本文整理汇总了Python中pylearn2.datasets.dense_design_matrix.DenseDesignMatrix类的典型用法代码示例。如果您正苦于以下问题:Python DenseDesignMatrix类的具体用法?Python DenseDesignMatrix怎么用?Python DenseDesignMatrix使用的例子?那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了DenseDesignMatrix类的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: get_feats_from_cnn
def get_feats_from_cnn(rows, model=None):
"""
fprop rows using best trained model and returns activations of the
penultimate layer
"""
conf = utils.get_config()
patch_size = conf['patch_size']
region_size = conf['region_size']
batch_size = None
preds = utils.get_predictor(model=model, return_all=True)
y = np.zeros(len(rows))
samples = np.zeros(
(len(rows), region_size, region_size, 1), dtype=np.float32)
for i, row in enumerate(rows):
print 'processing %i-th image: %s' % (i, row['image_filename'])
try:
samples[i] = utils.get_samples_from_image(row, False)[0]
except ValueError as e:
print '{1} Value error: {0}'.format(str(e), row['image_filename'])
y[i] = utils.is_positive(row)
ds = DenseDesignMatrix(topo_view=samples)
pipeline = utils.get_pipeline(
ds.X_topo_space.shape, patch_size, batch_size)
pipeline.apply(ds)
return preds[-2](ds.get_topological_view()), y
示例2: test_convert_to_one_hot
def test_convert_to_one_hot():
rng = np.random.RandomState([2013, 11, 14])
m = 11
d = DenseDesignMatrix(
X=rng.randn(m, 4),
y=rng.randint(low=0, high=10, size=(m,)))
d.convert_to_one_hot()
示例3: next
def next(self):
next_index = self._subset_iterator.next()
# convert to boolean selection
sel = np.zeros(self.num_examples, dtype=bool)
sel[next_index] = True
next_index = sel
rval = []
for data, fn in safe_izip(self._raw_data, self._convert):
try:
this_data = data[next_index]
except TypeError:
this_data = data[next_index, :]
if fn:
this_data = fn(this_data)
if self._preprocessor is not None:
d = DenseDesignMatrix(X=this_data)
self._preprocessor.apply(d)
this_data = d.get_design_matrix()
assert not np.any(np.isnan(this_data))
rval.append(this_data)
rval = tuple(rval)
if not self._return_tuple and len(rval) == 1:
rval, = rval
return rval
示例4: apply_ZCA_fast
def apply_ZCA_fast(patches, normalize, zca_preprocessor):
patches = patches.astype(np.float32)
if normalize:
patches /= 255.0
dataset = DenseDesignMatrix(X = patches.T)
zca_preprocessor.apply(dataset)
patches = dataset.get_design_matrix()
return patches.T
示例5: test
def test(store_inverse):
preprocessed_X = copy.copy(self.X)
preprocessor = ZCA(store_inverse=store_inverse)
dataset = DenseDesignMatrix(X=preprocessed_X,
preprocessor=preprocessor,
fit_preprocessor=True)
preprocessed_X = dataset.get_design_matrix()
assert_allclose(self.X, preprocessor.inverse(preprocessed_X))
示例6: make_dataset
def make_dataset(num_batches):
m = num_batches*batch_size
X = rng.randn(m, num_features)
y = rng.randn(m, num_features)
rval = DenseDesignMatrix(X=X, y=y)
rval.yaml_src = "" # suppress no yaml_src warning
return rval
示例7: __init__
def __init__(self, which_set, data_path=None,
term_range=None, target_type='cluster100'):
"""
which_set: a string specifying which portion of the dataset
to load. Valid values are 'train', 'valid' or 'test'
data_path: a string specifying the directory containing the
webcluster data. If None (default), use environment
variable WEBCLUSTER_DATA_PATH.
term_range: a tuple for taking only a slice of the available
terms. Default is to use all 6275. For example, an input
range of (10,2000) will truncate the 10 most frequent terms
and the 6275-2000=4275 les frequent terms, whereby frequency
we mean how many unique documents each term is in.
target_type: the type of targets to use. Valid options are
'cluster[10,100,1000]'
"""
self.__dict__.update(locals())
del self.self
self.corpus_terms = None
self.doc_info = None
print "loading WebCluster DDM. which_set =", self.which_set
if self.data_path is None:
self.data_path \
= string_utils.preprocess('${WEBCLUSTER_DATA_PATH}')
fname = os.path.join(self.data_path, which_set+'_doc_inputs.npy')
X = np.load(fname)
if self.term_range is not None:
X = X[:,self.term_range[0]:self.term_range[1]]
X = X/X.sum(1).reshape(X.shape[0],1)
print X.sum(1).mean()
fname = os.path.join(self.data_path, which_set+'_doc_targets.npy')
# columns: 0:cluster10s, 1:cluster100s, 2:cluster1000s
self.cluster_hierarchy = np.load(fname)
y = None
if self.target_type == 'cluster10':
y = self.cluster_hierarchy[:,0]
elif self.target_type == 'cluster100':
y = self.cluster_hierarchy[:,1]
elif self.target_type == 'cluster1000':
y = self.cluster_hierarchy[:,2]
elif self.target_type is None:
pass
else:
raise NotImplementedError()
DenseDesignMatrix.__init__(self, X=X, y=y)
print "... WebCluster ddm loaded"
示例8: test
def test(store_inverse):
rng = np.random.RandomState([1, 2, 3])
X = as_floatX(rng.randn(15, 10))
preprocessed_X = copy.copy(X)
preprocessor = ZCA(store_inverse=store_inverse)
dataset = DenseDesignMatrix(X=preprocessed_X,
preprocessor=preprocessor,
fit_preprocessor=True)
preprocessed_X = dataset.get_design_matrix()
assert_allclose(X, preprocessor.inverse(preprocessed_X))
示例9: convert_to_dataset
def convert_to_dataset(X,y):
X = np.vstack(X);
y = np.vstack(y);
# convert labels
y = self.label_converter.get_labels(y, self.label_mode);
y = np.hstack(y);
one_hot_y = one_hot(y);
dataset = DenseDesignMatrix(X=X, y=one_hot_y);
dataset.labels = y; # for confusion matrix
return dataset;
示例10: make_dataset
def make_dataset(num_batches):
disturb_mem.disturb_mem()
m = num_batches*batch_size
X = rng.randn(m, num_features)
y = np.zeros((m,1))
y[:,0] = np.dot(X, w) > 0.
rval = DenseDesignMatrix(X=X, y=y)
rval.yaml_src = "" # suppress no yaml_src warning
X = rval.get_batch_design(batch_size)
assert X.shape == (batch_size, num_features)
return rval
示例11: test_zero_vector
def test_zero_vector(self):
""" Test that passing in the zero vector does not result in
a divide by 0 """
dataset = DenseDesignMatrix(X=as_floatX(np.zeros((1, 1))))
# the settings of subtract_mean and use_norm are not relevant to
# the test
# std_bias = 0.0 is the only value for which there should be a risk
# of failure occurring
preprocessor = GlobalContrastNormalization(subtract_mean=True, sqrt_bias=0.0, use_std=True)
dataset.apply_preprocessor(preprocessor)
result = dataset.get_design_matrix()
assert not np.any(np.isnan(result))
assert not np.any(np.isinf(result))
示例12: test_finitedataset_source_check
def test_finitedataset_source_check():
"""
Check that the FiniteDatasetIterator returns sensible
errors when there is a missing source in the dataset.
"""
dataset = DenseDesignMatrix(X=np.random.rand(20,15).astype(theano.config.floatX),
y=np.random.rand(20,5).astype(theano.config.floatX))
assert_raises(ValueError,
dataset.iterator,
mode='sequential',
batch_size=5,
data_specs=(VectorSpace(15),'featuresX'))
try:
dataset.iterator(mode='sequential',
batch_size=5,
data_specs=(VectorSpace(15),'featuresX'))
except ValueError as e:
assert 'featuresX' in str(e)
示例13: test_random_image
def test_random_image(self):
"""
Test on a random image if the per-processor loads and works without
anyerror and doesn't result in any nan or inf values
"""
rng = np.random.RandomState([1, 2, 3])
X = as_floatX(rng.randn(5, 32 * 32 * 3))
axes = ["b", 0, 1, "c"]
view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3), axes)
dataset = DenseDesignMatrix(X=X, view_converter=view_converter)
dataset.axes = axes
preprocessor = LeCunLCN(img_shape=[32, 32])
dataset.apply_preprocessor(preprocessor)
result = dataset.get_design_matrix()
assert not np.any(np.isnan(result))
assert not np.any(np.isinf(result))
示例14: test_split_nfold_datasets
def test_split_nfold_datasets():
#Load and create ddm from cifar100
path = "/data/lisa/data/cifar100/cifar-100-python/train"
obj = serial.load(path)
X = obj['data']
assert X.max() == 255.
assert X.min() == 0.
X = np.cast['float32'](X)
y = None #not implemented yet
view_converter = DefaultViewConverter((32,32,3))
ddm = DenseDesignMatrix(X = X, y =y, view_converter = view_converter)
assert not np.any(np.isnan(ddm.X))
ddm.y_fine = np.asarray(obj['fine_labels'])
ddm.y_coarse = np.asarray(obj['coarse_labels'])
folds = ddm.split_dataset_nfolds(10)
print folds[0].shape
示例15: __init__
def __init__(self, filename, X=None, topo_view=None, y=None,
load_all=False, **kwargs):
if 'preprocessor' in kwargs:
if ('fit_preprocessor' in kwargs and
kwargs['fit_preprocessor'] is False) or ('fit_preprocessor'
not in kwargs):
self._preprocessor = kwargs['preprocessor']
kwargs['preprocessor'] = None
else:
self._preprocessor = None
self.load_all = load_all
if h5py is None:
raise RuntimeError("Could not import h5py.")
self._file = h5py.File(filename)
if X is not None:
X = self.get_dataset(X, load_all)
if topo_view is not None:
topo_view = self.get_dataset(topo_view, load_all)
if y is not None:
y = self.get_dataset(y, load_all)
DenseDesignMatrix.__init__(self, X=X, topo_view=topo_view, y=y,
**kwargs)