def __init__(self,
window_shape,
randomize=None,
randomize_once=None,
center=None,
rng=(2013, 2, 20),
pad_randomized=0,
flip=True):
self._window_shape = tuple(window_shape)
# Defined in setup(). A dict that maps Datasets in self._randomize and
# self._randomize_once to zero-padded versions of their topological
# views.
self._original = None
self._randomize = randomize if randomize else []
self._randomize_once = randomize_once if randomize_once else []
self._center = center if center else []
self._pad_randomized = pad_randomized
self._flip = flip
if randomize is None and randomize_once is None and center is None:
warnings.warn(self.__class__.__name__ + " instantiated without "
"any dataset arguments, and therefore does nothing",
stacklevel=2)
self._rng = make_np_rng(rng, which_method="random_integers")
def __init__(self,
window_shape,
center_shape=None,
central_window_shape=None,
randomize=None,
randomize_once=None,
center=None,
rotate=True,
scale_diff=0.0,
rng=(2013, 0o2, 20),
shear=0.0,
translation=0.0,
preprocess=None):
self._window_shape = window_shape
self._center_shape = center_shape
self._central_window_shape = central_window_shape
self._randomize = randomize if randomize else []
self._randomize_once = randomize_once if randomize_once else []
self._center = center if center else []
self._rotate = rotate
self._scale_diff = scale_diff
self._shear = shear
self._translation = translation
self._preprocess = preprocess
self._rng = make_np_rng(rng, which_method="random_integers")
def make_random_conv3D(irange,
input_axes,
output_axes,
signal_shape,
filter_shape,
kernel_stride=(2, 2, 1),
pad=0,
message="",
rng=None,
partial_sum=None):
if rng is None:
rng = make_np_rng(rng, default_seed, which_method='uniform')
### b 0 1 t c
_filter_5d_shape = (filter_shape[0], filter_shape[1], filter_shape[2],
filter_shape[3], filter_shape[4])
# initialize weights
print(_filter_5d_shape)
W = sharedX(rng.uniform(-irange, irange, (_filter_5d_shape)))
print 'w is set'
return Conv3DB01TC(filters=W,
input_axes=input_axes,
output_axes=output_axes,
signal_shape=signal_shape,
filter_shape=filter_shape,
kernel_stride=kernel_stride,
pad=pad,
message=message,
partial_sum=partial_sum)
def __init__(self, which_set='debug', start=None, end=None, shuffle=True,
lazy_load=False, rng=_default_seed):
assert which_set in ['debug', 'train', 'test']
if which_set == 'debug':
maxlen, n_samples, n_annotations, n_features = 10, 12, 13, 14
X = N.zeros(shape=(n_samples, maxlen))
X_mask = X # same with X
Z = N.zeros(shape=(n_annotations, n_samples, n_features))
elif which_set == 'train':
pass
else:
pass
self.X, self.X_mask, self.Z = (X, X_mask, Z)
self.sources = ('features', 'target')
self.spaces = CompositeSpace([
SequenceSpace(space=VectorSpace(dim=self.X.shape[1])),
SequenceDataSpace(space=VectorSpace(dim=self.Z.shape[-1]))
])
self.data_spces = (self.spaces, self.sources)
# self.X_space, self.X_mask_space, self.Z_space
# Default iterator
self._iter_mode = resolve_iterator_class('sequential')
self._iter_topo = False
self._iter_target = False
self._iter_data_specs = self.data_spces
self.rng = make_np_rng(rng, which_method='random_intergers')
def __init__(self, data=None, data_specs=None, rng=_default_seed,
preprocessor=None, fit_preprocessor=False):
# data_specs should be flat, and there should be no
# duplicates in source, as we keep only one version
assert is_flat_specs(data_specs)
if isinstance(data_specs[1], tuple):
assert sorted(set(data_specs[1])) == sorted(data_specs[1])
space, source = data_specs
space.np_validate(data)
# TODO: assume that data[0] is num example => error if channel in c01b
# assert len(set(elem.shape[0] for elem in list(data))) <= 1
self.data = data
self.data_specs = data_specs
# TODO: assume that data[0] is num example => error if channel in c01b
self.num_examples = list(data)[-1].shape[0] # TODO: list(data)[0].shape[0]
self.compress = False
self.design_loc = None
self.rng = make_np_rng(rng, which_method='random_integers')
# Defaults for iterators
self._iter_mode = resolve_iterator_class('sequential')
if preprocessor:
preprocessor.apply(self, can_fit=fit_preprocessor)
self.preprocessor = preprocessor
def make_random_conv2D(irange, input_space, output_space,
kernel_shape, batch_size=None, \
subsample = (1,1), border_mode = 'valid',
message = "", rng = None):
"""
.. todo::
WRITEME properly
Creates a Conv2D with random kernels
"""
rng = make_np_rng(rng, default_seed, which_method='uniform')
W = sharedX(rng.uniform(
-irange, irange,
(output_space.num_channels, input_space.num_channels,
kernel_shape[0], kernel_shape[1])
))
return Conv2D(
filters=W,
batch_size=batch_size,
input_space=input_space,
output_axes=output_space.axes,
subsample=subsample, border_mode=border_mode,
filters_shape=W.get_value(borrow=True).shape, message=message
)
def __init__(self,
window_shape,
randomize=None,
randomize_once=None,
center=None,
rng=(2013, 2, 20),
pad_randomized=0,
flip=True):
self._window_shape = tuple(window_shape)
# Defined in setup(). A dict that maps Datasets in self._randomize and
# self._randomize_once to zero-padded versions of their topological
# views.
self._original = None
self._randomize = randomize if randomize else []
self._randomize_once = randomize_once if randomize_once else []
self._center = center if center else []
self._pad_randomized = pad_randomized
self._flip = flip
if randomize is None and randomize_once is None and center is None:
warnings.warn(self.__class__.__name__ + " instantiated without "
"any dataset arguments, and therefore does nothing",
stacklevel=2)
self._rng = make_np_rng(rng, which_method="random_integers")
def __init__(self, X, y):
if (self.dataset_name in dataset_info.aod_datasets
and self.which_set == "full"):
self.targets, self.novels = self.load_aod_gts()
assert self.targets.shape == self.novels.shape
if X.shape[0] % self.targets.shape[0] != 0:
raise ValueError("AOD data and targets seems to have "
"incompatible shapes: %r vs %r" %
(X.shape, self.targets.shape))
X = self.preprocess(X)
if self.shuffle:
logger.info("Shuffling data")
self.shuffle_rng = make_np_rng(None, [1, 2, 3],
which_method="shuffle")
for i in xrange(m):
j = self.shuffle_rng.randint(m)
tmp = X[i].copy()
X[i] = X[j]
X[j] = tmp
tmp = y[i:i + 1].copy()
y[i] = y[j]
y[j] = tmp
max_labels = np.amax(y) + 1
logger.info("%d labels found." % max_labels)
super(MRI, self).__init__(X=X,
y=y,
view_converter=self.view_converter,
y_labels=max_labels)
assert not np.any(np.isnan(self.X))
def test_convolutional_compatible():
"""
VAE allows convolutional encoding networks
"""
encoding_model = MLP(
layers=[
SpaceConverter(
layer_name='conv2d_converter',
output_space=Conv2DSpace(shape=[4, 4], num_channels=1)
),
ConvRectifiedLinear(
layer_name='h',
output_channels=2,
kernel_shape=[2, 2],
kernel_stride=[1, 1],
pool_shape=[1, 1],
pool_stride=[1, 1],
pool_type='max',
irange=0.01)
]
)
decoding_model = MLP(layers=[Linear(layer_name='h', dim=16, irange=0.01)])
prior = DiagonalGaussianPrior()
conditional = BernoulliVector(mlp=decoding_model, name='conditional')
posterior = DiagonalGaussian(mlp=encoding_model, name='posterior')
vae = VAE(nvis=16, prior=prior, conditional=conditional,
posterior=posterior, nhid=16)
X = T.matrix('X')
lower_bound = vae.log_likelihood_lower_bound(X, num_samples=10)
f = theano.function(inputs=[X], outputs=lower_bound)
rng = make_np_rng(default_seed=11223)
f(as_floatX(rng.uniform(size=(10, 16))))
def __init__(self, num_arms, mean_std=1.0, std_std=1.0):
self.rng = make_np_rng(None, [2013, 11, 12], which_method="randn")
self.means = sharedX(self.rng.randn(num_arms) * mean_std)
self.stds = sharedX(np.abs(self.rng.randn(num_arms) * std_std))
self.theano_rng = make_theano_rng(None,
self.rng.randint(2**16),
which_method="normal")
def make_weights(input_space, output_space, kernel_shape, **kwargs):
rs = make_np_rng(rng, default_seed, which_method='normal')
shape = (output_space.num_channels, input_space.num_channels,
kernel_shape[0], kernel_shape[1])
return sharedX(rs.normal(0, istd, shape))
def make_weights(input_space, output_space, kernel_shape, **kwargs):
rs = make_np_rng(rng, default_seed, which_method='uniform')
shape = (output_space.num_channels, input_space.num_channels,
kernel_shape[0], kernel_shape[1])
return sharedX(rs.uniform(-irange, irange, shape))
def _create_subset_iterator(self, mode, batch_size=None, num_batches=None,
rng=None):
subset_iterator = resolve_iterator_class(mode)
if rng is None and subset_iterator.stochastic:
rng = make_np_rng()
return subset_iterator(self.get_num_examples(), batch_size,
num_batches, rng)
def setup_rng(self):
"""
.. todo::
WRITEME
"""
self.rng = make_np_rng(None, [2012, 10, 17], which_method="uniform")
def __init__(self, X, y):
if (self.dataset_name in dataset_info.aod_datasets
and self.which_set == "full"):
self.targets, self.novels = self.load_aod_gts()
assert self.targets.shape == self.novels.shape
if X.shape[0] % self.targets.shape[0] != 0:
raise ValueError("AOD data and targets seems to have "
"incompatible shapes: %r vs %r"
% (X.shape, self.targets.shape))
X = self.preprocess(X)
if self.shuffle:
logger.info("Shuffling data")
self.shuffle_rng = make_np_rng(None, [1 ,2 ,3],
which_method="shuffle")
for i in xrange(m):
j = self.shuffle_rng.randint(m)
tmp = X[i].copy()
X[i] = X[j]
X[j] = tmp
tmp = y[i:i+1].copy()
y[i] = y[j]
y[j] = tmp
max_labels = np.amax(y) + 1
logger.info("%d labels found." % max_labels)
super(MRI, self).__init__(X=X,
y=y,
view_converter=self.view_converter,
y_labels=max_labels)
assert not np.any(np.isnan(self.X))
def split_patients(patients, valid_percent, test_percent, rng=(2014, 10, 22)):
if isinstance(rng, (list, tuple)):
rng = make_np_rng(None, rng, which_method='uniform')
vals = np.asarray(patients.values())
keys = np.asarray(patients.keys())
sss = StratifiedShuffleSplit(
vals, n_iter=1, test_size=test_percent, random_state=rng)
remaining_idx, test_idx = sss.__iter__().next()
if valid_percent > 0:
# Rate of samples required to build validation set
valid_rate = valid_percent / (1 - test_percent)
sss = StratifiedShuffleSplit(
vals[remaining_idx], n_iter=1, test_size=valid_rate, random_state=rng)
tr_idx, val_idx = sss.__iter__().next()
valid_idx = remaining_idx[val_idx]
train_idx = remaining_idx[tr_idx]
else:
train_idx = remaining_idx
valid_idx = []
train_patients = dict(zip(keys[train_idx], vals[train_idx]))
valid_patients = dict(zip(keys[valid_idx], vals[valid_idx]))
test_patients = dict(zip(keys[test_idx], vals[test_idx]))
return train_patients, valid_patients, test_patients
def __init__(self,
cost=None,
batch_size=None,
batches_per_iter=None,
updates_per_batch=10,
monitoring_batch_size=None,
monitoring_batches=None,
monitoring_dataset=None,
termination_criterion=None,
set_batch_size=False,
reset_alpha=True,
conjugate=False,
min_init_alpha=.001,
reset_conjugate=True,
line_search_mode=None,
verbose_optimization=False,
scale_step=1.,
theano_function_mode=None,
init_alpha=None,
seed=None):
self.__dict__.update(locals())
del self.self
if monitoring_dataset is None:
assert monitoring_batches is None
assert monitoring_batch_size is None
self._set_monitoring_dataset(monitoring_dataset)
self.bSetup = False
self.termination_criterion = termination_criterion
self.rng = make_np_rng(seed, [2012, 10, 16],
which_method=["randn", "randint"])
def __init__(self, dataset_size, batch_size, num_batches=None, rng=None):
self._rng = make_np_rng(rng,
which_method=["random_integers", "shuffle"])
assert num_batches is None or num_batches >= 0
self._dataset_size = dataset_size
if batch_size is None:
if num_batches is not None:
batch_size = int(np.ceil(self._dataset_size / num_batches))
else:
raise ValueError("need one of batch_size, num_batches "
"for sequential batch iteration")
elif batch_size is not None:
if num_batches is not None:
max_num_batches = np.ceil(self._dataset_size / batch_size)
if num_batches > max_num_batches:
raise ValueError("dataset of %d examples can only provide "
"%d batches with batch_size %d, but %d "
"batches were requested" %
(self._dataset_size, max_num_batches,
batch_size, num_batches))
else:
num_batches = np.ceil(self._dataset_size / batch_size)
self._batch_size = batch_size
self._num_batches = int(num_batches)
self._next_batch_no = 0
self._idx = 0
self._batch_order = list(range(self._num_batches))
self._rng.shuffle(self._batch_order)
def __init__(self, dataset_size, batch_size, num_batches=None, rng=None):
self._rng = make_np_rng(rng, which_method=["random_integers",
"shuffle"])
assert num_batches is None or num_batches >= 0
self._dataset_size = dataset_size
if batch_size is None:
if num_batches is not None:
batch_size = int(np.ceil(self._dataset_size / num_batches))
else:
raise ValueError("need one of batch_size, num_batches "
"for sequential batch iteration")
elif batch_size is not None:
if num_batches is not None:
max_num_batches = np.ceil(self._dataset_size / batch_size)
if num_batches > max_num_batches:
raise ValueError("dataset of %d examples can only provide "
"%d batches with batch_size %d, but %d "
"batches were requested" %
(self._dataset_size, max_num_batches,
batch_size, num_batches))
else:
num_batches = np.ceil(self._dataset_size / batch_size)
self._batch_size = batch_size
self._num_batches = int(num_batches)
self._next_batch_no = 0
self._idx = 0
self._batch_order = list(range(self._num_batches))
self._rng.shuffle(self._batch_order)
def __init__(self, dataset_size, batch_size, num_batches, rng=None):
super(ShuffledSequentialSubsetIterator,
self).__init__(dataset_size, batch_size, num_batches, None)
self._rng = make_np_rng(rng,
which_method=["random_integers", "shuffle"])
self._shuffled = np.arange(self._dataset_size)
self._rng.shuffle(self._shuffled)
def make_sparse_random_local(num_nonzero, input_space, output_space,
kernel_shape, batch_size, \
kernel_stride = (1,1), border_mode = 'valid', message = "", rng=None):
"""
.. todo::
WRITEME
"""
raise NotImplementedError("Not yet modified after copy-paste from "
"pylearn2.linear.conv2d_c01b")
""" Creates a Conv2D with random kernels, where the randomly initialized
values are sparse"""
rng = make_np_rng(rng,
default_sparse_seed,
which_method=['randn', 'randint'])
W = np.zeros(( output_space.num_channels, input_space.num_channels, \
kernel_shape[0], kernel_shape[1]))
def random_coord():
return [rng.randint(dim) for dim in W.shape]
for i in xrange(num_nonzero):
o, ch, r, c = random_coord()
while W[o, ch, r, c] != 0:
o, ch, r, c = random_coord()
W[o, ch, r, c] = rng.randn()
W = sharedX(W)
def setup_rng(self):
"""
.. todo::
WRITEME
"""
self.rng = make_np_rng(None, [2012, 10, 17], which_method="uniform")
def __init__(self, nvis, nhid, init_lambda, init_p, init_alpha,
learning_rate):
"""
.. todo::
WRITEME
"""
self.nvis = int(nvis)
self.nhid = int(nhid)
self.init_lambda = float(init_lambda)
self.init_p = float(init_p)
self.init_alpha = N.cast[config.floatX](init_alpha)
self.tol = 1e-6
self.time_constant = 1e-2
self.learning_rate = N.cast[config.floatX](learning_rate)
self.predictor_learning_rate = self.learning_rate
self.rng = make_np_rng(None, [1, 2, 3], which_method="randn")
self.error_record = []
self.ERROR_RECORD_MODE_MONITORING = 0
self.error_record_mode = self.ERROR_RECORD_MODE_MONITORING
self.instrumented = False
self.redo_everything()
def test_convolutional_compatible():
"""
VAE allows convolutional encoding networks
"""
encoding_model = MLP(
layers=[
SpaceConverter(layer_name="conv2d_converter", output_space=Conv2DSpace(shape=[4, 4], num_channels=1)),
ConvRectifiedLinear(
layer_name="h",
output_channels=2,
kernel_shape=[2, 2],
kernel_stride=[1, 1],
pool_shape=[1, 1],
pool_stride=[1, 1],
pool_type="max",
irange=0.01,
),
]
)
decoding_model = MLP(layers=[Linear(layer_name="h", dim=16, irange=0.01)])
prior = DiagonalGaussianPrior()
conditional = BernoulliVector(mlp=decoding_model, name="conditional")
posterior = DiagonalGaussian(mlp=encoding_model, name="posterior")
vae = VAE(nvis=16, prior=prior, conditional=conditional, posterior=posterior, nhid=16)
X = T.matrix("X")
lower_bound = vae.log_likelihood_lower_bound(X, num_samples=10)
f = theano.function(inputs=[X], outputs=lower_bound)
rng = make_np_rng(default_seed=11223)
f(as_floatX(rng.uniform(size=(10, 16))))
def make_random_conv2D(irange,
input_channels,
input_axes,
output_axes,
output_channels,
kernel_shape,
kernel_stride=(1, 1),
pad=0,
message="",
rng=None,
partial_sum=None,
sparse_init=None):
"""
.. todo::
WRITEME properly
Creates a Conv2D with random kernels.
Should be functionally equivalent to
pylearn2.linear.conv2d.make_random_conv2D
"""
rng = make_np_rng(rng, default_seed, which_method='uniform')
W = sharedX( rng.uniform(-irange,irange,(input_channels, \
kernel_shape[0], kernel_shape[1], output_channels)))
return Conv2D(filters=W,
input_axes=input_axes,
output_axes=output_axes,
kernel_stride=kernel_stride,
pad=pad,
message=message,
partial_sum=partial_sum)
def __init__(self, nvis, nhid, coeff):
self.nvis = nvis
self.nhid = nhid
self.coeff = float(coeff)
self.rng = make_np_rng(None, [1, 2, 3], which_method="randn")
self.redo_everything()
def reset_rng(self):
"""
.. todo::
WRITEME
"""
self.rng = make_np_rng([1,2,3], which_method=['randn','uniform'])
def __init__(self, min_x=-6.28, max_x=6.28, std=.05, rng=_default_seed):
"""
Constructor.
"""
super(CosDataset, self).__init__()
#: lower limit for x as in cos(x)
self.min_x = min_x
#: higher limit for x as in cos(x)
self.max_x = max_x
#: standard deviation for the noise added to the values we generate
self.std = std
# argument to resolve_iterator_class() can be either
# a string from [sequential, shuffled_sequential, random_slice,
# random_uniform, batchwise_shuffled_sequential, even_sequential,
# even_shuffled_sequential, even_batchwise_shuffled_sequential,
# even_sequences] or a SubsetIterator sublass.
#: default iterator implementation (a class to be instantiated)
self._iter_subset_class = resolve_iterator_class('sequential')
#: default data specifications for iterator
self._iter_data_specs = (VectorSpace(2), 'features')
#: default batch size for the iterator
self._iter_batch_size = 100
#: default number of batches for the iterator
self._iter_num_batches = 10
#: random number generator
self.rng = make_np_rng(rng, which_method=['uniform', 'randn'])
def __init__(
self,
cost=None,
batch_size=None,
batches_per_iter=None,
updates_per_batch=10,
monitoring_batches=None,
monitoring_dataset=None,
termination_criterion=None,
set_batch_size=False,
reset_alpha=True,
conjugate=False,
min_init_alpha=0.001,
reset_conjugate=True,
line_search_mode=None,
verbose_optimization=False,
scale_step=1.0,
theano_function_mode=None,
init_alpha=None,
seed=None,
):
self.__dict__.update(locals())
del self.self
if monitoring_dataset is None:
assert monitoring_batches == None
self._set_monitoring_dataset(monitoring_dataset)
self.bSetup = False
self.termination_criterion = termination_criterion
self.rng = make_np_rng(seed, [2012, 10, 16], which_method=["randn", "randint"])
def make_sparse_random_conv2D(num_nonzero, input_space, output_space,
kernel_shape, pad=0, kernel_stride=(1, 1),
border_mode='valid', message="", rng=None,
partial_sum=None):
"""
.. todo::
WRITEME properly
Creates a Conv2D with random kernels, where the randomly initialized
values are sparse
"""
rng = make_np_rng(rng, default_sparse_seed,
which_method=['randn', 'randint'])
W = np.zeros((input_space.num_channels, kernel_shape[0],
kernel_shape[1], output_space.num_channels))
def random_coord():
return [rng.randint(dim) for dim in W.shape[0:3]]
for o in xrange(output_space.num_channels):
for i in xrange(num_nonzero):
ch, r, c = random_coord()
while W[ch, r, c, o] != 0:
ch, r, c = random_coord()
W[ch, r, c, o] = rng.randn()
W = sharedX(W)
return Conv2D(filters=W, input_axes=input_space.axes,
output_axes=output_space.axes, kernel_stride=kernel_stride,
pad=pad, message=message, partial_sum=partial_sum)
def make_sparse_random_conv2D(num_nonzero, input_space, output_space,
kernel_shape, pad=0, kernel_stride=(1, 1),
border_mode='valid', message="", rng=None,
partial_sum=None):
"""
.. todo::
WRITEME properly
Creates a Conv2D with random kernels, where the randomly initialized
values are sparse
"""
rng = make_np_rng(rng, default_sparse_seed,
which_method=['randn', 'randint'])
W = np.zeros((input_space.num_channels, kernel_shape[0],
kernel_shape[1], output_space.num_channels))
def random_coord():
return [rng.randint(dim) for dim in W.shape[0:3]]
for o in xrange(output_space.num_channels):
for i in xrange(num_nonzero):
ch, r, c = random_coord()
while W[ch, r, c, o] != 0:
ch, r, c = random_coord()
W[ch, r, c, o] = rng.randn()
W = sharedX(W)
return Conv2D(filters=W, input_axes=input_space.axes,
output_axes=output_space.axes, kernel_stride=kernel_stride,
pad=pad, message=message, partial_sum=partial_sum)
def __init__(self, nvis, nhid, coeff):
self.nvis = nvis
self.nhid = nhid
self.coeff = float(coeff)
self.rng = make_np_rng(None, [1, 2, 3], which_method="randn")
self.redo_everything()
def _create_subset_iterator(self, mode, batch_size=None, num_batches=None,
rng=None):
subset_iterator = resolve_iterator_class(mode)
if rng is None and subset_iterator.stochastic:
rng = make_np_rng()
return subset_iterator(self.get_num_examples(), batch_size,
num_batches, rng)
def __init__(self, nvis, nhid,
init_lambda,
init_p, init_alpha, learning_rate):
"""
.. todo::
WRITEME
"""
self.nvis = int(nvis)
self.nhid = int(nhid)
self.init_lambda = float(init_lambda)
self.init_p = float(init_p)
self.init_alpha = N.cast[config.floatX](init_alpha)
self.tol = 1e-6
self.time_constant = 1e-2
self.learning_rate = N.cast[config.floatX](learning_rate)
self.predictor_learning_rate = self.learning_rate
self.rng = make_np_rng(None, [1,2,3], which_method="randn")
self.error_record = []
self.ERROR_RECORD_MODE_MONITORING = 0
self.error_record_mode = self.ERROR_RECORD_MODE_MONITORING
self.instrumented = False
self.redo_everything()
def __init__(self,
data=None,
data_specs=None,
rng=_default_seed,
preprocessor=None,
fit_preprocessor=False):
# data_specs should be flat, and there should be no
# duplicates in source, as we keep only one version
assert is_flat_specs(data_specs)
if isinstance(data_specs[1], tuple):
assert sorted(set(data_specs[1])) == sorted(data_specs[1])
space, source = data_specs
space.np_validate(data)
assert len(set(elem.shape[0] for elem in list(data))) <= 1
self.data = data
self.data_specs = data_specs
self.num_examples = list(data)[0].shape[0]
self.compress = False
self.design_loc = None
self.rng = make_np_rng(rng, which_method='random_integers')
# Defaults for iterators
self._iter_mode = resolve_iterator_class('sequential')
if preprocessor:
preprocessor.apply(self, can_fit=fit_preprocessor)
self.preprocessor = preprocessor
def make_random_conv2D(irange, input_channels, input_axes, output_axes,
output_channels,
kernel_shape,
kernel_stride = (1,1), pad=0, message = "", rng = None,
partial_sum = None, sparse_init = None):
"""
.. todo::
WRITEME properly
Creates a Conv2D with random kernels.
Should be functionally equivalent to
pylearn2.linear.conv2d.make_random_conv2D
"""
rng = make_np_rng(rng, default_seed, which_method='uniform')
W = sharedX( rng.uniform(-irange,irange,(input_channels, \
kernel_shape[0], kernel_shape[1], output_channels)))
return Conv2D(filters = W,
input_axes = input_axes,
output_axes = output_axes,
kernel_stride = kernel_stride, pad=pad,
message = message, partial_sum=partial_sum)
def make_sparse_random_local(num_nonzero, input_space, output_space,
kernel_shape, batch_size, \
kernel_stride = (1,1), border_mode = 'valid', message = "", rng=None):
"""
.. todo::
WRITEME
"""
raise NotImplementedError("Not yet modified after copy-paste from "
"pylearn2.linear.conv2d_c01b")
""" Creates a Conv2D with random kernels, where the randomly initialized
values are sparse"""
rng = make_np_rng(rng, default_sparse_seed, which_method=['randn','randint'])
W = np.zeros(( output_space.num_channels, input_space.num_channels, \
kernel_shape[0], kernel_shape[1]))
def random_coord():
return [ rng.randint(dim) for dim in W.shape ]
for i in xrange(num_nonzero):
o, ch, r, c = random_coord()
while W[o, ch, r, c] != 0:
o, ch, r, c = random_coord()
W[o, ch, r, c] = rng.randn()
W = sharedX( W)
def make_random_conv3D(irange, input_axes, output_axes,
signal_shape,
filter_shape,
kernel_stride = (1,1), pad=0, message = "", rng = None,
partial_sum = None):
if rng is None:
rng = make_np_rng(rng, default_seed, which_method='uniform')
_filter_5d_shape = (
filter_shape[0],
filter_shape[1],
filter_shape[2],
filter_shape[3],filter_shape[4])
# initialize weights
W = sharedX(rng.uniform(-irange,irange,(_filter_5d_shape)))
return Conv3DBCT01(filters = W,
input_axes = input_axes,
output_axes = output_axes,
signal_shape = signal_shape,
filter_shape = filter_shape,
kernel_stride = kernel_stride, pad=pad,
message = message, partial_sum=partial_sum)
def make_random_conv2D(irange, input_space, output_space,
kernel_shape, batch_size=None, \
subsample = (1,1), border_mode = 'valid',
message = "", rng = None):
"""
.. todo::
WRITEME properly
Creates a Conv2D with random kernels
"""
rng = make_np_rng(rng, default_seed, which_method='uniform')
W = sharedX(rng.uniform(
-irange, irange,
(output_space.num_channels, input_space.num_channels,
kernel_shape[0], kernel_shape[1])
))
return Conv2D(
filters=W,
batch_size=batch_size,
input_space=input_space,
output_axes=output_space.axes,
subsample=subsample, border_mode=border_mode,
filters_shape=W.get_value(borrow=True).shape, message=message
)
def build_stacked_ae(nvis,
nhids,
act_enc,
act_dec,
tied_weights=False,
irange=1e-3,
rng=None,
corruptor=None,
contracting=False):
"""
.. todo::
WRITEME properly
Allocate a stack of autoencoders.
"""
rng = make_np_rng(rng, which_method='randn')
layers = []
final = {}
# "Broadcast" arguments if they are singular, or accept sequences if
# they are the same length as nhids
for c in [
'corruptor', 'contracting', 'act_enc', 'act_dec', 'tied_weights',
'irange'
]:
if type(locals()[c]) is not str and hasattr(locals()[c], '__len__'):
assert len(nhids) == len(locals()[c])
final[c] = locals()[c]
else:
final[c] = [locals()[c]] * len(nhids)
# The number of visible units in each layer is the initial input
# size and the first k-1 hidden unit sizes.
nviss = [nvis] + nhids[:-1]
seq = izip(
nhids,
nviss,
final['act_enc'],
final['act_dec'],
final['corruptor'],
final['contracting'],
final['tied_weights'],
final['irange'],
)
# Create each layer.
for (nhid, nvis, act_enc, act_dec, corr, cae, tied, ir) in seq:
args = (nvis, nhid, act_enc, act_dec, tied, ir, rng)
if cae and corr is not None:
raise ValueError("Can't specify denoising and contracting "
"objectives simultaneously")
elif cae:
autoenc = ContractiveAutoencoder(*args)
elif corr is not None:
autoenc = DenoisingAutoencoder(corr, *args)
else:
autoenc = Autoencoder(*args)
layers.append(autoenc)
# Create the stack
return StackedBlocks(layers)
def spatiotemporal_cubes(file_tuples, shape, n_patches=numpy.inf, rng=None):
"""
Generator function that yields a stream of (filename, slicetuple)
representing a spatiotemporal patch of that file.
Parameters
----------
file_tuples : list of tuples
Each element should be a 2-tuple consisting of a filename
(or arbitrary identifier) and a (length, height, width)
shape tuple of the dimensions (number of frames in the video,
height and width of each frame).
shape : tuple
A shape tuple consisting of the desired (length, height, width)
of each spatiotemporal patch.
n_patches : int, optional
The number of patches to generate. By default, generates patches
infinitely.
rng : RandomState object or seed, optional
The random number generator (or seed) to use. Defaults to None,
meaning it will be seeded from /dev/urandom or the clock.
Returns
-------
generator : generator object
A generator that yields a stream of (filename, slicetuple) tuples.
The slice tuple is such that it indexes into a 3D array containing
the entire clip with frames indexed along the first axis, rows
along the second and columns along the third.
"""
frame_lookup = FrameLookup([(a, b[0]) for a, b in file_tuples])
file_lookup = OrderedDict(file_tuples)
patch_length, patch_height, patch_width = shape
done = 0
rng = make_np_rng(rng, which_method="random_integers")
while done < n_patches:
frame = rng.random_integers(0, len(frame_lookup) - 1)
filename, file_length, frame_no = frame_lookup[frame]
# Check that there is a contiguous block of frames starting at
# frame_no that is at least as long as our desired cube length.
if file_length - frame_no < patch_length:
continue
_, video_height, video_width = file_lookup[filename][:3]
# The last row and column in which a patch could "start" to still
# fall within frame.
last_row = video_height - patch_height
last_col = video_width - patch_width
row = rng.random_integers(0, last_row)
col = rng.random_integers(0, last_col)
patch_slice = (
slice(frame_no, frame_no + patch_length),
slice(row, row + patch_height),
slice(col, col + patch_width),
)
done += 1
yield filename, patch_slice
def __init__(
self,
learning_rate,
cost=None,
batch_size=None,
monitoring_batches=None,
monitoring_dataset=None,
monitor_iteration_mode="sequential",
termination_criterion=None,
update_callbacks=None,
learning_rule=None,
init_momentum=None,
set_batch_size=False,
train_iteration_mode=None,
batches_per_iter=None,
theano_function_mode=None,
monitoring_costs=None,
seed=[2012, 10, 5],
):
if isinstance(cost, (list, tuple, set)):
raise TypeError(
"SGD no longer supports using collections of "
+ "Costs to represent a sum of Costs. Use "
+ "pylearn2.costs.cost.SumOfCosts instead."
)
if init_momentum:
warnings.warn(
"init_momentum interface is deprecated and will "
"become officially unsuported as of May 9, 2014. Please use the "
"`learning_rule` parameter instead, providing an object of type "
"`pylearn2.training_algorithms.learning_rule.Momentum` instead"
)
# Convert to new interface under the hood.
self.learning_rule = Momentum(init_momentum)
else:
self.learning_rule = learning_rule
self.learning_rate = sharedX(learning_rate, "learning_rate")
self.cost = cost
self.batch_size = batch_size
self.set_batch_size = set_batch_size
self.batches_per_iter = batches_per_iter
self._set_monitoring_dataset(monitoring_dataset)
self.monitoring_batches = monitoring_batches
self.monitor_iteration_mode = monitor_iteration_mode
if monitoring_dataset is None:
if monitoring_batches is not None:
raise ValueError("Specified an amount of monitoring batches " + "but not a monitoring dataset.")
self.termination_criterion = termination_criterion
self._register_update_callbacks(update_callbacks)
if train_iteration_mode is None:
train_iteration_mode = "shuffled_sequential"
self.train_iteration_mode = train_iteration_mode
self.first = True
self.rng = make_np_rng(seed, which_method=["randn", "randint"])
self.theano_function_mode = theano_function_mode
self.monitoring_costs = monitoring_costs
def __call__(self):
print('loading model')
model_path = self.model_path
self.model = serial.load(model_path)
self.model.set_dtype('float32')
input_space = self.model.get_input_space()
#This code all assumes the model works on vectors
assert isinstance(input_space, VectorSpace)
nvis = input_space.dim
self.size = int(np.sqrt(nvis/3))
rng = make_np_rng(None, [1,2,3], which_method="randint")
#Generate the random pooling structure
num_filters = self.model.mu.get_value().shape[0]
idxs = rng.randint(0,num_filters,(self.num_output_features,))
top = idxs.copy()
bottom = idxs.copy()
left = idxs.copy()
right = idxs.copy()
for i in xrange(self.num_output_features):
top[i] = rng.randint(num_superpixels)
bottom[i] = rng.randint(top[i],num_superpixels)
left[i] = rng.randint(num_superpixels)
right[i] = rng.randint(left[i],num_superpixels)
#output[i] will pool over detector feature map i
self.idxs = idxs
#output [i] will pool over a rectangle with upper left coordinates (top[i],left[i])
#and lower right coordinates (bottom[i],right[i])
self.top = top
self.bottom = bottom
self.left = left
self.right = right
#Run the experiment
if self.chunk_size is not None:
dataset_family = self.dataset_family
which_set = self.which_set
dataset_descriptor = self.dataset_family[which_set][size]
num_examples = dataset_descriptor.num_examples
assert num_examples % self.chunk_size == 0
self.chunk_id = 0
for i in xrange(0,num_examples, self.chunk_size):
self.restrict = (i, i + self.chunk_size)
self._execute()
self.chunk_id += 1
else:
self._execute()
def reset_RNG(self):
"""
Restore the default seed of the rng used for choosing random
examples.
"""
if "default_rng" not in dir(self):
self.default_rng = make_np_rng(None, [17, 2, 946], which_method="random_integers")
self.rng = copy.copy(self.default_rng)
def spatiotemporal_cubes(file_tuples, shape, n_patches=numpy.inf, rng=None):
"""
Generator function that yields a stream of (filename, slicetuple)
representing a spatiotemporal patch of that file.
Parameters
----------
file_tuples : list of tuples
Each element should be a 2-tuple consisting of a filename
(or arbitrary identifier) and a (length, height, width)
shape tuple of the dimensions (number of frames in the video,
height and width of each frame).
shape : tuple
A shape tuple consisting of the desired (length, height, width)
of each spatiotemporal patch.
n_patches : int, optional
The number of patches to generate. By default, generates patches
infinitely.
rng : RandomState object or seed, optional
The random number generator (or seed) to use. Defaults to None,
meaning it will be seeded from /dev/urandom or the clock.
Returns
-------
generator : generator object
A generator that yields a stream of (filename, slicetuple) tuples.
The slice tuple is such that it indexes into a 3D array containing
the entire clip with frames indexed along the first axis, rows
along the second and columns along the third.
"""
frame_lookup = FrameLookup([(a, b[0]) for a, b in file_tuples])
file_lookup = OrderedDict(file_tuples)
patch_length, patch_height, patch_width = shape
done = 0
rng = make_np_rng(rng, which_method="random_integers")
while done < n_patches:
frame = rng.random_integers(0, len(frame_lookup) - 1)
filename, file_length, frame_no = frame_lookup[frame]
# Check that there is a contiguous block of frames starting at
# frame_no that is at least as long as our desired cube length.
if file_length - frame_no < patch_length:
continue
_, video_height, video_width = file_lookup[filename][:3]
# The last row and column in which a patch could "start" to still
# fall within frame.
last_row = video_height - patch_height
last_col = video_width - patch_width
row = numpy.random.random_integers(0, last_row)
col = numpy.random.random_integers(0, last_col)
patch_slice = (slice(frame_no, frame_no + patch_length),
slice(row, row + patch_height),
slice(col, col + patch_width))
done += 1
yield filename, patch_slice
def __init__(self, nvis, nhid, act_enc, act_dec, tied_weights=False, irange=1e-3, rng=9001):
super(Autoencoder, self).__init__()
assert nvis > 0, "Number of visible units must be non-negative"
assert nhid > 0, "Number of hidden units must be positive"
self.input_space = VectorSpace(nvis)
self.output_space = VectorSpace(nhid)
# Save a few parameters needed for resizing
self.nhid = nhid
self.irange = irange
self.tied_weights = tied_weights
self.rng = make_np_rng(rng, which_method="randn")
self._initialize_hidbias()
if nvis > 0:
self._initialize_visbias(nvis)
self._initialize_weights(nvis)
else:
self.visbias = None
self.weights = None
seed = int(self.rng.randint(2 ** 30))
# why a theano rng? should we remove it?
self.s_rng = make_theano_rng(seed, which_method="uniform")
if tied_weights and self.weights is not None:
self.w_prime = self.weights.T
else:
self._initialize_w_prime(nvis)
def _resolve_callable(conf, conf_attr):
"""
.. todo::
WRITEME
"""
if conf[conf_attr] is None or conf[conf_attr] == "linear":
return None
# If it's a callable, use it directly.
if hasattr(conf[conf_attr], "__call__"):
return conf[conf_attr]
elif conf[conf_attr] in globals() and hasattr(globals()[conf[conf_attr]], "__call__"):
return globals()[conf[conf_attr]]
elif hasattr(tensor.nnet, conf[conf_attr]):
return getattr(tensor.nnet, conf[conf_attr])
elif hasattr(tensor, conf[conf_attr]):
return getattr(tensor, conf[conf_attr])
else:
raise ValueError("Couldn't interpret %s value: '%s'" % (conf_attr, conf[conf_attr]))
self.act_enc = _resolve_callable(locals(), "act_enc")
self.act_dec = _resolve_callable(locals(), "act_dec")
self._params = [self.visbias, self.hidbias, self.weights]
if not self.tied_weights:
self._params.append(self.w_prime)
def __init__(self,
learning_rate,
cost=None,
batch_size=None,
monitoring_batches=None,
monitoring_dataset=None,
monitor_iteration_mode='sequential',
termination_criterion=None,
update_callbacks=None,
learning_rule=None,
init_momentum=None,
set_batch_size=False,
train_iteration_mode=None,
batches_per_iter=None,
theano_function_mode=None,
monitoring_costs=None,
seed=[2012, 10, 5]):
if isinstance(cost, (list, tuple, set)):
raise TypeError("SGD no longer supports using collections of " +
"Costs to represent a sum of Costs. Use " +
"pylearn2.costs.cost.SumOfCosts instead.")
if init_momentum:
warnings.warn(
"init_momentum interface is deprecated and will "
"become officially unsuported as of May 9, 2014. Please use the "
"`learning_rule` parameter instead, providing an object of type "
"`pylearn2.training_algorithms.learning_rule.Momentum` instead"
)
# Convert to new interface under the hood.
self.learning_rule = Momentum(init_momentum)
else:
self.learning_rule = learning_rule
self.learning_rate = sharedX(learning_rate, 'learning_rate')
self.cost = cost
self.batch_size = batch_size
self.set_batch_size = set_batch_size
self.batches_per_iter = batches_per_iter
self._set_monitoring_dataset(monitoring_dataset)
self.monitoring_batches = monitoring_batches
self.monitor_iteration_mode = monitor_iteration_mode
if monitoring_dataset is None:
if monitoring_batches is not None:
raise ValueError("Specified an amount of monitoring batches " +
"but not a monitoring dataset.")
self.termination_criterion = termination_criterion
self._register_update_callbacks(update_callbacks)
if train_iteration_mode is None:
train_iteration_mode = 'shuffled_sequential'
self.train_iteration_mode = train_iteration_mode
self.first = True
self.rng = make_np_rng(seed, which_method=["randn", "randint"])
self.theano_function_mode = theano_function_mode
self.monitoring_costs = monitoring_costs
def __init__(self, num_examples, rng=(2013, 5, 17)):
"""
.. todo::
WRITEME
"""
rng = make_np_rng(rng, self._default_seed, which_method='uniform')
X = rng.uniform(-1, 1, size=(num_examples, 2))
y = _four_regions_labels(X)
super(FourRegions, self).__init__(X=X, y=y, y_labels=4)
def __init__(self, num_examples, rng=(2013, 5, 17)):
"""
.. todo::
WRITEME
"""
rng = make_np_rng(rng, self._default_seed, which_method='uniform')
X = rng.uniform(-1, 1, size=(num_examples, 2))
y = _four_regions_labels(X)
super(FourRegions, self).__init__(X=X, y=y, y_labels=4)
def test_np_rng():
"""
Tests that the four possible ways of creating
a numpy RNG give the same results with the same seed
"""
rngs = [make_np_rng(rng_or_seed=42, which_method='uniform'),
make_np_rng(rng_or_seed=numpy.random.RandomState(42),
which_method='uniform'),
make_np_rng(default_seed=42),
make_np_rng()]
random_numbers = rngs[0].uniform(size=(100,))
equals = numpy.ones((100,))
for rng in rngs[1:]:
equal = random_numbers == rng.uniform(size=(100,))
equals *= equal
assert equals.all()
def __init__(self, min_x=-6.28, max_x=6.28, std=.05, rng=None):
"""
.. todo::
WRITEME
"""
self.min_x, self.max_x, self.std = min_x, max_x, std
rng = make_np_rng(rng, [17, 2, 946], which_method=['uniform', 'randn'])
self.default_rng = copy.copy(rng)
self.rng = rng
def __init__(self, min_x=-6.28, max_x=6.28, std=.05, rng=None):
"""
.. todo::
WRITEME
"""
self.min_x, self.max_x, self.std = min_x, max_x, std
rng = make_np_rng(rng, [17, 2, 946], which_method=['uniform', 'randn'])
self.default_rng = copy.copy(rng)
self.rng = rng
def reset_RNG(self):
"""
Restore the default seed of the rng used for choosing random
examples.
"""
if 'default_rng' not in dir(self):
self.default_rng = make_np_rng(None, [17, 2, 946],
which_method="random_integers")
self.rng = copy.copy(self.default_rng)
def __call__(self):
print 'loading model'
model_path = self.model_path
self.model = serial.load(model_path)
self.model.set_dtype('float32')
input_space = self.model.get_input_space()
#This code all assumes the model works on vectors
assert isinstance(input_space, VectorSpace)
nvis = input_space.dim
self.size = int(np.sqrt(nvis / 3))
rng = make_np_rng(None, [1, 2, 3], which_method="randint")
#Generate the random pooling structure
num_filters = self.model.mu.get_value().shape[0]
idxs = rng.randint(0, num_filters, (self.num_output_features, ))
top = idxs.copy()
bottom = idxs.copy()
left = idxs.copy()
right = idxs.copy()
for i in xrange(self.num_output_features):
top[i] = rng.randint(num_superpixels)
bottom[i] = rng.randint(top[i], num_superpixels)
left[i] = rng.randint(num_superpixels)
right[i] = rng.randint(left[i], num_superpixels)
#output[i] will pool over detector feature map i
self.idxs = idxs
#output [i] will pool over a rectangle with upper left coordinates (top[i],left[i])
#and lower right coordinates (bottom[i],right[i])
self.top = top
self.bottom = bottom
self.left = left
self.right = right
#Run the experiment
if self.chunk_size is not None:
dataset_family = self.dataset_family
which_set = self.which_set
dataset_descriptor = self.dataset_family[which_set][size]
num_examples = dataset_descriptor.num_examples
assert num_examples % self.chunk_size == 0
self.chunk_id = 0
for i in xrange(0, num_examples, self.chunk_size):
self.restrict = (i, i + self.chunk_size)
self._execute()
self.chunk_id += 1
else:
self._execute()
def __init__(self, dataset_size, batch_size, num_batches, rng=None):
super(ShuffledSequentialSubsetIterator, self).__init__(
dataset_size,
batch_size,
num_batches,
None
)
self._rng = make_np_rng(rng, which_method=["random_integers",
"shuffle"])
self._shuffled = np.arange(self._dataset_size)
self._rng.shuffle(self._shuffled)
def test_np_rng():
"""
Tests that the four possible ways of creating
a numpy RNG give the same results with the same seed
"""
rngs = [
make_np_rng(rng_or_seed=42, which_method='uniform'),
make_np_rng(rng_or_seed=numpy.random.RandomState(42),
which_method='uniform'),
make_np_rng(default_seed=42),
make_np_rng()
]
random_numbers = rngs[0].uniform(size=(100, ))
equals = numpy.ones((100, ))
for rng in rngs[1:]:
equal = random_numbers == rng.uniform(size=(100, ))
equals *= equal
assert equals.all()
def shuffle(X, Y):
shuffle_rng = make_np_rng(None, [1, 2, 3], which_method="shuffle")
for i in xrange(X.shape[0]):
j = shuffle_rng.randint(len(X))
# Copy ensures that memory is not aliased.
tmp = X[i, :, :, :].copy()
X[i, :, :, :] = X[j, :, :, :]
X[j, :, :, :] = tmp
tmp = Y[i:i + 1].copy()
Y[i] = Y[j]
Y[j] = tmp
def __init__(self,
nvis,
prior,
conditional,
posterior,
nhid,
learn_prior=True,
kl_integrator=None,
batch_size=None,
seed=None):
super(VAE, self).__init__()
self.__dict__.update(locals())
del self.self
self.rng = make_np_rng(self.seed, default_seed,
['uniform', 'randint', 'randn'])
self.prior.set_vae(self)
self.conditional.set_vae(self)
self.posterior.set_vae(self)
self.learn_prior = learn_prior
# Space initialization
self.input_space = VectorSpace(dim=self.nvis)
self.input_source = 'features'
self.latent_space = VectorSpace(dim=self.nhid)
# Parameter initialization
self.prior.initialize_parameters(nhid=self.nhid)
self.conditional.initialize_parameters(input_space=self.latent_space,
ndim=self.nvis)
self.posterior.initialize_parameters(input_space=self.input_space,
ndim=self.nhid)
self._params = (self.get_posterior_params() +
self.get_conditional_params())
if self.learn_prior:
self._params += self.get_prior_params()
names = []
for param in self._params:
if param.name not in names:
names.append(param.name)
else:
raise Exception(
"no two parameters must share the same name: " +
param.name)
# Look for the right KLIntegrator if it's not specified
if self.kl_integrator is None:
self.kl_integrator = find_integrator_for(self.prior,
self.posterior)
