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']
})
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']})
class TestCudaRBM:
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']})
def test_cudanet_positive(self):
self.layer.positive(self.inputs)
target = np.array([0.50541031, 0.50804842],
dtype='float32')
assert_tensor_near_equal(self.layer.p_hid_plus.asnumpyarray()[:, 0],
target)
def test_cudanet_negative(self):
self.layer.positive(self.inputs)
self.layer.negative(self.inputs)
target = np.array([0.50274211, 0.50407821],
dtype='float32')
assert_tensor_near_equal(self.layer.p_hid_minus.asnumpyarray()[:, 0],
target)
@nottest # TODO: remove randomness
def test_cudanet_cost(self):
self.layer.positive(self.inputs)
self.layer.negative(self.inputs)
thecost = self.cost.apply_function(self.inputs)
target = 106.588943481
assert_tensor_near_equal(thecost, target)
class TestCudaRBM:
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']
})
def test_cudanet_positive(self):
self.layer.positive(self.inputs)
target = np.array([0.50541031, 0.50804842], dtype='float32')
assert_tensor_near_equal(self.layer.p_hid_plus.asnumpyarray()[:, 0],
target)
def test_cudanet_negative(self):
self.layer.positive(self.inputs)
self.layer.negative(self.inputs)
target = np.array([0.50274211, 0.50407821], dtype='float32')
assert_tensor_near_equal(self.layer.p_hid_minus.asnumpyarray()[:, 0],
target)
@nottest # TODO: remove randomness
def test_cudanet_cost(self):
self.layer.positive(self.inputs)
self.layer.negative(self.inputs)
thecost = self.cost.apply_function(self.inputs)
target = 106.588943481
assert_tensor_near_equal(thecost, target)
