def test_logistic_cputensor():
lgstc = Logistic()
inputs = np.array([0, 1, -2]).reshape((3, 1))
be = CPU(rng_seed=0)
temp = be.zeros((3, 1))
outputs = 1.0 / (1.0 + np.exp(-inputs))
lgstc.apply_function(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_logistic_derivative_cputensor():
lgstc = Logistic()
inputs = np.array([0, 1, -2]).reshape((3, 1))
be = CPU(rng_seed=0)
outputs = 1.0 / (1.0 + np.exp(-inputs))
outputs = outputs * (1.0 - outputs)
temp = be.zeros(inputs.shape)
lgstc.apply_derivative(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_logistic_cc2tensor():
lgstc = Logistic()
from neon.backends.cc2 import GPU, GPUTensor
inputs = np.array([0, 1, -2]).reshape((3, 1))
outputs = 1.0 / (1.0 + np.exp(-inputs))
be = GPU(rng_seed=0)
temp = be.zeros((3, 1))
lgstc.apply_function(be, GPUTensor(inputs), temp)
assert_tensor_near_equal(GPUTensor(outputs), temp)
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']
})
