def test_linear_regression():
inpt = T.matrix('inpt')
inpt.tag.test_value = np.zeros((3, 10))
inpt.tag.test_value
target = T.matrix('target')
target.tag.test_value = np.zeros((3, 2))
l = AffineNonlinear(inpt, 10, 2, 'tanh')
loss = squared(target, l.output).sum(1).mean()
m = SupervisedModel(inpt=inpt, target=target, output=l.output, loss=loss,
parameters=l.parameters)
f_predict = m.function([m.inpt], m.output)
f_loss = m.function([m.inpt, m.target], m.loss)
X = np.zeros((20, 10))
Z = np.zeros((20, 2))
Y = f_predict(X)
assert Y.shape == (20, 2), 'ouput has wrong shape'
l = f_loss(X, Z)
assert np.array(l).ndim == 0, 'loss is not a scalar'
def test_linear_regression():
inpt = T.matrix('inpt')
inpt.tag.test_value = np.zeros((3, 10))
inpt.tag.test_value
target = T.matrix('target')
target.tag.test_value = np.zeros((3, 2))
l = AffineNonlinear(inpt, 10, 2, 'tanh')
loss = squared(target, l.output).sum(1).mean()
m = SupervisedModel(inpt=inpt,
target=target,
output=l.output,
loss=loss,
parameters=l.parameters)
f_predict = m.function([m.inpt], m.output)
f_loss = m.function([m.inpt, m.target], m.loss)
X = np.zeros((20, 10))
Z = np.zeros((20, 2))
Y = f_predict(X)
assert Y.shape == (20, 2), 'ouput has wrong shape'
l = f_loss(X, Z)
assert np.array(l).ndim == 0, 'loss is not a scalar'
