def setup(self):
from neon.backends.cc2 import GPU, GPUTensor
# TODO: remove randomness from expected target results
self.be = GPU(rng_seed=0)
# reusable fake data
self.inputs = GPUTensor(np.ones((2, 100)))
# create fake layer
nin = 2
conf = {
'name': 'testlayer',
'num_nodes': 2,
'weight_init': GaussianValGen(backend=self.be, loc=0.0, scale=0.01)
}
lr_params = {'learning_rate': 0.01}
thislr = {'type': 'gradient_descent', 'lr_params': lr_params}
activation = Logistic()
self.layer = RBMLayer(name=conf['name'])
# create fake cost
self.cost = SumSquaredDiffs(olayer=self.layer)
self.layer.initialize({
'backend': self.be,
'batch_size': 100,
'lrule_init': thislr,
'nin': nin,
'nout': conf['num_nodes'],
'activation': activation,
'weight_init': conf['weight_init']
})
