def test_summarize_model():
"""
Asks the summarize_model.py script to inspect a pickled model and
check that it completes succesfully
"""
skip_if_no_matplotlib()
with open('model.pkl', 'wb') as f:
cPickle.dump(MLP(layers=[Linear(dim=5, layer_name='h0', irange=0.1)],
nvis=10), f, protocol=cPickle.HIGHEST_PROTOCOL)
summarize('model.pkl')
os.remove('model.pkl')
def test_show_examples():
"""
Create a YAML file of the MNIST dataset and show examples
"""
skip_if_no_matplotlib()
skip_if_no_data()
with open('temp.yaml', 'w') as f:
f.write("""
!obj:pylearn2.datasets.mnist.MNIST {
which_set: 'train'
}
""")
show_examples('temp.yaml', 28, 28, out='garbage.png')
os.remove('temp.yaml')
def test_show_weights():
"""
Create a pickled model and show the weights
"""
skip_if_no_matplotlib()
with open('model.pkl', 'wb') as f:
model = MLP(layers=[Linear(dim=1, layer_name='h0', irange=0.1)],
nvis=784)
model.dataset_yaml_src = """
!obj:pylearn2.datasets.mnist.MNIST {
which_set: 'train'
}
"""
cPickle.dump(model, f, protocol=cPickle.HIGHEST_PROTOCOL)
show_weights('model.pkl', rescale='individual',
border=True, out='garbage.png')
os.remove('model.pkl')
os.remove('garbage.png')
