def __init__(self,
k,
nvis,
convergence_th=1e-6,
max_iter=None,
verbose=False):
"""
Parameters in conf:
:type k: int
:param k: number of clusters.
:type convergence_th: float
:param convergence_th: threshold of distance to clusters under which
kmeans stops iterating.
:type max_iter: int
:param max_iter: maximum number of iterations. Defaults to infinity.
"""
Block.__init__(self)
Model.__init__(self)
self.input_space = VectorSpace(nvis)
self.k = k
self.convergence_th = convergence_th
if max_iter:
if max_iter < 0:
raise Exception('KMeans init: max_iter should be positive.')
self.max_iter = max_iter
else:
self.max_iter = float('inf')
self.verbose = verbose
def __init__(self, rbms=None, max_updates=1e6, flags={}):
Model.__init__(self)
Block.__init__(self)
self.jobman_channel = None
self.jobman_state = {}
self.validate_flags(flags)
self.register_names_to_del(['jobman_channel'])
# dump initialization parameters to object
for (k,v) in locals().iteritems():
if k!='self': setattr(self,k,v)
# validate that RBMs have the same number of units.
for (rbm1, rbm2) in zip(rbms[:-1], rbms[1:]):
assert rbm1.n_h == rbm2.n_v
assert rbm1.batch_size == rbm2.batch_size
#assert rbm1.flags['enable_centering']
#assert rbm2.flags['enable_centering']
self.rbms = rbms
self.depth = len(rbms)
self.rng = self.rbms[0].rng
# configure input-space (necessary evil)
self.input_space = VectorSpace(self.rbms[0].n_v)
self.output_space = VectorSpace(self.rbms[-1].n_h)
self.batches_seen = 0 # incremented on every batch
self.examples_seen = 0 # incremented on every training example
self.batch_size = self.rbms[0].batch_size
self.cpu_time = 0
self.init_train_sequence()
self.do_theano()
def __init__(self, k, nvis, convergence_th=1e-6, max_iter=None, verbose=False):
"""
Parameters in conf:
:type k: int
:param k: number of clusters.
:type convergence_th: float
:param convergence_th: threshold of distance to clusters under which
kmeans stops iterating.
:type max_iter: int
:param max_iter: maximum number of iterations. Defaults to infinity.
"""
Block.__init__(self)
Model.__init__(self)
self.input_space = VectorSpace(nvis)
self.k = k
self.convergence_th = convergence_th
if max_iter:
if max_iter < 0:
raise Exception('KMeans init: max_iter should be positive.')
self.max_iter = max_iter
else:
self.max_iter = float('inf')
self.verbose = verbose
def __init__(self, n_vis, n_hid, sigma=0.4, W=None, h_bias=None, v_bias=None, numpy_rng=None,theano_rng=None):
""" """
Model.__init__(self) # self.names_to_del = set(); self._test_batch_size = 2
Block.__init__(self) # self.fn = None; self.cpu_only = False
self.n_vis = n_vis
self.n_hid = n_hid
self.sigma = sigma
self.coeff = 1. / (self.sigma**2) #coefficient
self.input_space = VectorSpace(dim=self.n_vis) # add input_space
self.output_space = VectorSpace(dim=self.n_hid) # add output_space
if numpy_rng is None:
# create a number generator
numpy_rng = numpy.random.RandomState(seed=19920130)
self.numpy_rng = numpy_rng
if theano_rng is None:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
self.theano_rng = theano_rng
if W is None:
init_W = numpy.asarray(numpy_rng.uniform(
low=-4 * numpy.sqrt(6. / (n_hid + n_vis)),
high=4 * numpy.sqrt(6. / (n_hid + n_vis)),
size=(n_vis, n_hid)),
dtype=theano.config.floatX)
# theano shared variables for weights and biases
W = theano.shared(value=init_W, name='W', borrow=True)
else:
assert isinstance(W, theano.tensor.sharedvar.TensorSharedVariable)
assert W.get_value().ndim == 2
if h_bias is None:
# create shared variable for hidden units bias
h_bias = theano.shared(value=numpy.zeros(n_hid, dtype=theano.config.floatX), name='h_bias', borrow=True)
else:
assert isinstance(h_bias, theano.tensor.sharedvar.TensorSharedVariable)
assert h_bias.get_value().ndim == 1
if v_bias is None:
# create shared variable for visible units bias
v_bias = theano.shared(value=numpy.zeros(n_vis, dtype=theano.config.floatX), name='v_bias', borrow=True)
else:
assert isinstance(W, theano.tensor.sharedvar.TensorSharedVariable)
assert v_bias.get_value().ndim == 1
self.W = W
self.h_bias = h_bias
self.v_bias = v_bias
self._params = [self.W, self.h_bias, self.v_bias]
def __init__(self, n_vis, n_hid, corruptor=None, W=None, b_enc=None, b_dec=None, numpy_rng=None, dec_f=True, extra_cost=None,theano_rng=None):
"""构造函数
dec_f: 解码单元是否包含非线性函数
extra_cost:除了基本的MSE Cost和CE Cost之外的代价函数其他惩罚项，例如稀疏惩罚，weight decay等等.
用于self.get_default_cost()方法中. 这样依赖需要在模型初始化之前加入希望添加的惩罚项即可.
"""
Model.__init__(self) # self.names_to_del = set(); self._test_batch_size = 2
Block.__init__(self) # self.fn = None; self.cpu_only = False
self.n_vis = n_vis
self.n_hid = n_hid
self.extra_cost = extra_cost
self.dec_f = dec_f
if corruptor is not None:
self.corruptor = corruptor
self.input_space = VectorSpace(dim=self.n_vis) # add input_space
self.output_space = VectorSpace(dim=self.n_hid) # add output_space
if numpy_rng is None:
# create a number generator
numpy_rng = numpy.random.RandomState(seed=19900418)
self.numpy_rng = numpy_rng
if theano_rng is None:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
self.theano_rng = theano_rng
if W is None:
init_W = numpy.asarray(numpy_rng.uniform(
low= -4 * numpy.sqrt(6. / (n_hid + n_vis + 1.)),
high= 4 * numpy.sqrt(6. / (n_hid + n_vis + 1.)),
size=(n_vis, n_hid)),
dtype=theano.config.floatX)
# theano shared variables for weights and biases
W = theano.shared(value=init_W, name='W', borrow=True)
if b_enc is None:
# create shared variable for hidden units bias
b_enc = theano.shared(value=numpy.zeros(n_hid, dtype=theano.config.floatX), name='b_enc', borrow=True)
if b_dec is None:
# create shared variable for visible units bias
b_dec = theano.shared(value=numpy.zeros(n_vis, dtype=theano.config.floatX), name='b_dec', borrow=True)
self.W = W
self.b_enc = b_enc
self.b_dec = b_dec
self._params = [self.W, self.b_enc, self.b_dec]
def __init__(self, n_vis, n_hid, W=None, h_bias=None, v_bias=None, numpy_rng=None,theano_rng=None):
Model.__init__(self) # self.names_to_del = set(); self._test_batch_size = 2
Block.__init__(self) # self.fn = None; self.cpu_only = False
self.n_vis = n_vis
self.n_hid = n_hid
self.input_space = VectorSpace(dim=self.n_vis) # add input_space
self.output_space = VectorSpace(dim=self.n_hid) # add output_space
if numpy_rng is None:
# create a number generator
numpy_rng = numpy.random.RandomState(seed=19900418)
self.numpy_rng = numpy_rng
if theano_rng is None:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
self.theano_rng = theano_rng
if W is None:
init_W = numpy.asarray(numpy_rng.uniform(
low=-4 * numpy.sqrt(6. / (n_hid + n_vis)),
high=4 * numpy.sqrt(6. / (n_hid + n_vis)),
size=(n_vis, n_hid)),
dtype=theano.config.floatX)
# theano shared variables for weights and biases
W = theano.shared(value=init_W, name='W', borrow=True)
if h_bias is None:
# create shared variable for hidden units bias
h_bias = theano.shared(value=numpy.zeros(n_hid, dtype=theano.config.floatX), name='h_bias', borrow=True)
if v_bias is None:
# create shared variable for visible units bias
v_bias = theano.shared(value=numpy.zeros(n_vis, dtype=theano.config.floatX), name='v_bias', borrow=True)
self.W = W
self.h_bias = h_bias
self.v_bias = v_bias
self._params = [self.W, self.h_bias, self.v_bias]
def __init__(self,
input=None, Wv=None, vbias=None, hbias=None,
numpy_rng = None, theano_rng = None,
n_h=100, bw_s=1, n_v=100, init_from=None,
neg_sample_steps=1,
lr=None, lr_timestamp=None, lr_mults = {},
iscales={}, clip_min={}, clip_max={}, l1 = {}, l2 = {},
sp_type='kl', sp_weight={}, sp_targ={},
batch_size = 13,
compile=True,
debug=False,
seed=1241234,
my_save_path=None, save_at=None, save_every=None,
flags = {},
max_updates = 5e5):
"""
:param n_h: number of h-hidden units
:param n_v: number of visible units
:param iscales: optional dictionary containing initialization scale for each parameter
:param neg_sample_steps: number of sampling updates to perform in negative phase.
:param l1: hyper-parameter controlling amount of L1 regularization
:param l2: hyper-parameter controlling amount of L2 regularization
:param batch_size: size of positive and negative phase minibatch
:param compile: compile sampling and learning functions
:param seed: seed used to initialize numpy and theano RNGs.
"""
Model.__init__(self)
Block.__init__(self)
assert lr is not None
for k in ['Wv', 'vbias', 'hbias']: assert k in iscales.keys()
iscales.setdefault('mu', 1.)
iscales.setdefault('alpha', 0.)
for k in ['h']: assert k in sp_weight.keys()
for k in ['h']: assert k in sp_targ.keys()
self.jobman_channel = None
self.jobman_state = {}
self.register_names_to_del(['jobman_channel'])
### make sure all parameters are floatX ###
for (k,v) in l1.iteritems(): l1[k] = npy_floatX(v)
for (k,v) in l2.iteritems(): l2[k] = npy_floatX(v)
for (k,v) in sp_weight.iteritems(): sp_weight[k] = npy_floatX(v)
for (k,v) in sp_targ.iteritems(): sp_targ[k] = npy_floatX(v)
for (k,v) in clip_min.iteritems(): clip_min[k] = npy_floatX(v)
for (k,v) in clip_max.iteritems(): clip_max[k] = npy_floatX(v)
# dump initialization parameters to object
for (k,v) in locals().iteritems():
if k!='self': setattr(self,k,v)
# allocate random number generators
self.rng = numpy.random.RandomState(seed) if numpy_rng is None else numpy_rng
self.theano_rng = RandomStreams(self.rng.randint(2**30)) if theano_rng is None else theano_rng
############### ALLOCATE PARAMETERS #################
self.n_s = self.n_h * self.bw_s
if Wv is None:
wv_val = self.rng.randn(n_v, self.n_s) * iscales['Wv']
self.Wv = sharedX(wv_val, name='Wv')
else:
self.Wv = Wv
self.Wh = numpy.zeros((self.n_s, self.n_h), dtype=floatX)
for i in xrange(self.n_h):
self.Wh[i*bw_s:(i+1)*bw_s, i] = 1.
# allocate shared variables for bias parameters
if vbias is None:
self.vbias = sharedX(iscales['vbias'] * numpy.ones(n_v), name='vbias')
else:
self.vbias = vbias
# allocate shared variables for bias parameters
if hbias is None:
self.hbias = sharedX(iscales['hbias'] * numpy.ones(n_h), name='hbias')
else:
self.hbias = hbias
# mean (mu) and precision (alpha) parameters on s
self.mu = sharedX(iscales['mu'] * numpy.ones(self.n_s), name='mu')
self.alpha = sharedX(iscales['alpha'] * numpy.ones(self.n_s), name='alpha')
self.alpha_prec = T.exp(self.alpha)
#### load layer 1 parameters from file ####
if init_from:
self.load_params(init_from)
# allocate shared variable for persistent chain
self.neg_v = sharedX(self.rng.rand(batch_size, n_v), name='neg_v')
self.neg_ev = sharedX(self.rng.rand(batch_size, n_v), name='neg_ev')
self.neg_s = sharedX(self.rng.rand(batch_size, self.n_s), name='neg_s')
self.neg_h = sharedX(self.rng.rand(batch_size, n_h), name='neg_h')
# moving average values for sparsity
self.sp_pos_v = sharedX(self.rng.rand(1,self.n_v), name='sp_pos_v')
self.sp_pos_h = sharedX(self.rng.rand(1,self.n_h), name='sp_pog_h')
# learning rate, with deferred 1./t annealing
self.iter = sharedX(0.0, name='iter')
if lr['type'] == 'anneal':
num = lr['init'] * lr['start']
denum = T.maximum(lr['start'], lr['slope'] * self.iter)
self.lr = T.maximum(lr['floor'], num/denum)
elif lr['type'] == 'linear':
lr_start = npy_floatX(lr['start'])
lr_end = npy_floatX(lr['end'])
self.lr = lr_start + self.iter * (lr_end - lr_start) / npy_floatX(self.max_updates)
else:
raise ValueError('Incorrect value for lr[type]')
# learning rate - implemented as shared parameter for GPU
self.lr_mults_it = {}
self.lr_mults_shrd = {}
for (k,v) in lr_mults.iteritems():
# make sure all learning rate multipliers are float64
self.lr_mults_it[k] = tools.HyperParamIterator(lr_timestamp, lr_mults[k])
self.lr_mults_shrd[k] = sharedX(self.lr_mults_it[k].value,
name='lr_mults_shrd'+k)
# allocate symbolic variable for input
self.input = T.matrix('input') if input is None else input
# configure input-space (new pylearn2 feature?)
self.input_space = VectorSpace(n_v)
self.output_space = VectorSpace(n_h)
self.batches_seen = 0 # incremented on every batch
self.examples_seen = 0 # incremented on every training example
self.force_batch_size = batch_size # force minibatch size
self.error_record = []
if compile: self.do_theano()
def __init__(self,
input=None,
n_u=[100, 100],
enable={},
load_from=None,
iscales=None,
clip_min={},
clip_max={},
pos_mf_steps=1,
pos_sample_steps=0,
neg_sample_steps=1,
lr_spec={},
lr_mults={},
l1={},
l2={},
l1_inf={},
flags={},
momentum_lambda=0,
cg_params={},
batch_size=13,
computational_bs=0,
compile=True,
seed=1241234,
sp_targ_h=None,
sp_weight_h=None,
sp_pos_k=5,
my_save_path=None,
save_at=None,
save_every=None,
max_updates=1e6):
"""
:param n_u: list, containing number of units per layer. n_u[0] contains number
of visible units, while n_u[i] (with i > 0) contains number of hid. units at layer i.
:param enable: dictionary of flags with on/off behavior
:param iscales: optional dictionary containing initialization scale for each parameter.
Key of dictionary should match the name of the associated shared variable.
:param pos_mf_steps: number of mean-field iterations to perform in positive phase
:param neg_sample_steps: number of sampling updates to perform in negative phase.
:param lr: base learning rate
:param lr_timestamp: list containing update indices at which to change the lr multiplier
:param lr_mults: dictionary, optionally containing a list of learning rate multipliers
for parameters of the model. Length of this list should match length of
lr_timestamp (the lr mult will transition whenever we reach the associated
timestamp). Keys should match the name of the shared variable, whose learning
rate is to be adjusted.
:param l1: dictionary, whose keys are model parameter names, and values are
hyper-parameters controlling degree of L1-regularization.
:param l2: same as l1, but for L2 regularization.
:param l1_inf: same as l1, but the L1 penalty is centered as -\infty instead of 0.
:param cg_params: dictionary with keys ['rtol','damp','maxiter']
:param batch_size: size of positive and negative phase minibatch
:param computational_bs: batch size used internaly by natural
gradient to reduce memory consumption
:param seed: seed used to initialize numpy and theano RNGs.
:param my_save_path: if None, do not save model. Otherwise, contains stem of filename
to which we will save the model (everything but the extension).
:param save_at: list containing iteration counts at which to save model
:param save_every: scalar value. Save model every `save_every` iterations.
"""
Model.__init__(self)
Block.__init__(self)
### VALIDATE PARAMETERS AND SET DEFAULT VALUES ###
assert lr_spec is not None
for (k, v) in clip_min.iteritems():
clip_min[k] = npy_floatX(v)
for (k, v) in clip_max.iteritems():
clip_max[k] = npy_floatX(v)
[iscales.setdefault('bias%i' % i, 0.) for i in xrange(len(n_u))]
[iscales.setdefault('W%i' % i, 0.1) for i in xrange(len(n_u))]
flags.setdefault('enable_centering', False)
flags.setdefault('enable_natural', False)
flags.setdefault('enable_warm_start', False)
flags.setdefault('mlbiases', False)
flags.setdefault('precondition', None)
flags.setdefault('minres', False)
flags.setdefault('minresQLP', False)
if flags['precondition'] == 'None': flags['precondition'] = None
self.jobman_channel = None
self.jobman_state = {}
self.register_names_to_del(['jobman_channel'])
### DUMP INITIALIZATION PARAMETERS TO OBJECT ###
for (k, v) in locals().iteritems():
if k != 'self': setattr(self, k, v)
assert len(n_u) > 1
self.n_v = n_u[0]
self.depth = len(n_u)
# allocate random number generators
self.rng = numpy.random.RandomState(seed)
self.theano_rng = RandomStreams(self.rng.randint(2**30))
# allocate bilinear-weight matrices
self.input = T.matrix()
self.init_parameters()
self.init_dparameters()
self.init_centering()
self.init_samples()
# learning rate, with deferred 1./t annealing
self.iter = sharedX(0.0, name='iter')
if lr_spec['type'] == 'anneal':
num = lr_spec['init'] * lr_spec['start']
denum = T.maximum(lr_spec['start'], lr_spec['slope'] * self.iter)
self.lr = T.maximum(lr_spec['floor'], num / denum)
elif lr_spec['type'] == 'linear':
lr_start = npy_floatX(lr_spec['start'])
lr_end = npy_floatX(lr_spec['end'])
self.lr = lr_start + self.iter * (lr_end - lr_start) / npy_floatX(
self.max_updates)
else:
raise ValueError('Incorrect value for lr_spec[type]')
# counter for CPU-time
self.cpu_time = 0.
if load_from:
self.load_parameters(fname=load_from)
# configure input-space (?new pylearn2 feature?)
self.input_space = VectorSpace(n_u[0])
self.output_space = VectorSpace(n_u[-1])
self.batches_seen = 0 # incremented on every batch
self.examples_seen = 0 # incremented on every training example
self.force_batch_size = batch_size # force minibatch size
self.error_record = []
if compile: self.do_theano()
def __init__(self, model_type=None, alpha=0.2,
n_vis_img=None, n_vis_txt=None, n_hid_img=None, n_hid_txt=None, corruptor_img=None, corruptor_txt=None,
W_img=None, W_txt=None, b_enc_img=None, b_enc_txt=None, b_dec_img=None, b_dec_txt=None, dec_f_img=True, dec_f_txt=True,
img_AE=None, txt_AE=None, numpy_rng=None, theano_rng=None):
"""
model_type: String, 选择模型类型，目的是为了控制get_default_cost()方法找到所希望的训练代价
可选参数: 'Combine', 'CrossModal', 'FullModal'
param: alpha, 标准代价和关联代价的权重稀疏，alpha越大则标准代价在总的代价函数中的比重越大
param: img_AE, 图像端用AE
param: txt_AE, 文本端用AE
"""
Model.__init__(self) # self.names_to_del = set(); self._test_batch_size = 2
Block.__init__(self) # self.fn = None; self.cpu_only = False
assert model_type in ['Combine', 'CrossModal', 'FullModal']
self.model_type = model_type
self.alpha = alpha
if numpy_rng is None:
# create a number generator
numpy_rng = numpy.random.RandomState(seed=19900418)
self.numpy_rng = numpy_rng
if theano_rng is None:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
self.theano_rng = theano_rng
#两个AE共用的部分只有随机数发生器
if img_AE is None:
assert n_vis_img is not None
assert n_hid_img is not None
img_AE = MyAutoEncoder(n_vis=n_vis_img, n_hid=n_hid_img, corruptor=corruptor_img,
W=W_img, b_enc=b_enc_img, b_dec=b_dec_img, dec_f=dec_f_img,
numpy_rng=self.numpy_rng, theano_rng=self.theano_rng)
if txt_AE is None:
assert n_vis_txt is not None
assert n_hid_txt is not None
txt_AE = MyAutoEncoder(n_vis=n_vis_txt, n_hid=n_hid_txt, corruptor=corruptor_txt,
W=W_txt, b_enc=b_enc_txt, b_dec=b_dec_txt, dec_f=dec_f_txt,
numpy_rng=self.numpy_rng, theano_rng=self.theano_rng)
assert img_AE.n_hid == txt_AE.n_hid #目前的模型只能接受两端具有相同维度的编码空间
self.img_AE = img_AE
self.txt_AE = txt_AE
self.W_img = img_AE.W #not used
self.W_txt = txt_AE.W #not used
self.b_enc_img = img_AE.b_enc #not used
self.b_dec_img = img_AE.b_dec #not used
self.b_enc_txt = txt_AE.b_enc #not used
self.b_dec_txt = txt_AE.b_dec #not used
self.n_vis_img = self.img_AE.n_vis
self.n_vis_txt = self.txt_AE.n_vis
self.n_hid_img = self.img_AE.n_hid
self.n_hid_txt = self.txt_AE.n_hid
self.n_vis = self.img_AE.n_vis + self.txt_AE.n_vis
self.n_hid = self.img_AE.n_hid + self.txt_AE.n_hid
self.input_space = VectorSpace(dim=self.n_vis) # add input_space
self.output_space = VectorSpace(dim=self.n_hid) # add output_space
#init_W = numpy.concatenate([self.img_AE.W, self.txt_AE_W], axis=1)
#self.W = theano.shared(value=init_W, name='W', borrow=True)
#参数顺序：图像权值矩阵， 图像编码偏置，图像解码偏置，文本权值矩阵，文本编码偏置，文本解码偏置
self._params = [self.img_AE.W, self.img_AE.b_enc, self.img_AE.b_dec, self.txt_AE.W, self.txt_AE.b_enc, self.txt_AE.b_dec]
# Set PCA subclass from argument.
if args.algorithm == 'cov_eig':
PCAImpl = CovEigPCA
elif args.algorithm == 'svd':
PCAImpl = SVDPCA
elif args.algorithm == 'online':
PCAImpl = OnlinePCA
conf['minibatch_size'] = args.minibatch_size
else:
# This should never happen.
raise NotImplementedError(args.algorithm)
# Load precomputed PCA transformation if requested; otherwise compute it.
if args.load_file:
pca = Block.load(args.load_file)
else:
print "... computing PCA"
pca = PCAImpl(**conf)
pca.train(train_data)
# Save the computed transformation.
pca.save(args.save_file)
# Apply the transformation to test and valid subsets.
inputs = tensor.matrix()
pca_transform = theano.function([inputs], pca(inputs))
valid_pca = pca_transform(valid_data)
test_pca = pca_transform(test_data)
print >> sys.stderr, "New shapes:", map(numpy.shape, [valid_pca, test_pca])
# TODO: Compute ALC here when the code using the labels is ready.
# Set PCA subclass from argument.
if args.algorithm == 'cov_eig':
PCAImpl = CovEigPCA
elif args.algorithm == 'svd':
PCAImpl = SVDPCA
elif args.algorithm == 'online':
PCAImpl = OnlinePCA
conf['minibatch_size'] = args.minibatch_size
else:
# This should never happen.
raise NotImplementedError(args.algorithm)
# Load precomputed PCA transformation if requested; otherwise compute it.
if args.load_file:
pca = Block.load(args.load_file)
else:
print "... computing PCA"
pca = PCAImpl(**conf)
pca.train(train_data)
# Save the computed transformation.
pca.save(args.save_file)
# Apply the transformation to test and valid subsets.
inputs = tensor.matrix()
pca_transform = theano.function([inputs], pca(inputs))
valid_pca = pca_transform(valid_data)
test_pca = pca_transform(test_data)
print >> sys.stderr, "New shapes:", map(numpy.shape, [valid_pca, test_pca])
# TODO: Compute ALC here when the code using the labels is ready.
def __init__(self, numpy_rng = None, theano_rng = None,
n_h=100, bw_s=1, n_v=100, init_from=None,
neg_sample_steps=1,
lr_spec=None, lr_timestamp=None, lr_mults = {},
iscales={}, clip_min={}, clip_max={}, truncation_bound={},
l1 = {}, l2 = {}, orth_lambda=0.,
var_param_alpha='exp', var_param_lambd='linear',
sp_type='kl', sp_weight={}, sp_targ={},
batch_size = 13,
compile=True,
debug=False,
seed=1241234,
my_save_path=None, save_at=None, save_every=None,
flags = {},
max_updates = 5e5):
"""
:param n_h: number of h-hidden units
:param n_v: number of visible units
:param iscales: optional dictionary containing initialization scale for each parameter
:param neg_sample_steps: number of sampling updates to perform in negative phase.
:param l1: hyper-parameter controlling amount of L1 regularization
:param l2: hyper-parameter controlling amount of L2 regularization
:param batch_size: size of positive and negative phase minibatch
:param compile: compile sampling and learning functions
:param seed: seed used to initialize numpy and theano RNGs.
"""
Model.__init__(self)
Block.__init__(self)
assert lr_spec is not None
for k in ['Wv', 'hbias']: assert k in iscales.keys()
iscales.setdefault('mu', 1.)
iscales.setdefault('alpha', 0.)
iscales.setdefault('lambd', 0.)
for k in ['h']: assert k in sp_weight.keys()
for k in ['h']: assert k in sp_targ.keys()
self.validate_flags(flags)
self.jobman_channel = None
self.jobman_state = {}
self.register_names_to_del(['jobman_channel'])
### make sure all parameters are floatX ###
for (k,v) in l1.iteritems(): l1[k] = npy_floatX(v)
for (k,v) in l2.iteritems(): l2[k] = npy_floatX(v)
for (k,v) in sp_weight.iteritems(): sp_weight[k] = npy_floatX(v)
for (k,v) in sp_targ.iteritems(): sp_targ[k] = npy_floatX(v)
for (k,v) in clip_min.iteritems(): clip_min[k] = npy_floatX(v)
for (k,v) in clip_max.iteritems(): clip_max[k] = npy_floatX(v)
# dump initialization parameters to object
for (k,v) in locals().iteritems():
if k!='self': setattr(self,k,v)
# allocate random number generators
self.rng = numpy.random.RandomState(seed) if numpy_rng is None else numpy_rng
self.theano_rng = RandomStreams(self.rng.randint(2**30)) if theano_rng is None else theano_rng
# allocate symbolic variable for input
self.input = T.matrix('input')
self.init_parameters()
self.init_chains()
# learning rate, with deferred 1./t annealing
self.iter = sharedX(0.0, name='iter')
if lr_spec['type'] == 'anneal':
num = lr_spec['init'] * lr_spec['start']
denum = T.maximum(lr_spec['start'], lr_spec['slope'] * self.iter)
self.lr = T.maximum(lr_spec['floor'], num/denum)
elif lr_spec['type'] == 'linear':
lr_start = npy_floatX(lr_spec['start'])
lr_end = npy_floatX(lr_spec['end'])
self.lr = lr_start + self.iter * (lr_end - lr_start) / npy_floatX(self.max_updates)
else:
raise ValueError('Incorrect value for lr_spec[type]')
# configure input-space (new pylearn2 feature?)
self.input_space = VectorSpace(n_v)
self.output_space = VectorSpace(n_h)
self.batches_seen = 0 # incremented on every batch
self.examples_seen = 0 # incremented on every training example
self.force_batch_size = batch_size # force minibatch size
self.error_record = []
if compile: self.do_theano()
#### load layer 1 parameters from file ####
if init_from:
self.load_params(init_from)
def __init__(self, nvis, nhid, irange=0.5, rng=None, init_bias_vis = 0.0, init_bias_hid=0.0,
base_lr = 1e-3, anneal_start = None, nchains = 100, sml_gibbs_steps = 1,
random_patches_src = None,
monitor_reconstruction = False):
"""
Construct an RBM object.
Parameters
----------
nvis : int
Number of visible units in the model.
nhid : int
Number of hidden units in the model.
irange : float, optional
The size of the initial interval around 0 for weights.
rng : RandomState object or seed
NumPy RandomState object to use when initializing parameters
of the model, or (integer) seed to use to create one.
init_bias_vis : array_like, optional
Initial value of the visible biases, broadcasted as necessary.
init_bias_hid : array_like, optional
initial value of the hidden biases, broadcasted as necessary.
monitor_reconstruction : if True, will request a monitoring channel to monitor
reconstruction error
random_patches_src: Either None, or a Dataset from which to draw random patches
in order to initialize the weights. Patches will be multiplied by irange
Parameters for default SML learning rule:
base_lr : the base learning rate
anneal_start : number of steps after which to start annealing on a 1/t schedule
nchains: number of negative chains
sml_gibbs_steps: number of gibbs steps to take per update
"""
Model.__init__(self)
Block.__init__(self)
if rng is None:
# TODO: global rng configuration stuff.
rng = numpy.random.RandomState(1001)
self.rng = rng
try:
b_vis = numpy.zeros(nvis)
b_vis += init_bias_vis
except ValueError:
raise ValueError("bad shape or value for init_bias_vis")
self.bias_vis = sharedX(b_vis, name='bias_vis', borrow=True)
try:
b_hid = numpy.zeros(nhid)
b_hid += init_bias_hid
except ValueError:
raise ValueError('bad shape or value for init_bias_hid')
self.bias_hid = sharedX(b_hid, name='bias_hid', borrow=True)
self.random_patches_src = random_patches_src
self.register_names_to_del(['random_patches_src'])
if random_patches_src is None:
W = rng.uniform(-irange, irange, (nvis, nhid))
else:
if hasattr(random_patches_src, '__array__'):
W = irange * random_patches_src.T
assert W.shape == (nvis, nhid)
else:
#assert type(irange) == type(0.01)
#assert irange == 0.01
W = irange * random_patches_src.get_batch_design(nhid).T
self.weights = sharedX(
W,
name='W',
borrow=True
)
self.__dict__.update(nhid=nhid, nvis=nvis)
self._params = [self.bias_vis, self.bias_hid, self.weights]
self.base_lr = base_lr
self.anneal_start = anneal_start
self.nchains = nchains
self.sml_gibbs_steps = sml_gibbs_steps
def __init__(self, input_space, output_channels, pool_shape, batch_size=None, detector_axes=('b', 'c', 0, 1),
kernel_shape=(2,2), kernel_stride=(1, 1), border_mode='valid',
transformer=None, h_bias=None, v_bias=None, numpy_rng=None,theano_rng=None):
"""
vis_space: Conv2DSpace
transformer: pylearn2.linear.Conv2D instance
h_bias: vector, 大小等于输出的feature maps数，每个分量对应一个feature map
v_bias: vector, 大小等于输入的feature maps数，每个分量对应一个feature map
pool_shape:
pool_stride: 根据Honglak Lee的原文，pool区域无交叠，于是要求pool_stride=pool_shape，因此暂时不单独设置pool_stride参数
需要注意，对于卷积RBM，其隐层对应于卷积后的detector_layer，而输出则对应与pool_layer，因此相对于普通RBM只有输入和输出两个space，CRBM有三个space
"""
Model.__init__(self) # self.names_to_del = set(); self._test_batch_size = 2
Block.__init__(self) # self.fn = None; self.cpu_only = False
self.kernel_shape = kernel_shape
self.kernel_stride = kernel_stride
self.pool_shape = pool_shape
self.pool_stride = pool_shape
self.border_mode = border_mode
self.batch_size = batch_size
self.force_batch_size = batch_size
input_shape = input_space.shape
input_channels = input_space.num_channels
if self.border_mode == 'valid':
detector_shape = [(input_shape[0] - kernel_shape[0])/int(kernel_stride[0]) + 1, (input_shape[1] - kernel_shape[1])/kernel_stride[1] + 1]
elif self.border_mode == 'full':
detector_shape = [(input_shape[0] + kernel_shape[0])/int(kernel_stride[0]) - 1, (input_shape[1] + kernel_shape[1])/kernel_stride[1] - 1]
assert isinstance(input_space, Conv2DSpace)
self.input_space = input_space # add input_space
self.detector_space = Conv2DSpace(shape=detector_shape, num_channels=output_channels, axes=detector_axes) # add detector_space
#当前只考虑detector layer的feature map可以被pool_shape无交叠完整分割的情况
#今后需要补充：边缘补齐的情况
output_shape = (detector_shape[0] / pool_shape[0], detector_shape[1] / pool_shape[1])
self.output_space = Conv2DSpace(shape=output_shape, num_channels=output_channels, axes=detector_axes) # add output_space
self.n_vis = numpy.prod(input_space.shape) * input_space.num_channels
self.n_hid = detector_shape[0] * detector_shape[1] * output_channels
if numpy_rng is None:
# create a number generator
numpy_rng = numpy.random.RandomState(seed=19900418)
self.numpy_rng = numpy_rng
if theano_rng is None:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
self.theano_rng = theano_rng
if transformer is None:
irange = 4 * numpy.sqrt(6. / (self.n_hid + self.n_vis))
transformer = make_random_conv2D(irange=irange, input_space=self.input_space, output_space=self.detector_space,
kernel_shape = self.kernel_shape, batch_size = self.batch_size,
subsample = kernel_stride,border_mode = self.border_mode, rng=self.numpy_rng)
else:
assert isinstance(transformer, Conv2D)
if h_bias is None:
# create shared variable for hidden units bias
h_bias = theano.shared(value=numpy.zeros(self.detector_space.num_channels, dtype=theano.config.floatX), name='h_bias', borrow=True)
if v_bias is None:
# create shared variable for visible units bias
v_bias = theano.shared(value=numpy.zeros(self.input_space.num_channels, dtype=theano.config.floatX), name='v_bias', borrow=True)
self.transformer = transformer
self.h_bias = h_bias
self.v_bias = v_bias
self._params = safe_union(self.transformer.get_params(), [self.h_bias, self.v_bias])
def __init__(self,
input=None,
Wv=None,
vbias=None,
hbias=None,
numpy_rng=None,
theano_rng=None,
n_h=100,
bw_s=1,
n_v=100,
init_from=None,
neg_sample_steps=1,
lr=None,
lr_timestamp=None,
lr_mults={},
iscales={},
clip_min={},
clip_max={},
vbound=5.,
l1={},
l2={},
orth_lambda=0.,
var_param_alpha='exp',
var_param_beta='linear',
sp_type='kl',
sp_weight={},
sp_targ={},
batch_size=13,
scalar_b=False,
compile=True,
debug=False,
seed=1241234,
my_save_path=None,
save_at=None,
save_every=None,
flags={},
max_updates=5e5):
"""
:param n_h: number of h-hidden units
:param n_v: number of visible units
:param iscales: optional dictionary containing initialization scale for each parameter
:param neg_sample_steps: number of sampling updates to perform in negative phase.
:param l1: hyper-parameter controlling amount of L1 regularization
:param l2: hyper-parameter controlling amount of L2 regularization
:param batch_size: size of positive and negative phase minibatch
:param compile: compile sampling and learning functions
:param seed: seed used to initialize numpy and theano RNGs.
"""
Model.__init__(self)
Block.__init__(self)
assert lr is not None
for k in ['Wv', 'vbias', 'hbias']:
assert k in iscales.keys()
iscales.setdefault('mu', 1.)
iscales.setdefault('alpha', 0.)
iscales.setdefault('beta', 0.)
for k in ['h']:
assert k in sp_weight.keys()
for k in ['h']:
assert k in sp_targ.keys()
self.jobman_channel = None
self.jobman_state = {}
self.register_names_to_del(['jobman_channel'])
### make sure all parameters are floatX ###
for (k, v) in l1.iteritems():
l1[k] = npy_floatX(v)
for (k, v) in l2.iteritems():
l2[k] = npy_floatX(v)
for (k, v) in sp_weight.iteritems():
sp_weight[k] = npy_floatX(v)
for (k, v) in sp_targ.iteritems():
sp_targ[k] = npy_floatX(v)
for (k, v) in clip_min.iteritems():
clip_min[k] = npy_floatX(v)
for (k, v) in clip_max.iteritems():
clip_max[k] = npy_floatX(v)
# dump initialization parameters to object
for (k, v) in locals().iteritems():
if k != 'self': setattr(self, k, v)
# allocate random number generators
self.rng = numpy.random.RandomState(
seed) if numpy_rng is None else numpy_rng
self.theano_rng = RandomStreams(self.rng.randint(
2**30)) if theano_rng is None else theano_rng
############### ALLOCATE PARAMETERS #################
self.n_s = self.n_h * self.bw_s
self.wv_norms = sharedX(1.0 * numpy.ones(self.n_s), name='wv_norms')
if Wv is None:
wv_val = self.rng.randn(n_v, self.n_s) * iscales['Wv']
self.Wv = sharedX(wv_val, name='Wv')
else:
self.Wv = Wv
self.Wh = numpy.zeros((self.n_s, self.n_h), dtype=floatX)
for i in xrange(self.n_h):
self.Wh[i * bw_s:(i + 1) * bw_s, i] = 1.
# allocate shared variables for bias parameters
if hbias is None:
self.hbias = sharedX(iscales['hbias'] * numpy.ones(n_h),
name='hbias')
else:
self.hbias = hbias
# mean (mu) and precision (alpha) parameters on s
self.mu = sharedX(iscales['mu'] * numpy.ones(self.n_s), name='mu')
self.alpha = sharedX(iscales['alpha'] * numpy.ones(self.n_s),
name='alpha')
var_param_func = {
'exp': T.exp,
'softplus': T.nnet.softplus,
'linear': lambda x: x
}
self.alpha_prec = var_param_func[self.var_param_alpha](self.alpha)
# diagonal of precision matrix of visible units
self.vbound = sharedX(vbound, name='vbound')
self.beta = sharedX(iscales['beta'] * numpy.ones(n_v), name='beta')
self.beta_prec = var_param_func[self.var_param_beta](self.beta)
# allocate shared variable for persistent chain
self.neg_v = sharedX(self.rng.rand(batch_size, n_v), name='neg_v')
self.neg_ev = sharedX(self.rng.rand(batch_size, n_v), name='neg_ev')
self.neg_s = sharedX(self.rng.rand(batch_size, self.n_s), name='neg_s')
self.neg_h = sharedX(self.rng.rand(batch_size, n_h), name='neg_h')
# moving average values for sparsity
self.sp_pos_v = sharedX(self.rng.rand(1, self.n_v), name='sp_pos_v')
self.sp_pos_h = sharedX(self.rng.rand(1, self.n_h), name='sp_pog_h')
# learning rate, with deferred 1./t annealing
self.iter = sharedX(0.0, name='iter')
if lr['type'] == 'anneal':
num = lr['init'] * lr['start']
denum = T.maximum(lr['start'], lr['slope'] * self.iter)
self.lr = T.maximum(lr['floor'], num / denum)
elif lr['type'] == 'linear':
lr_start = npy_floatX(lr['start'])
lr_end = npy_floatX(lr['end'])
self.lr = lr_start + self.iter * (lr_end - lr_start) / npy_floatX(
self.max_updates)
else:
raise ValueError('Incorrect value for lr[type]')
# learning rate - implemented as shared parameter for GPU
self.lr_mults_it = {}
self.lr_mults_shrd = {}
for (k, v) in lr_mults.iteritems():
# make sure all learning rate multipliers are float64
self.lr_mults_it[k] = tools.HyperParamIterator(
lr_timestamp, lr_mults[k])
self.lr_mults_shrd[k] = sharedX(self.lr_mults_it[k].value,
name='lr_mults_shrd' + k)
# allocate symbolic variable for input
self.input = T.matrix('input') if input is None else input
# configure input-space (new pylearn2 feature?)
self.input_space = VectorSpace(n_v)
self.output_space = VectorSpace(n_h)
self.batches_seen = 0 # incremented on every batch
self.examples_seen = 0 # incremented on every training example
self.force_batch_size = batch_size # force minibatch size
self.error_record = []
if compile: self.do_theano()
#### load layer 1 parameters from file ####
if init_from:
self.load_params(init_from)
def __init__(self,
numpy_rng=None,
theano_rng=None,
n_h=99,
n_v=100,
init_from=None,
neg_sample_steps=1,
lr_spec=None,
lr_mults={},
iscales={},
clip_min={},
clip_max={},
l1={},
l2={},
sp_weight={},
sp_targ={},
batch_size=13,
compile=True,
debug=False,
seed=1241234,
my_save_path=None,
save_at=None,
save_every=None,
flags={},
max_updates=5e5):
"""
:param n_h: number of h-hidden units
:param n_v: number of visible units
:param iscales: optional dictionary containing initialization scale for each parameter
:param neg_sample_steps: number of sampling updates to perform in negative phase.
:param l1: hyper-parameter controlling amount of L1 regularization
:param l2: hyper-parameter controlling amount of L2 regularization
:param batch_size: size of positive and negative phase minibatch
:param compile: compile sampling and learning functions
:param seed: seed used to initialize numpy and theano RNGs.
"""
Model.__init__(self)
Block.__init__(self)
assert lr_spec is not None
for k in ['h']:
assert k in sp_weight.keys()
for k in ['h']:
assert k in sp_targ.keys()
self.validate_flags(flags)
self.jobman_channel = None
self.jobman_state = {}
self.register_names_to_del(['jobman_channel'])
### make sure all parameters are floatX ###
for (k, v) in l1.iteritems():
l1[k] = npy_floatX(v)
for (k, v) in l2.iteritems():
l2[k] = npy_floatX(v)
for (k, v) in sp_weight.iteritems():
sp_weight[k] = npy_floatX(v)
for (k, v) in sp_targ.iteritems():
sp_targ[k] = npy_floatX(v)
for (k, v) in clip_min.iteritems():
clip_min[k] = npy_floatX(v)
for (k, v) in clip_max.iteritems():
clip_max[k] = npy_floatX(v)
# dump initialization parameters to object
for (k, v) in locals().iteritems():
if k != 'self': setattr(self, k, v)
# allocate random number generators
self.rng = numpy.random.RandomState(
seed) if numpy_rng is None else numpy_rng
self.theano_rng = RandomStreams(self.rng.randint(
2**30)) if theano_rng is None else theano_rng
############### ALLOCATE PARAMETERS #################
# allocate symbolic variable for input
self.input = T.matrix('input')
self.init_parameters()
self.init_chains()
# learning rate, with deferred 1./t annealing
self.iter = sharedX(0.0, name='iter')
if lr_spec['type'] == 'anneal':
num = lr_spec['init'] * lr_spec['start']
denum = T.maximum(lr_spec['start'], lr_spec['slope'] * self.iter)
self.lr = T.maximum(lr_spec['floor'], num / denum)
elif lr_spec['type'] == 'linear':
lr_start = npy_floatX(lr_spec['start'])
lr_end = npy_floatX(lr_spec['end'])
self.lr = lr_start + self.iter * (lr_end - lr_start) / npy_floatX(
self.max_updates)
else:
raise ValueError('Incorrect value for lr_spec[type]')
# configure input-space (new pylearn2 feature?)
self.input_space = VectorSpace(n_v)
self.output_space = VectorSpace(n_h)
self.batches_seen = 0 # incremented on every batch
self.examples_seen = 0 # incremented on every training example
self.force_batch_size = batch_size # force minibatch size
self.error_record = []
if compile: self.do_theano()
if init_from:
raise NotImplementedError()
def perform(self, X):
# work around some awkwardness with blocks
rval = Block.perform(self, X)
if isinstance(rval, list):
rval = tuple(rval)
return rval
def perform(self, X):
# work around some awkwardness with blocks
rval = Block.perform(self, X)
if isinstance(rval, list):
rval = tuple(rval)
return rval
def __init__(self, nvis = None, nhid = None,
vis_space = None,
hid_space = None,
transformer = None,
irange=0.5, rng=None, init_bias_vis = None,
init_bias_vis_marginals = None, init_bias_hid=0.0,
base_lr = 1e-3, anneal_start = None, nchains = 100, sml_gibbs_steps = 1,
random_patches_src = None,
monitor_reconstruction = False):
"""
Construct an RBM object.
Parameters
----------
nvis : int
Number of visible units in the model.
(Specifying this implies that the model acts on a vector,
i.e. it sets vis_space = pylearn2.space.VectorSpace(nvis) )
nhid : int
Number of hidden units in the model.
(Specifying this implies that the model acts on a vector)
vis_space:
A pylearn2.space.Space object describing what kind of vector
space the RBM acts on. Don't specify if you used nvis / hid
hid_space:
A pylearn2.space.Space object describing what kind of vector
space the RBM's hidden units live in. Don't specify if you used
nvis / nhid
init_bias_vis_marginals: either None, or a Dataset to use to initialize
the visible biases to the inverse sigmoid of the data marginals
irange : float, optional
The size of the initial interval around 0 for weights.
rng : RandomState object or seed
NumPy RandomState object to use when initializing parameters
of the model, or (integer) seed to use to create one.
init_bias_vis : array_like, optional
Initial value of the visible biases, broadcasted as necessary.
init_bias_hid : array_like, optional
initial value of the hidden biases, broadcasted as necessary.
monitor_reconstruction : if True, will request a monitoring channel to monitor
reconstruction error
random_patches_src: Either None, or a Dataset from which to draw random patches
in order to initialize the weights. Patches will be multiplied by irange
Parameters for default SML learning rule:
base_lr : the base learning rate
anneal_start : number of steps after which to start annealing on a 1/t schedule
nchains: number of negative chains
sml_gibbs_steps: number of gibbs steps to take per update
"""
Model.__init__(self)
Block.__init__(self)
if init_bias_vis_marginals is not None:
assert init_bias_vis is None
X = init_bias_vis_marginals.X
assert X.min() >= 0.0
assert X.max() <= 1.0
marginals = X.mean(axis=0)
#rescale the marginals a bit to avoid NaNs
init_bias_vis = inverse_sigmoid_numpy(.01 + .98 * marginals)
if init_bias_vis is None:
init_bias_vis = 0.0
if rng is None:
# TODO: global rng configuration stuff.
rng = numpy.random.RandomState(1001)
self.rng = rng
if vis_space is None:
#if we don't specify things in terms of spaces and a transformer,
#assume dense matrix multiplication and work off of nvis, nhid
assert hid_space is None
assert transformer is None or isinstance(transformer,MatrixMul)
assert nvis is not None
assert nhid is not None
if transformer is None:
if random_patches_src is None:
W = rng.uniform(-irange, irange, (nvis, nhid))
else:
if hasattr(random_patches_src, '__array__'):
W = irange * random_patches_src.T
assert W.shape == (nvis, nhid)
else:
#assert type(irange) == type(0.01)
#assert irange == 0.01
W = irange * random_patches_src.get_batch_design(nhid).T
self.transformer = MatrixMul( sharedX(
W,
name='W',
borrow=True
)
)
else:
self.transformer = transformer
self.vis_space = VectorSpace(nvis)
self.hid_space = VectorSpace(nhid)
else:
assert hid_space is not None
assert transformer is not None
assert nvis is None
assert nhid is None
self.vis_space = vis_space
self.hid_space = hid_space
self.transformer = transformer
try:
b_vis = self.vis_space.get_origin()
b_vis += init_bias_vis
except ValueError:
raise ValueError("bad shape or value for init_bias_vis")
self.bias_vis = sharedX(b_vis, name='bias_vis', borrow=True)
try:
b_hid = self.hid_space.get_origin()
b_hid += init_bias_hid
except ValueError:
raise ValueError('bad shape or value for init_bias_hid')
self.bias_hid = sharedX(b_hid, name='bias_hid', borrow=True)
self.random_patches_src = random_patches_src
self.register_names_to_del(['random_patches_src'])
self.__dict__.update(nhid=nhid, nvis=nvis)
self._params = safe_union(self.transformer.get_params(), [self.bias_vis, self.bias_hid])
self.base_lr = base_lr
self.anneal_start = anneal_start
self.nchains = nchains
self.sml_gibbs_steps = sml_gibbs_steps
def __init__(self, nvis = None, nhid = None,
vis_space = None,
hid_space = None,
transformer = None,
irange=0.5, rng=None, init_bias_vis = None,
init_bias_vis_marginals = None, init_bias_hid=0.0,
base_lr = 1e-3, anneal_start = None, nchains = 100, sml_gibbs_steps = 1,
random_patches_src = None,
monitor_reconstruction = False):
"""
Construct an RBM object.
Parameters
----------
nvis : int
Number of visible units in the model.
(Specifying this implies that the model acts on a vector,
i.e. it sets vis_space = pylearn2.space.VectorSpace(nvis) )
nhid : int
Number of hidden units in the model.
(Specifying this implies that the model acts on a vector)
vis_space:
A pylearn2.space.Space object describing what kind of vector
space the RBM acts on. Don't specify if you used nvis / hid
hid_space:
A pylearn2.space.Space object describing what kind of vector
space the RBM's hidden units live in. Don't specify if you used
nvis / nhid
init_bias_vis_marginals: either None, or a Dataset to use to initialize
the visible biases to the inverse sigmoid of the data marginals
irange : float, optional
The size of the initial interval around 0 for weights.
rng : RandomState object or seed
NumPy RandomState object to use when initializing parameters
of the model, or (integer) seed to use to create one.
init_bias_vis : array_like, optional
Initial value of the visible biases, broadcasted as necessary.
init_bias_hid : array_like, optional
initial value of the hidden biases, broadcasted as necessary.
monitor_reconstruction : if True, will request a monitoring channel to monitor
reconstruction error
random_patches_src: Either None, or a Dataset from which to draw random patches
in order to initialize the weights. Patches will be multiplied by irange
Parameters for default SML learning rule:
base_lr : the base learning rate
anneal_start : number of steps after which to start annealing on a 1/t schedule
nchains: number of negative chains
sml_gibbs_steps: number of gibbs steps to take per update
"""
Model.__init__(self)
Block.__init__(self)
if init_bias_vis_marginals is not None:
assert init_bias_vis is None
X = init_bias_vis_marginals.X
assert X.min() >= 0.0
assert X.max() <= 1.0
marginals = X.mean(axis=0)
#rescale the marginals a bit to avoid NaNs
init_bias_vis = inverse_sigmoid_numpy(.01 + .98 * marginals)
if init_bias_vis is None:
init_bias_vis = 0.0
if rng is None:
# TODO: global rng configuration stuff.
rng = numpy.random.RandomState(1001)
self.rng = rng
if vis_space is None:
#if we don't specify things in terms of spaces and a transformer,
#assume dense matrix multiplication and work off of nvis, nhid
assert hid_space is None
assert transformer is None or isinstance(transformer,MatrixMul)
assert nvis is not None
assert nhid is not None
if transformer is None:
if random_patches_src is None:
W = rng.uniform(-irange, irange, (nvis, nhid))
else:
if hasattr(random_patches_src, '__array__'):
W = irange * random_patches_src.T
assert W.shape == (nvis, nhid)
else:
#assert type(irange) == type(0.01)
#assert irange == 0.01
W = irange * random_patches_src.get_batch_design(nhid).T
self.transformer = MatrixMul( sharedX(
W,
name='W',
borrow=True
)
)
else:
self.transformer = transformer
self.vis_space = VectorSpace(nvis)
self.hid_space = VectorSpace(nhid)
else:
assert hid_space is not None
assert transformer is not None
assert nvis is None
assert nhid is None
self.vis_space = vis_space
self.hid_space = hid_space
self.transformer = transformer
try:
b_vis = self.vis_space.get_origin()
b_vis += init_bias_vis
except ValueError:
raise ValueError("bad shape or value for init_bias_vis")
self.bias_vis = sharedX(b_vis, name='bias_vis', borrow=True)
try:
b_hid = self.hid_space.get_origin()
b_hid += init_bias_hid
except ValueError:
raise ValueError('bad shape or value for init_bias_hid')
self.bias_hid = sharedX(b_hid, name='bias_hid', borrow=True)
self.random_patches_src = random_patches_src
self.register_names_to_del(['random_patches_src'])
self.__dict__.update(nhid=nhid, nvis=nvis)
self._params = safe_union(self.transformer.get_params(), [self.bias_vis, self.bias_hid])
self.base_lr = base_lr
self.anneal_start = anneal_start
self.nchains = nchains
self.sml_gibbs_steps = sml_gibbs_steps
def __init__(self,
numpy_rng = None, theano_rng = None,
n_h=99, n_v=100, init_from=None,
min_beta=0.9, num_beta=20, gamma=10, cratio=1, cdelay=0,
neg_sample_steps=1,
lr_spec=None, lr_mults = {},
iscales={}, clip_min={}, clip_max={},
l1 = {}, l2 = {},
sp_weight={}, sp_targ={},
batch_size = 13,
compile=True, debug=False, seed=1241234,
flags = {},
max_updates = 5e5, **kwargs):
"""
:param n_h: number of h-hidden units
:param n_v: number of visible units
:param iscales: optional dictionary containing initialization scale for each parameter
:param neg_sample_steps: number of sampling updates to perform in negative phase.
:param l1: hyper-parameter controlling amount of L1 regularization
:param l2: hyper-parameter controlling amount of L2 regularization
:param batch_size: size of positive and negative phase minibatch
:param compile: compile sampling and learning functions
:param seed: seed used to initialize numpy and theano RNGs.
"""
Model.__init__(self)
Block.__init__(self)
assert lr_spec is not None
for k in ['h']: assert k in sp_weight.keys()
for k in ['h']: assert k in sp_targ.keys()
self.validate_flags(flags)
self.jobman_channel = None
self.jobman_state = {}
self.register_names_to_del(['jobman_channel'])
### make sure all parameters are floatX ###
for (k,v) in l1.iteritems(): l1[k] = npy_floatX(v)
for (k,v) in l2.iteritems(): l2[k] = npy_floatX(v)
for (k,v) in sp_weight.iteritems(): sp_weight[k] = npy_floatX(v)
for (k,v) in sp_targ.iteritems(): sp_targ[k] = npy_floatX(v)
for (k,v) in clip_min.iteritems(): clip_min[k] = npy_floatX(v)
for (k,v) in clip_max.iteritems(): clip_max[k] = npy_floatX(v)
# dump initialization parameters to object
for (k,v) in locals().iteritems():
if k!='self': setattr(self,k,v)
# allocate random number generators
self.rng = numpy.random.RandomState(seed) if numpy_rng is None else numpy_rng
self.theano_rng = RandomStreams(self.rng.randint(2**30)) if theano_rng is None else theano_rng
############### ALLOCATE PARAMETERS #################
# allocate symbolic variable for input
self.input = T.matrix('input')
self.init_parameters()
self.init_chains()
# learning rate, with deferred 1./t annealing
self.iter = sharedX(0.0, name='iter')
if lr_spec['type'] == 'anneal':
num = lr_spec['init'] * lr_spec['start']
denum = T.maximum(lr_spec['start'], lr_spec['slope'] * self.iter)
self.lr = T.maximum(lr_spec['floor'], num/denum)
elif lr_spec['type'] == '1_t':
self.lr = npy_floatX(lr_spec['num']) / (self.iter + npy_floatX(lr_spec['denum']))
elif lr_spec['type'] == 'linear':
lr_start = npy_floatX(lr_spec['start'])
lr_end = npy_floatX(lr_spec['end'])
self.lr = lr_start + self.iter * (lr_end - lr_start) / npy_floatX(self.max_updates)
elif lr_spec['type'] == 'constant':
self.lr = sharedX(lr_spec['value'], name='lr')
else:
raise ValueError('Incorrect value for lr_spec[type]')
# configure input-space (new pylearn2 feature?)
self.input_space = VectorSpace(n_v)
self.output_space = VectorSpace(n_h)
self.batches_seen = 0 # incremented on every batch
self.examples_seen = 0 # incremented on every training example
self.logz = sharedX(0.0, name='logz')
self.cpu_time = 0
self.error_record = []
if compile: self.do_theano()
if init_from:
raise NotImplementedError()
def __init__(self, input = None, n_u=[100,100], enable={}, load_from=None,
iscales=None, clip_min={}, clip_max={},
pos_mf_steps=1, pos_sample_steps=0, neg_sample_steps=1,
lr_spec={}, lr_mults = {},
l1 = {}, l2 = {}, l1_inf={}, flags={}, momentum_lambda=0,
cg_params = {},
batch_size = 13,
computational_bs = 0,
compile=True,
seed=1241234,
sp_targ_h = None, sp_weight_h=None, sp_pos_k = 5,
my_save_path=None, save_at=None, save_every=None,
max_updates=1e6):
"""
:param n_u: list, containing number of units per layer. n_u[0] contains number
of visible units, while n_u[i] (with i > 0) contains number of hid. units at layer i.
:param enable: dictionary of flags with on/off behavior
:param iscales: optional dictionary containing initialization scale for each parameter.
Key of dictionary should match the name of the associated shared variable.
:param pos_mf_steps: number of mean-field iterations to perform in positive phase
:param neg_sample_steps: number of sampling updates to perform in negative phase.
:param lr: base learning rate
:param lr_timestamp: list containing update indices at which to change the lr multiplier
:param lr_mults: dictionary, optionally containing a list of learning rate multipliers
for parameters of the model. Length of this list should match length of
lr_timestamp (the lr mult will transition whenever we reach the associated
timestamp). Keys should match the name of the shared variable, whose learning
rate is to be adjusted.
:param l1: dictionary, whose keys are model parameter names, and values are
hyper-parameters controlling degree of L1-regularization.
:param l2: same as l1, but for L2 regularization.
:param l1_inf: same as l1, but the L1 penalty is centered as -\infty instead of 0.
:param cg_params: dictionary with keys ['rtol','damp','maxiter']
:param batch_size: size of positive and negative phase minibatch
:param computational_bs: batch size used internaly by natural
gradient to reduce memory consumption
:param seed: seed used to initialize numpy and theano RNGs.
:param my_save_path: if None, do not save model. Otherwise, contains stem of filename
to which we will save the model (everything but the extension).
:param save_at: list containing iteration counts at which to save model
:param save_every: scalar value. Save model every `save_every` iterations.
"""
Model.__init__(self)
Block.__init__(self)
### VALIDATE PARAMETERS AND SET DEFAULT VALUES ###
assert lr_spec is not None
for (k,v) in clip_min.iteritems(): clip_min[k] = npy_floatX(v)
for (k,v) in clip_max.iteritems(): clip_max[k] = npy_floatX(v)
[iscales.setdefault('bias%i' % i, 0.) for i in xrange(len(n_u))]
[iscales.setdefault('W%i' % i, 0.1) for i in xrange(len(n_u))]
flags.setdefault('enable_centering', False)
flags.setdefault('enable_natural', False)
flags.setdefault('enable_warm_start', False)
flags.setdefault('mlbiases', False)
flags.setdefault('precondition', None)
flags.setdefault('minres', False)
flags.setdefault('minresQLP', False)
if flags['precondition'] == 'None': flags['precondition'] = None
self.jobman_channel = None
self.jobman_state = {}
self.register_names_to_del(['jobman_channel'])
### DUMP INITIALIZATION PARAMETERS TO OBJECT ###
for (k,v) in locals().iteritems():
if k!='self': setattr(self,k,v)
assert len(n_u) > 1
self.n_v = n_u[0]
self.depth = len(n_u)
# allocate random number generators
self.rng = numpy.random.RandomState(seed)
self.theano_rng = RandomStreams(self.rng.randint(2**30))
# allocate bilinear-weight matrices
self.input = T.matrix()
self.init_parameters()
self.init_dparameters()
self.init_centering()
self.init_samples()
# learning rate, with deferred 1./t annealing
self.iter = sharedX(0.0, name='iter')
if lr_spec['type'] == 'anneal':
num = lr_spec['init'] * lr_spec['start']
denum = T.maximum(lr_spec['start'], lr_spec['slope'] * self.iter)
self.lr = T.maximum(lr_spec['floor'], num/denum)
elif lr_spec['type'] == 'linear':
lr_start = npy_floatX(lr_spec['start'])
lr_end = npy_floatX(lr_spec['end'])
self.lr = lr_start + self.iter * (lr_end - lr_start) / npy_floatX(self.max_updates)
else:
raise ValueError('Incorrect value for lr_spec[type]')
# counter for CPU-time
self.cpu_time = 0.
if load_from:
self.load_parameters(fname=load_from)
# configure input-space (?new pylearn2 feature?)
self.input_space = VectorSpace(n_u[0])
self.output_space = VectorSpace(n_u[-1])
self.batches_seen = 0 # incremented on every batch
self.examples_seen = 0 # incremented on every training example
self.force_batch_size = batch_size # force minibatch size
self.error_record = []
if compile: self.do_theano()
