def test_TransferFailure(self):
X = numpy.zeros((8, 4))
ae = AE(layers=[L("Tanh", units=8)], n_iter=1)
ae.fit(X)
nn = mlp.MultiLayerPerceptron(layers=[mlp.Layer("Tanh", units=4)])
assert_raises(AssertionError, ae.transfer, nn)
def test_TransferSuccess(self):
X = numpy.zeros((8, 4))
ae = AE(layers=[L("Tanh", units=4)], n_iter=1)
ae.fit(X)
nn = mlp.MultiLayerPerceptron(layers=[mlp.Layer("Tanh", units=4)])
ae.transfer(nn)
def test_LayerTypes(self):
X = numpy.zeros((8, 12))
for l in ['autoencoder', 'denoising']:
ae = AE(layers=[L("Sigmoid", type=l, units=4)])
y = ae.fit_transform(X)
assert_equals(type(y), numpy.ndarray)
assert_equals(y.shape, (8, 4))
def test_CostFunctions(self):
X = numpy.zeros((8, 12))
for t in ['msre', 'mbce']:
ae = AE(layers=[L("Sigmoid", units=4, cost=t)], n_iter=1)
y = ae.fit_transform(X)
assert_equals(type(y), numpy.ndarray)
assert_equals(y.shape, (8, 4))
def test_FitVerbose(self):
X = numpy.zeros((8, 4))
ae = AE(layers=[L("Sigmoid", units=8)], n_iter=1, verbose=1)
ae.fit(X)
def test_LifeCycle(self):
ae = AE(layers=[L("Sigmoid", units=8)])
del ae