本文整理汇总了Python中pylearn2.utils.rng.make_np_rng函数的典型用法代码示例。如果您正苦于以下问题:Python make_np_rng函数的具体用法?Python make_np_rng怎么用?Python make_np_rng使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了make_np_rng函数的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
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")
示例2: make_sparse_random_conv2D
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)
示例3: make_weights
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))
示例4: __init__
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')
示例5: __init__
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))
示例6: __init__
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
开发者ID:Dining-Engineers,项目名称:Multi-Column-Deep-Neural-Network,代码行数:25,代码来源:vector_spaces_dataset_c01b.py
示例7: __init__
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)
示例8: setup_rng
def setup_rng(self):
"""
.. todo::
WRITEME
"""
self.rng = make_np_rng(None, [2012, 10, 17], which_method="uniform")
示例9: make_random_conv2D
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
)
示例10: _create_subset_iterator
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)
示例11: make_random_conv2D
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)
示例12: __init__
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()
示例13: make_sparse_random_local
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)
示例14: split_patients
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
示例15: make_random_conv3D
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)