def get_init(features, initializer_class, nhid=5):
initializer = initializer_class.build({}, graph=features.graph)
if hasattr(initializer, "Input"):
initializer.Input.resize(2)
initializer.Input[0].connect(features.Output)
nvis = features.Output.meta.shape[1]
layer = Sigmoid(layer_name="a", irange=0, dim=nhid)
# layer needs to be initialized by MLP first
MLP(layers=[layer], nvis=nvis)
initializer.init_layer(layer, nvis=nvis, nhid=nhid)
weights = layer.get_weights()
biases = layer.get_biases()
return weights, biases
def testLSF(self):
init = LeastSquaresWeightInitializer.build({}, graph=Graph())
init.Input.resize(2)
init.Input[0].setValue(self.X)
init.Input[1].setValue(self.y)
for k in (2, 3, 5, 7):
layer = Sigmoid(layer_name="a", irange=0, dim=k)
# layer needs to be initialized by MLP first
MLP(layers=[layer], nvis=self.d)
np.random.seed(123)
init.init_layer(layer, nvis=self.d, nhid=k)
weights = layer.get_weights()
np.testing.assert_array_equal(weights.shape, (self.d, k))
assert np.abs(weights).sum() > 0
def testPCA(self):
init = PCAWeightInitializer.build({}, graph=Graph())
init.Input.resize(2)
init.Input[0].setValue(self.X)
init.Input[1].setValue(self.y)
for k in (2, 4, 6, 8):
layer = Sigmoid(layer_name="a", irange=0, dim=k)
# layer needs to be initialized by MLP first
MLP(layers=[layer], nvis=self.d)
np.random.seed(123)
init.init_layer(layer, nvis=self.d, nhid=k)
weights = layer.get_weights()
np.testing.assert_array_equal(weights.shape, (self.d, k))
assert np.abs(weights).sum() > 0
if k <= 2*self.d:
for i in range(k):
for j in range(i+2, k, 2):
dot = np.dot(weights[:, i], weights[:, j])
np.testing.assert_almost_equal(dot, 0, decimal=4)
