#'lr_vb': 0.10,
'irange': 0.001,
}
# A symbolic input representing your minibatch.
minibatch = tensor.matrix()
minibatch = theano.printing.Print('min')(minibatch)
# Allocate a denoising autoencoder with binomial noise corruption.
cae = ContractiveAutoencoder(conf['nvis'], conf['nhid'], conf['act_enc'],
conf['act_dec'])
# Allocate an optimizer, which tells us how to update our model.
cost = SquaredError(cae)(minibatch, cae.reconstruct(minibatch)).mean()
cost += cae.contraction_penalty(minibatch).mean()
trainer = SGDOptimizer(cae, conf['base_lr'], conf['anneal_start'])
updates = trainer.cost_updates(cost)
# Finally, build a Theano function out of all this.
train_fn = theano.function([minibatch], cost, updates=updates)
# Suppose we want minibatches of size 10
batchsize = 10
# Here's a manual training loop. I hope to have some classes that
# automate this a litle bit.
for epoch in xrange(5):
for offset in xrange(0, data.shape[0], batchsize):
minibatch_err = train_fn(data[offset:(offset + batchsize)])
print("epoch %d, batch %d-%d: %f" %
(epoch, offset, offset + batchsize - 1, minibatch_err))
#'lr_vb': 0.10,
'irange': 0.001,
}
# A symbolic input representing your minibatch.
minibatch = tensor.matrix()
minibatch = theano.printing.Print('min')(minibatch)
# Allocate a denoising autoencoder with binomial noise corruption.
cae = ContractiveAutoencoder(conf['nvis'], conf['nhid'],
conf['act_enc'], conf['act_dec'])
# Allocate an optimizer, which tells us how to update our model.
cost = SquaredError(cae)(minibatch, cae.reconstruct(minibatch)).mean()
cost += cae.contraction_penalty(minibatch).mean()
trainer = SGDOptimizer(cae, conf['base_lr'], conf['anneal_start'])
updates = trainer.cost_updates(cost)
# Finally, build a Theano function out of all this.
train_fn = theano.function([minibatch], cost, updates=updates)
# Suppose we want minibatches of size 10
batchsize = 10
# Here's a manual training loop. I hope to have some classes that
# automate this a litle bit.
for epoch in xrange(5):
for offset in xrange(0, data.shape[0], batchsize):
minibatch_err = train_fn(data[offset:(offset + batchsize)])
print ("epoch %d, batch %d-%d: %f" %
(epoch, offset, offset + batchsize - 1, minibatch_err))
W_irange=conf['W_irange'],
rng=rng)
sampler = PersistentCDSampler(
rbm,
data[0:conf['batch_size']],
rng,
steps=conf['pcd_steps'],
particles_clip=(conf['particles_min'], conf['particles_max']),
)
minibatch = tensor.matrix()
optimizer = SGDOptimizer(
rbm,
conf['base_lr'],
conf['anneal_start'],
log_alpha_clip=(numpy.log(conf['alpha_min']), numpy.log(conf['alpha_max'])),
B_clip=(conf['B_min'], conf['B_max']),
Lambda_clip=(conf['Lambda_min'], conf['Lambda_max']),
)
updates = training_updates(visible_batch=minibatch, model=rbm,
sampler=sampler, optimizer=optimizer)
proxy_cost = rbm.reconstruction_error(minibatch, rng=sampler.s_rng)
train_fn = theano.function([minibatch], proxy_cost, updates=updates)
vis = tensor.matrix('vis')
free_energy_fn = theano.function([vis], rbm.free_energy_given_v(vis))
utils.debug.setdebug()
recon = []
def main_train(epochs, batchsize, solution="", sparse_penalty=0, sparsityTarget=0, sparsityTargetPenalty=0):
# Experiment specific arguments
conf_dataset = {
"dataset": "avicenna",
"expname": "dummy", # Used to create the submission file
"transfer": True,
"normalize": True, # (Default = True)
"normalize_on_the_fly": False, # (Default = False)
"randomize_valid": True, # (Default = True)
"randomize_test": True, # (Default = True)
"saving_rate": 0, # (Default = 0)
"savedir": "./outputs",
}
# First layer = PCA-75 whiten
pca_layer = {
"name": "1st-PCA",
"num_components": 75,
"min_variance": -50,
"whiten": True,
"pca_class": "CovEigPCA",
# Training properties
"proba": [1, 0, 0],
"savedir": "./outputs",
}
# Load the dataset
data = utils.load_data(conf_dataset)
if conf_dataset["transfer"]:
# Data for the ALC proxy
label = data[3]
data = data[:3]
# First layer : train or load a PCA
pca = create_pca(conf_dataset, pca_layer, data, model=pca_layer["name"])
data = [utils.sharedX(pca.function()(set.get_value(borrow=True)), borrow=True) for set in data]
"""
if conf_dataset['transfer']:
data_train, label_train = utils.filter_labels(data[0], label)
alc = embed.score(data_train, label_train)
print '... resulting ALC on train (for PCA) is', alc
"""
nvis = utils.get_constant(data[0].shape[1]).item()
conf = {
"corruption_level": 0.1,
"nhid": 200,
"nvis": nvis,
"anneal_start": 100,
"base_lr": 0.001,
"tied_weights": True,
"act_enc": "sigmoid",
"act_dec": None,
#'lr_hb': 0.10,
#'lr_vb': 0.10,
"tied_weights": True,
"solution": solution,
"sparse_penalty": sparse_penalty,
"sparsityTarget": sparsityTarget,
"sparsityTargetPenalty": sparsityTargetPenalty,
"irange": 0,
}
# A symbolic input representing your minibatch.
minibatch = tensor.matrix()
# Allocate a denoising autoencoder with binomial noise corruption.
corruptor = GaussianCorruptor(conf["corruption_level"])
da = DenoisingAutoencoder(
corruptor,
conf["nvis"],
conf["nhid"],
conf["act_enc"],
conf["act_dec"],
conf["tied_weights"],
conf["solution"],
conf["sparse_penalty"],
conf["sparsityTarget"],
conf["sparsityTargetPenalty"],
)
# Allocate an optimizer, which tells us how to update our model.
# TODO: build the cost another way
cost = SquaredError(da)(minibatch, da.reconstruct(minibatch)).mean()
trainer = SGDOptimizer(da, conf["base_lr"], conf["anneal_start"])
updates = trainer.cost_updates(cost)
# Finally, build a Theano function out of all this.
train_fn = theano.function([minibatch], cost, updates=updates)
# Suppose we want minibatches of size 10
proba = utils.getboth(conf, pca_layer, "proba")
iterator = BatchIterator(data, proba, batchsize)
# Here's a manual training loop. I hope to have some classes that
# automate this a litle bit.
final_cost = 0
for epoch in xrange(epochs):
c = []
for minibatch_data in iterator:
minibatch_err = train_fn(minibatch_data)
c.append(minibatch_err)
final_cost = numpy.mean(c)
print "epoch %d, cost : %f" % (epoch, final_cost)
print "############################## Fin de l'experience ############################"
print "Calcul de l'ALC : "
if conf_dataset["transfer"]:
data_train, label_train = utils.filter_labels(data[0], label)
alc = embed.score(data_train, label_train)
print "Solution : ", solution
print "sparse_penalty = ", sparse_penalty
print "sparsityTarget = ", sparsityTarget
print "sparsityTargetPenalty = ", sparsityTargetPenalty
print "Final denoising error is : ", final_cost
print "... resulting ALC on train is", alc
return (alc, final_cost)
def main_train(epochs,
batchsize,
solution='',
sparse_penalty=0,
sparsityTarget=0,
sparsityTargetPenalty=0):
# Experiment specific arguments
conf_dataset = {
'dataset': 'avicenna',
'expname': 'dummy', # Used to create the submission file
'transfer': True,
'normalize': True, # (Default = True)
'normalize_on_the_fly': False, # (Default = False)
'randomize_valid': True, # (Default = True)
'randomize_test': True, # (Default = True)
'saving_rate': 0, # (Default = 0)
'savedir': './outputs',
}
# First layer = PCA-75 whiten
pca_layer = {
'name': '1st-PCA',
'num_components': 75,
'min_variance': -50,
'whiten': True,
'pca_class': 'CovEigPCA',
# Training properties
'proba': [1, 0, 0],
'savedir': './outputs',
}
# Load the dataset
data = utils.load_data(conf_dataset)
if conf_dataset['transfer']:
# Data for the ALC proxy
label = data[3]
data = data[:3]
# First layer : train or load a PCA
pca = create_pca(conf_dataset, pca_layer, data, model=pca_layer['name'])
data = [
utils.sharedX(pca.function()(set.get_value(borrow=True)), borrow=True)
for set in data
]
'''
if conf_dataset['transfer']:
data_train, label_train = utils.filter_labels(data[0], label)
alc = embed.score(data_train, label_train)
print '... resulting ALC on train (for PCA) is', alc
'''
nvis = utils.get_constant(data[0].shape[1]).item()
conf = {
'corruption_level': 0.1,
'nhid': 200,
'nvis': nvis,
'anneal_start': 100,
'base_lr': 0.001,
'tied_weights': True,
'act_enc': 'sigmoid',
'act_dec': None,
#'lr_hb': 0.10,
#'lr_vb': 0.10,
'tied_weights': True,
'solution': solution,
'sparse_penalty': sparse_penalty,
'sparsityTarget': sparsityTarget,
'sparsityTargetPenalty': sparsityTargetPenalty,
'irange': 0,
}
# A symbolic input representing your minibatch.
minibatch = tensor.matrix()
# Allocate a denoising autoencoder with binomial noise corruption.
corruptor = GaussianCorruptor(conf['corruption_level'])
da = DenoisingAutoencoder(corruptor, conf['nvis'], conf['nhid'],
conf['act_enc'], conf['act_dec'],
conf['tied_weights'], conf['solution'],
conf['sparse_penalty'], conf['sparsityTarget'],
conf['sparsityTargetPenalty'])
# Allocate an optimizer, which tells us how to update our model.
# TODO: build the cost another way
cost = SquaredError(da)(minibatch, da.reconstruct(minibatch)).mean()
trainer = SGDOptimizer(da, conf['base_lr'], conf['anneal_start'])
updates = trainer.cost_updates(cost)
# Finally, build a Theano function out of all this.
train_fn = theano.function([minibatch], cost, updates=updates)
# Suppose we want minibatches of size 10
proba = utils.getboth(conf, pca_layer, 'proba')
iterator = BatchIterator(data, proba, batchsize)
# Here's a manual training loop. I hope to have some classes that
# automate this a litle bit.
final_cost = 0
for epoch in xrange(epochs):
c = []
for minibatch_data in iterator:
minibatch_err = train_fn(minibatch_data)
c.append(minibatch_err)
final_cost = numpy.mean(c)
print "epoch %d, cost : %f" % (epoch, final_cost)
print '############################## Fin de l\'experience ############################'
print 'Calcul de l\'ALC : '
if conf_dataset['transfer']:
data_train, label_train = utils.filter_labels(data[0], label)
alc = embed.score(data_train, label_train)
print 'Solution : ', solution
print 'sparse_penalty = ', sparse_penalty
print 'sparsityTarget = ', sparsityTarget
print 'sparsityTargetPenalty = ', sparsityTargetPenalty
print 'Final denoising error is : ', final_cost
print '... resulting ALC on train is', alc
return (alc, final_cost)
'nhid': 30,
'rbm_seed': 1,
'batch_size': 100,
'base_lr': 1e-4,
'anneal_start': 1,
'pcd_steps': 1,
}
rbm = GaussianBinaryRBM(nvis=conf['nvis'], nhid=conf['nhid'],
irange=0.5, energy_function_class = GRBM_Type_1)
rng = numpy.random.RandomState(seed=conf.get('rbm_seed', 42))
sampler = BlockGibbsSampler(rbm, data[0:100], rng,
steps=conf['pcd_steps'])
minibatch = tensor.matrix()
optimizer = SGDOptimizer(rbm, conf['base_lr'], conf['anneal_start'])
updates = training_updates(visible_batch=minibatch, model=rbm,
sampler=sampler, optimizer=optimizer)
proxy_cost = rbm.reconstruction_error(minibatch, rng=sampler.s_rng)
train_fn = theano.function([minibatch], proxy_cost, updates=updates)
vis = tensor.matrix('vis')
free_energy_fn = theano.function([vis], rbm.free_energy_given_v(vis))
#utils.debug.setdebug()
recon = []
nlls = []
for j in range(0, 401):
avg_rec_error = 0
