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_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_parameter_set_init_overwrite():
pars = ParameterSet()
matrix = pars.declare((10, 10))
pars.alloc()
pars[matrix] = np.eye(10)
assert np.allclose(pars.data.reshape(pars[matrix].shape), pars[matrix])
def test_parameter_set_init_overwrite():
pars = ParameterSet()
matrix = pars.declare((10, 10))
pars.alloc()
pars[matrix] = np.eye(10)
assert np.allclose(pars.data.reshape(pars[matrix].shape), pars[matrix])
def test_parameter_set_init_declare():
pars = ParameterSet()
matrix = pars.declare((10, 10))
vector = pars.declare((10,))
pars.alloc()
assert pars.data.shape == (110,), 'wrong size for flat pars allocated'
assert (pars[matrix].shape == (10, 10)), ('wrong size for 2d array in pars '
'allocated')
assert (pars[vector].shape == (10,)), ('wrong size for 1d array in pars '
'allocated')
def test_parameter_set_init_declare():
pars = ParameterSet()
matrix = pars.declare((10, 10))
vector = pars.declare((10, ))
pars.alloc()
assert pars.data.shape == (110, ), 'wrong size for flat pars allocated'
assert (pars[matrix].shape == (10,
10)), ('wrong size for 2d array in pars '
'allocated')
assert (pars[vector].shape == (10, )), ('wrong size for 1d array in pars '
'allocated')
def test_parameter_set_data_change():
pars = ParameterSet()
matrix = pars.declare((10, 10))
vector = pars.declare((10,))
pars.alloc()
pars[matrix] = 0
pars[vector] = 0
assert (pars.data == 0).all(), repr(pars.data)
pars[matrix] += 1
assert pars.data.sum() == 100
pars[vector] += 2
assert pars.data.sum() == 120
pars.data *= 0.5
assert pars.data.sum() == 60
def test_transfer_stateful():
inpt = T.tensor3('inpt')
def t(s, x):
return T.zeros_like(s) + 1, x
t.stateful = True
P = ParameterSet()
r = Recurrent(inpt, 4, t, P.declare)
P.alloc()
assert hasattr(r, 'state')
f = theano.function([P.flat, inpt], r.state)
s = f(P.data, np.zeros((3, 1, 4)))
assert (s == 1).all(), 'hidden state has wrong value'
def test_transfer_insize_outsize():
inpt = T.tensor3('inpt')
def t(x):
return x
t.in_size = 2
t.out_size = 3
P = ParameterSet()
r = Recurrent(inpt, 4, t, P.declare)
P.alloc()
s = P[r.weights].shape
assert s == (12, 8), 'Shape is %s' % str(s)
s = P[r.initial].shape
assert s == (12,), 'Shape is %s' % str(s)
def test_parameter_set_data_change():
pars = ParameterSet()
matrix = pars.declare((10, 10))
vector = pars.declare((10, ))
pars.alloc()
pars[matrix] = 0
pars[vector] = 0
assert (pars.data == 0).all(), repr(pars.data)
pars[matrix] += 1
assert pars.data.sum() == 100
pars[vector] += 2
assert pars.data.sum() == 120
pars.data *= 0.5
assert pars.data.sum() == 60
def test_transfer_stateful():
inpt = T.tensor3('inpt')
def t(s, x):
return T.zeros_like(s) + 1, x
t.stateful = True
P = ParameterSet()
r = Recurrent(inpt, 4, t, P.declare)
P.alloc()
assert hasattr(r, 'state')
f = theano.function([P.flat, inpt], r.state)
s = f(P.data, np.zeros((3, 1, 4)))
assert (s == 1).all(), 'hidden state has wrong value'
def test_transfer_insize_outsize():
inpt = T.tensor3('inpt')
def t(x):
return x
t.in_size = 2
t.out_size = 3
P = ParameterSet()
r = Recurrent(inpt, 4, t, P.declare)
P.alloc()
s = P[r.weights].shape
assert s == (12, 8), 'Shape is %s' % str(s)
s = P[r.initial].shape
assert s == (12, ), 'Shape is %s' % str(s)
def test_model_function():
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
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():
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
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"
