def test_serialization():
# Create a simple brick with two parameters
mlp = MLP(activations=[None, None],
dims=[10, 10, 10],
weights_init=Constant(1.),
use_bias=False)
mlp.initialize()
W = mlp.linear_transformations[1].W
W.set_value(W.get_value() * 2)
# Check the data using numpy.load
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f)
numpy_data = numpy.load(f.name)
assert set(numpy_data.keys()) == \
set(['mlp-linear_0.W', 'mlp-linear_1.W', 'pkl'])
assert_allclose(numpy_data['mlp-linear_0.W'], numpy.ones((10, 10)))
assert numpy_data['mlp-linear_0.W'].dtype == theano.config.floatX
# Ensure that it can be unpickled
mlp = load(f.name)
assert_allclose(mlp.linear_transformations[1].W.get_value(),
numpy.ones((10, 10)) * 2)
# Ensure that only parameters are saved as NPY files
mlp.random_data = numpy.random.rand(10)
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f)
numpy_data = numpy.load(f.name)
assert set(numpy_data.keys()) == \
set(['mlp-linear_0.W', 'mlp-linear_1.W', 'pkl'])
# Ensure that parameters can be loaded with correct names
parameter_values = load_parameter_values(f.name)
assert set(parameter_values.keys()) == \
set(['/mlp/linear_0.W', '/mlp/linear_1.W'])
# Ensure that duplicate names are dealt with
for child in mlp.children:
child.name = 'linear'
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f)
numpy_data = numpy.load(f.name)
assert set(numpy_data.keys()) == \
set(['mlp-linear.W', 'mlp-linear.W_2', 'pkl'])
# Ensure warnings are raised when __main__ namespace objects are dumped
foo.__module__ = '__main__'
import __main__
__main__.__dict__['foo'] = foo
mlp.foo = foo
with NamedTemporaryFile(delete=False) as f:
with warnings.catch_warnings(record=True) as w:
dump(mlp, f)
assert len(w) == 1
assert '__main__' in str(w[-1].message)
def test_serialization():
# Create a simple brick with two parameters
mlp = MLP(activations=[None, None], dims=[10, 10, 10],
weights_init=Constant(1.), use_bias=False)
mlp.initialize()
W = mlp.linear_transformations[1].W
W.set_value(W.get_value() * 2)
# Check the data using numpy.load
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f)
numpy_data = numpy.load(f.name)
assert set(numpy_data.keys()) == \
set(['mlp-linear_0.W', 'mlp-linear_1.W', 'pkl'])
assert_allclose(numpy_data['mlp-linear_0.W'], numpy.ones((10, 10)))
assert numpy_data['mlp-linear_0.W'].dtype == theano.config.floatX
# Ensure that it can be unpickled
mlp = load(f.name)
assert_allclose(mlp.linear_transformations[1].W.get_value(),
numpy.ones((10, 10)) * 2)
# Ensure that only parameters are saved as NPY files
mlp.random_data = numpy.random.rand(10)
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f)
numpy_data = numpy.load(f.name)
assert set(numpy_data.keys()) == \
set(['mlp-linear_0.W', 'mlp-linear_1.W', 'pkl'])
# Ensure that parameters can be loaded with correct names
parameter_values = load_parameter_values(f.name)
assert set(parameter_values.keys()) == \
set(['/mlp/linear_0.W', '/mlp/linear_1.W'])
# Ensure that duplicate names are dealt with
for child in mlp.children:
child.name = 'linear'
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f)
numpy_data = numpy.load(f.name)
assert set(numpy_data.keys()) == \
set(['mlp-linear.W', 'mlp-linear.W_2', 'pkl'])
# Ensure warnings are raised when __main__ namespace objects are dumped
foo.__module__ = '__main__'
import __main__
__main__.__dict__['foo'] = foo
mlp.foo = foo
with NamedTemporaryFile(delete=False) as f:
with warnings.catch_warnings(record=True) as w:
dump(mlp, f)
assert len(w) == 1
assert '__main__' in str(w[-1].message)
def test_serialization():
# Create a simple MLP to dump.
mlp = MLP(activations=[None, None],
dims=[10, 10, 10],
weights_init=Constant(1.),
use_bias=False)
mlp.initialize()
W = mlp.linear_transformations[1].W
W.set_value(W.get_value() * 2)
# Ensure warnings are raised when __main__ namespace objects are dumped.
foo.__module__ = '__main__'
import __main__
__main__.__dict__['foo'] = foo
mlp.foo = foo
with NamedTemporaryFile(delete=False) as f:
with warnings.catch_warnings(record=True) as w:
dump(mlp.foo, f)
assert len(w) == 1
assert '__main__' in str(w[-1].message)
# Check the parameters.
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f, parameters=[mlp.children[0].W, mlp.children[1].W])
with open(f.name, 'rb') as ff:
numpy_data = load_parameters(ff)
assert set(numpy_data.keys()) == \
set(['/mlp/linear_0.W', '/mlp/linear_1.W'])
assert_allclose(numpy_data['/mlp/linear_0.W'], numpy.ones((10, 10)))
assert numpy_data['/mlp/linear_0.W'].dtype == theano.config.floatX
# Ensure that it can be unpickled.
with open(f.name, 'rb') as ff:
mlp = load(ff)
assert_allclose(mlp.linear_transformations[1].W.get_value(),
numpy.ones((10, 10)) * 2)
# Ensure that duplicate names are dealt with.
for child in mlp.children:
child.name = 'linear'
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f, parameters=[mlp.children[0].W, mlp.children[1].W])
with open(f.name, 'rb') as ff:
numpy_data = load_parameters(ff)
assert set(numpy_data.keys()) == \
set(['/mlp/linear.W', '/mlp/linear.W_2'])
# Check when we don't dump the main object.
with NamedTemporaryFile(delete=False) as f:
dump(None, f, parameters=[mlp.children[0].W, mlp.children[1].W])
with tarfile.open(f.name, 'r') as tarball:
assert set(tarball.getnames()) == set(['_parameters'])
def test_serialization():
# Create a simple MLP to dump.
mlp = MLP(activations=[None, None], dims=[10, 10, 10],
weights_init=Constant(1.), use_bias=False)
mlp.initialize()
W = mlp.linear_transformations[1].W
W.set_value(W.get_value() * 2)
# Ensure warnings are raised when __main__ namespace objects are dumped.
foo.__module__ = '__main__'
import __main__
__main__.__dict__['foo'] = foo
mlp.foo = foo
with NamedTemporaryFile(delete=False) as f:
with warnings.catch_warnings(record=True) as w:
dump(mlp.foo, f)
assert len(w) == 1
assert '__main__' in str(w[-1].message)
# Check the parameters.
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f, parameters=[mlp.children[0].W, mlp.children[1].W])
with open(f.name, 'rb') as ff:
numpy_data = load_parameters(ff)
assert set(numpy_data.keys()) == \
set(['/mlp/linear_0.W', '/mlp/linear_1.W'])
assert_allclose(numpy_data['/mlp/linear_0.W'], numpy.ones((10, 10)))
assert numpy_data['/mlp/linear_0.W'].dtype == theano.config.floatX
# Ensure that it can be unpickled.
with open(f.name, 'rb') as ff:
mlp = load(ff)
assert_allclose(mlp.linear_transformations[1].W.get_value(),
numpy.ones((10, 10)) * 2)
# Ensure that duplicate names are dealt with.
for child in mlp.children:
child.name = 'linear'
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f, parameters=[mlp.children[0].W, mlp.children[1].W])
with open(f.name, 'rb') as ff:
numpy_data = load_parameters(ff)
assert set(numpy_data.keys()) == \
set(['/mlp/linear.W', '/mlp/linear.W_2'])
# Check when we don't dump the main object.
with NamedTemporaryFile(delete=False) as f:
dump(None, f, parameters=[mlp.children[0].W, mlp.children[1].W])
with tarfile.open(f.name, 'r') as tarball:
assert set(tarball.getnames()) == set(['_parameters'])
