clf_orig.get_score(test_x_associated, test_y_mapped.eval())
# 2. Train classifier on reconstructed images
clf_recon = SimpleClassifier('logistic', reconstruct_assoc_part, train_y_mapped.eval())
score_retrain = clf_recon.get_score(test_x_associated, test_y_mapped.eval())
score_retrain_mf = clf_recon.get_score(mf_recon, test_y_mapped.eval())
out_msg = '{} (orig, retrain):{},{}'.format(rbm, score_orig, score_retrain)
out_msg2 = '{} (orig, retrain):{},{}'.format(rbm, score_orig_mf, score_retrain_mf)
print out_msg
print out_msg2