def test_model_function():
pars = ParameterSet(weights=(2, 3))
inpt = T.matrix()
output = T.dot(inpt, pars.weights)
pars.data[...] = np.random.standard_normal(pars.data.shape)
model = Model()
model.exprs = {'inpt': inpt, 'output': output}
model.parameters = pars
f = model.function(['inpt'], 'output')
fx = model.function(['inpt'], 'output', explicit_pars=True)
np.random.seed(1010)
test_inpt = np.random.random((10, 2)).astype(theano.config.floatX)
r1 = f(test_inpt)
r2 = fx(pars.data, test_inpt)
print r1
print r2
correct = np.allclose(r1, r2)
assert correct, 'implicit pars and explicit pars have different output'
f1 = model.function(['inpt'], ['output'])
f2 = model.function([inpt], ['output'])
f3 = model.function([inpt], [output])
f4 = model.function(['inpt'], [output])
assert np.allclose(f1(test_inpt), f2(test_inpt)), "f1 and f2 don't agree"
assert np.allclose(f1(test_inpt), f3(test_inpt)), "f1 and f3 don't agree"
assert np.allclose(f1(test_inpt), f4(test_inpt)), "f1 and f4 don't agree"
assert np.allclose(f2(test_inpt), f3(test_inpt)), "f2 and f3 don't agree"
assert np.allclose(f2(test_inpt), f4(test_inpt)), "f2 and f4 don't agree"
assert np.allclose(f3(test_inpt), f4(test_inpt)), "f3 and f4 don't agree"
def test_model_function_mode():
pars = ParameterSet()
weights = pars.declare((2, 3))
pars.alloc()
inpt = T.matrix()
output = T.dot(inpt, weights)
pars.data[...] = np.random.standard_normal(pars.data.shape)
model = Model()
model.exprs = {'inpt': inpt, 'output': output}
model.parameters = pars
mode = theano.Mode()
f = model.function(['inpt'], 'output', mode=mode)
actual_mode = f.theano_func.maker.mode
assert actual_mode is mode, 'wrong mode: %s' % actual_mode
model.mode = theano.Mode()
f = model.function(['inpt'], 'output')
actual_mode = f.theano_func.maker.mode
# Maybe a weird way to compare modes, but it seems to get the job done.
equal = actual_mode.__dict__ == mode.__dict__
assert equal, 'wrong mode: (%s != %s)' % (actual_mode, mode)
def test_pickling_models():
ma = T.matrix()
m = Model()
m.parameters = ParameterSet(bla=2)
m.exprs = {'m_sqrd': m.parameters.bla.sum() * ma.sum()}
m.f = m.function([ma], 'm_sqrd', explicit_pars=False)
cPickle.dumps(m)
def test_nested_exprs():
ma = T.matrix()
m = Model()
m.parameters = ParameterSet()
bla = m.parameters.declare(2)
m.parameters.alloc()
m.parameters[bla] = 1, 2
m.exprs = {
'norms': {
'l1': abs(ma).sum(),
'l2': T.sqrt((ma**2).sum()),
},
'ma_multiplied': [ma, 2 * ma],
'bla': bla,
'blubb': 1,
}
f = m.function([], 'bla', explicit_pars=False, on_unused_input='ignore')
assert np.allclose(f(), [1, 2])
f = m.function([ma], ('norms', 'l1'),
explicit_pars=False,
on_unused_input='ignore')
assert f([[-1, 1]]) == 2
f = m.function([ma], ('norms', 'l2'),
explicit_pars=False,
on_unused_input='ignore')
assert np.allclose(f([[-1, 1]]), np.sqrt(2.))
f = m.function([ma], ('ma_multiplied', 0),
explicit_pars=False,
on_unused_input='ignore')
assert np.allclose(f([[-1, 1]]), [-1, 1])
f = m.function([ma], ('ma_multiplied', 1),
explicit_pars=False,
on_unused_input='ignore')
assert np.allclose(f([[-1, 1]]), [-2, 2])