本文整理汇总了Python中pylearn2.testing.skip.skip_if_no_data函数的典型用法代码示例。如果您正苦于以下问题:Python skip_if_no_data函数的具体用法?Python skip_if_no_data怎么用?Python skip_if_no_data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skip_if_no_data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: test_one_hot
def test_one_hot():
skip_if_no_data()
data = icml07.MNIST_rotated_background(which_set='train', one_hot=True, split=(100,100,100))
assert data.y.shape[1] == 10 # MNITS hast 10 classes
data = icml07.Rectangles(which_set='train', one_hot=True, split=(100,100,100))
assert data.y.shape[1] == 2 # Two classes
示例2: test_convolutional_network
def test_convolutional_network():
"""Test smaller version of convolutional_network.ipynb"""
skip.skip_if_no_data()
yaml_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),
'..'))
save_path = os.path.dirname(os.path.realpath(__file__))
# Escape potential backslashes in Windows filenames, since
# they will be processed when the YAML parser will read it
# as a string
save_path.replace('\\', r'\\')
yaml = open("{0}/conv.yaml".format(yaml_file_path), 'r').read()
hyper_params = {'train_stop': 50,
'valid_stop': 50050,
'test_stop': 50,
'batch_size': 50,
'output_channels_h2': 4,
'output_channels_h3': 4,
'max_epochs': 1,
'save_path': save_path}
yaml = yaml % (hyper_params)
train = yaml_parse.load(yaml)
train.main_loop()
try:
os.remove("{}/convolutional_network_best.pkl".format(save_path))
except OSError:
pass
示例3: test_avicenna
def test_avicenna():
"""test that train/valid/test sets load (when standardize=False/true)."""
skip_if_no_data()
data = Avicenna(which_set='train', standardize=False)
assert data.X.shape == (150205, 120)
data = Avicenna(which_set='valid', standardize=False)
assert data.X.shape == (4096, 120)
data = Avicenna(which_set='test', standardize=False)
assert data.X.shape == (4096, 120)
# test that train/valid/test sets load (when standardize=True).
data_train = Avicenna(which_set='train', standardize=True)
assert data.X.shape == (150205, 120)
data_valid = Avicenna(which_set='valid', standardize=True)
assert data.X.shape == (4096, 120)
data_test = Avicenna(which_set='test', standardize=True)
assert data.X.shape == (4096, 120)
dt = np.concatenate([data_train.X, data_valid.X, data_test.X], axis=0)
assert np.allclose(dt.mean(), 0)
assert np.allclose(dt.std(), 1.)
示例4: test_hepatitis
def test_hepatitis():
"""test hepatitis dataset"""
skip_if_no_data()
data = hepatitis.Hepatitis()
assert data.X is not None
assert np.all(data.X != np.inf)
assert np.all(data.X != np.nan)
示例5: setUp
def setUp(self):
"""
Attempts to load train and test
"""
skip_if_no_data()
self.train = MNIST_rotated_background(which_set='train')
self.test = MNIST_rotated_background(which_set='test')
示例6: Transform
def Transform():
"""Test smaller version of convolutional_network.ipynb"""
which_experiment = "S100"
skip.skip_if_no_data()
yaml_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
data_dir = string_utils.preprocess("${PYLEARN2_DATA_PATH}")
save_path = os.path.join(data_dir, "cifar10", "experiment_" + string.lower(which_experiment))
base_save_path = os.path.join(data_dir, "cifar10")
# Escape potential backslashes in Windows filenames, since
# they will be processed when the YAML parser will read it
# as a string
# save_path.replace('\\', r'\\')
yaml = open("{0}/experiment_base_transform.yaml".format(yaml_file_path), "r").read()
hyper_params = {
"batch_size": 64,
"output_channels_h1": 64,
"output_channels_h2": 128,
"output_channels_h3": 600,
"max_epochs": 100,
"save_path": save_path,
"base_save_path": base_save_path,
}
yaml = yaml % (hyper_params)
train = yaml_parse.load(yaml)
train.main_loop()
示例7: test_iris
def test_iris():
"""Load iris dataset"""
skip_if_no_data()
data = iris.Iris()
assert data.X is not None
assert np.all(data.X != np.inf)
assert np.all(data.X != np.nan)
示例8: test_ule
def test_ule():
skip_if_no_data()
# Test loading of transfer data
train, valid, test, transfer = utlc.load_ndarray_dataset("ule",
normalize=True,
transfer=True)
assert train.shape[0] == transfer.shape[0]
示例9: test
def test():
skip_if_no_data()
dirname = os.path.join(os.path.abspath(os.path.dirname(__file__)), '..')
with open(os.path.join(dirname, 'sr_dataset.yaml'), 'r') as f:
dataset = f.read()
hyper_params = {'train_stop': 50}
dataset = dataset % (hyper_params)
with open(os.path.join(dirname, 'sr_model.yaml'), 'r') as f:
model = f.read()
with open(os.path.join(dirname, 'sr_algorithm.yaml'), 'r') as f:
algorithm = f.read()
hyper_params = {'batch_size': 10,
'valid_stop': 50050}
algorithm = algorithm % (hyper_params)
with open(os.path.join(dirname, 'sr_train.yaml'), 'r') as f:
train = f.read()
train = train % locals()
train = yaml_parse.load(train)
train.main_loop()
示例10: test
def test():
skip_if_no_data()
dirname = os.path.join(os.path.abspath(os.path.dirname(__file__)), '..')
with open(os.path.join(dirname, 'sr_dataset.yaml'), 'r') as f:
dataset = f.read()
hyper_params = {'train_stop': 50}
dataset = dataset % (hyper_params)
with open(os.path.join(dirname, 'sr_model.yaml'), 'r') as f:
model = f.read()
with open(os.path.join(dirname, 'sr_algorithm.yaml'), 'r') as f:
algorithm = f.read()
hyper_params = {'batch_size': 10,
'valid_stop': 50050}
algorithm = algorithm % (hyper_params)
with open(os.path.join(dirname, 'sr_train.yaml'), 'r') as f:
train = f.read()
save_path = os.path.dirname(os.path.realpath(__file__))
train = train % locals()
train = yaml_parse.load(train)
train.main_loop()
try:
os.remove("{}/softmax_regression.pkl".format(save_path))
os.remove("{}/softmax_regression_best.pkl".format(save_path))
except:
pass
示例11: train_generic
def train_generic():
PATH_TO_PYLEARN2_MODES_DIR = os.path.abspath('./model_pylearn2')
PATH_TO_INPUT_DIR = os.path.abspath('./intermediate_files_pylearn2/toy_train/')
PROJECT_NAME = 'toy_train'
skip.skip_if_no_data()
train_dbm_model(PATH_TO_PYLEARN2_MODES_DIR, PATH_TO_INPUT_DIR, PROJECT_NAME)
示例12: test_FoveatedNORB
def test_FoveatedNORB():
"""
This function tests the FoveatedNORB class. In addition to the shape and
datatype of X and y member of the returned object, it also checks the
scale of data while passing different parameters to the constructor.
"""
skip_if_no_data()
data = FoveatedNORB('train')
datamin = data.X.min()
datamax = data.X.max()
assert data.X.shape == (24300, 8976)
assert data.X.dtype == 'float32'
assert data.y.shape == (24300, )
assert data.y_labels == 5
assert data.get_topological_view().shape == (24300, 96, 96, 2)
data = FoveatedNORB('train', center=True)
assert data.X.min() == datamin - 127.5
assert data.X.max() == datamax - 127.5
data = FoveatedNORB('train', center=True, scale=True)
assert numpy.all(data.X <= 1.)
assert numpy.all(data.X >= -1.)
data = FoveatedNORB('train', scale=True)
assert numpy.all(data.X <= 1.)
assert numpy.all(data.X >= 0.)
data = FoveatedNORB('test')
assert data.X.shape == (24300, 8976)
assert data.X.dtype == 'float32'
assert data.y.shape == (24300, )
assert data.y_labels == 5
assert data.get_topological_view().shape == (24300, 96, 96, 2)
示例13: train_convolutional_network
def train_convolutional_network(yaml_with_hyper_params):
skip.skip_if_no_data()
train = yaml_parse.load(yaml_with_hyper_params)
train.main_loop()
示例14: test_avicenna
def test_avicenna():
"""test that train/valid/test sets load (when standardize=False/true)."""
skip_if_no_data()
data = Avicenna(which_set='train', standardize=False)
assert data.X.shape == (150205, 120)
data = Avicenna(which_set='valid', standardize=False)
assert data.X.shape == (4096, 120)
data = Avicenna(which_set='test', standardize=False)
assert data.X.shape == (4096, 120)
# test that train/valid/test sets load (when standardize=True).
data_train = Avicenna(which_set='train', standardize=True)
assert data_train.X.shape == (150205, 120)
data_valid = Avicenna(which_set='valid', standardize=True)
assert data_valid.X.shape == (4096, 120)
data_test = Avicenna(which_set='test', standardize=True)
assert data_test.X.shape == (4096, 120)
dt = np.concatenate([data_train.X, data_valid.X, data_test.X], axis=0)
# Force double precision to compute mean and std, otherwise the test
# fails because of precision.
assert np.allclose(dt.mean(dtype='float64'), 0)
assert np.allclose(dt.std(dtype='float64'), 1.)
示例15: main
def main():
skip.skip_if_no_data()
# setting
data_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data'))
save_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'pylearn2/result'))
yaml_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'pylearn2/yaml'))
# set hyper parameter
yaml = open("{0}/conv_sample.yaml".format(yaml_path), 'r').read()
hyper_params = {'train_stop': 50,
'valid_stop': 50050,
'test_stop': 50,
'batch_size': 50,
'output_channels_h0': 2,
'output_channels_h1': 2,
'max_epochs': 10,
'data_path': data_path,
'save_path': save_path}
yaml = yaml % (hyper_params)
# train
train = yaml_parse.load(yaml)
train.main_loop()