def __init__(self, *args, **kwargs):
super(TestRecursive, self).__init__(*args, **kwargs)
self.input_dim = 2
self.state_dim = 2
self.model = Recursive(return_sequences=True)
self.model.add_input('input', ndim=3) # Input is 3D tensor
self.model.add_state('h', dim=self.state_dim)
self.model.add_node(Dense(self.input_dim + self.state_dim,
self.state_dim,
init='one'),
name='rec',
inputs=['input', 'h'],
return_state='h')
self.model.add_node(Activation('linear'),
name='out',
input='rec',
create_output=True)
self.model2 = Sequential()
self.model2.add(
SimpleRNN(input_dim=self.input_dim,
activation='linear',
inner_init='one',
output_dim=self.state_dim,
init='one',
return_sequences=True))
def __init__(self, *args, **kwargs):
super(TestOrthoRNN, self).__init__(*args, **kwargs)
self.input_dim = 2
self.state_dim = 2
self.model = Recursive(return_sequences=True)
self.model.add_input('input', ndim=3) # Input is 3D tensor
self.model.add_state('h', dim=self.state_dim)
self.model.add_node(Dense(self.input_dim, self.state_dim,
init='one'), name='i2h',
input='input')
self.model.add_node(Dense(self.state_dim, self.state_dim,
init='orthogonal'), name='h2h',
inputs='h')
self.model.add_node(Lambda(lambda x: x), name='rec',
inputs=['i2h', 'h2h'], merge_mode='sum',
return_state='h',
create_output=True)
self.model2 = Sequential()
self.model2.add(SimpleRNN(input_dim=self.input_dim, activation='linear',
inner_init='one',
output_dim=self.state_dim, init='one',
return_sequences=True))
U = self.model.nodes['h2h'].W.get_value()
self.model2.layers[0].U.set_value(U)
def __init__(self, *args, **kwargs):
super(TestOrthoRNN, self).__init__(*args, **kwargs)
self.input_dim = 2
self.state_dim = 2
self.model = Recursive(return_sequences=True)
self.model.add_input('input', ndim=3) # Input is 3D tensor
self.model.add_state('h', dim=self.state_dim)
self.model.add_node(Dense(self.input_dim, self.state_dim, init='one'),
name='i2h',
inputs=[
'input',
])
self.model.add_node(Dense(self.state_dim,
self.state_dim,
init='orthogonal'),
name='h2h',
inputs=[
'h',
])
self.model.add_node(Lambda(lambda x: x),
name='rec',
inputs=['i2h', 'h2h'],
merge_mode='sum',
return_state='h',
create_output=True)
self.model2 = Sequential()
self.model2.add(
SimpleRNN(input_dim=self.input_dim,
activation='linear',
inner_init='one',
output_dim=self.state_dim,
init='one',
return_sequences=True))
U = self.model.nodes['h2h'].W.get_value()
self.model2.layers[0].U.set_value(U)
def __init__(self, *args, **kwargs):
super(TestRecursive, self).__init__(*args, **kwargs)
self.input_dim = 2
self.state_dim = 2
self.model = Recursive(return_sequences=True)
self.model.add_input('input', ndim=3) # Input is 3D tensor
self.model.add_state('h', dim=self.state_dim)
self.model.add_node(Dense(self.input_dim + self.state_dim, self.state_dim,
init='one'), name='rec',
inputs=['input', 'h'],
return_state='h')
self.model.add_node(Activation('linear'), name='out', input='rec',
create_output=True)
self.model2 = Sequential()
self.model2.add(SimpleRNN(input_dim=self.input_dim, activation='linear',
inner_init='one',
output_dim=self.state_dim, init='one',
return_sequences=True))
class TestOrthoRNN(unittest.TestCase):
def __init__(self, *args, **kwargs):
super(TestOrthoRNN, self).__init__(*args, **kwargs)
self.input_dim = 2
self.state_dim = 2
self.model = Recursive(return_sequences=True)
self.model.add_input('input', ndim=3) # Input is 3D tensor
self.model.add_state('h', dim=self.state_dim)
self.model.add_node(Dense(self.input_dim, self.state_dim,
init='one'), name='i2h',
input='input')
self.model.add_node(Dense(self.state_dim, self.state_dim,
init='orthogonal'), name='h2h',
inputs='h')
self.model.add_node(Lambda(lambda x: x), name='rec',
inputs=['i2h', 'h2h'], merge_mode='sum',
return_state='h',
create_output=True)
self.model2 = Sequential()
self.model2.add(SimpleRNN(input_dim=self.input_dim, activation='linear',
inner_init='one',
output_dim=self.state_dim, init='one',
return_sequences=True))
U = self.model.nodes['h2h'].W.get_value()
self.model2.layers[0].U.set_value(U)
def test_step(self):
XX = T.matrix()
HH = T.matrix()
A = self.model._step(XX, HH)
F = function([XX, HH], A, allow_input_downcast=True)
x = np.ones((1, 2))
h = np.ones((1, 2))
y = F(x, h)
assert(y[-1].shape == (1, 2))
def test__get_output(self):
X = self.model.get_input()
Y = self.model._get_output()
F = function([X], Y, allow_input_downcast=True)
x = np.ones((3, 5, self.input_dim)).astype(floatX)
y = F(x)
print y
X2 = self.model2.get_input()
Y2 = self.model2.get_output()
F2 = function([X2], Y2)
y2 = F2(x)
assert_allclose(y2, y[-1])
def test_get_output(self):
X = self.model.get_input()
Y = self.model.get_output()
F = function([X], Y, allow_input_downcast=True)
x = np.ones((3, 5, self.input_dim)).astype(floatX)
y = F(x)
X2 = self.model2.get_input()
Y2 = self.model2.get_output()
F2 = function([X2], Y2)
y2 = F2(x)
print "y-length: {}".format(len(y))
assert_allclose(y2, y)
class TestRecursive(unittest.TestCase):
def __init__(self, *args, **kwargs):
super(TestRecursive, self).__init__(*args, **kwargs)
self.input_dim = 2
self.state_dim = 2
self.model = Recursive(return_sequences=True)
self.model.add_input('input', ndim=3) # Input is 3D tensor
self.model.add_state('h', dim=self.state_dim)
self.model.add_node(Dense(self.input_dim + self.state_dim, self.state_dim,
init='one'), name='rec',
inputs=['input', 'h'],
return_state='h')
self.model.add_node(Activation('linear'), name='out', input='rec',
create_output=True)
self.model2 = Sequential()
self.model2.add(SimpleRNN(input_dim=self.input_dim, activation='linear',
inner_init='one',
output_dim=self.state_dim, init='one',
return_sequences=True))
def test_step(self):
XX = T.matrix()
HH = T.matrix()
A = self.model._step(XX, HH)
F = function([XX, HH], A, allow_input_downcast=True)
x = np.ones((1, 2))
h = np.ones((1, 2))
y = F(x, h)
r = np.asarray([[4., 4.]])
assert_allclose([r, r], y)
def test_get_get_output(self):
X = self.model.get_input()
Y = self.model._get_output()
F = function([X], Y, allow_input_downcast=True)
x = np.ones((3, 5, self.input_dim)).astype(floatX)
y = F(x)
print y
X2 = self.model2.get_input()
Y2 = self.model2.get_output()
F2 = function([X2], Y2)
y2 = F2(x)
assert_allclose(y2, y[1])
class TestOrthoRNN(unittest.TestCase):
def __init__(self, *args, **kwargs):
super(TestOrthoRNN, self).__init__(*args, **kwargs)
self.input_dim = 2
self.state_dim = 2
self.model = Recursive(return_sequences=True)
self.model.add_input('input', ndim=3) # Input is 3D tensor
self.model.add_state('h', dim=self.state_dim)
self.model.add_node(Dense(self.input_dim, self.state_dim, init='one'),
name='i2h',
inputs=[
'input',
])
self.model.add_node(Dense(self.state_dim,
self.state_dim,
init='orthogonal'),
name='h2h',
inputs=[
'h',
])
self.model.add_node(Lambda(lambda x: x),
name='rec',
inputs=['i2h', 'h2h'],
merge_mode='sum',
return_state='h',
create_output=True)
self.model2 = Sequential()
self.model2.add(
SimpleRNN(input_dim=self.input_dim,
activation='linear',
inner_init='one',
output_dim=self.state_dim,
init='one',
return_sequences=True))
U = self.model.nodes['h2h'].W.get_value()
self.model2.layers[0].U.set_value(U)
def test_step(self):
XX = T.matrix()
HH = T.matrix()
A = self.model._step(XX, HH)
F = function([XX, HH], A, on_unused_input='warn')
x = np.ones((1, 2))
h = np.ones((1, 2))
y = F(x, h)
assert (y[-1].shape == (1, 2))
def test_get_get_output(self):
X = self.model.get_input()
Y = self.model._get_output()
F = function([X], Y, allow_input_downcast=True)
x = np.ones((3, 5, self.input_dim))
y = F(x)
print y
X2 = self.model2.get_input()
Y2 = self.model2.get_output()
F2 = function([X2], Y2)
y2 = F2(x)
assert_allclose(y2, y[-1])
class TestRecursive(unittest.TestCase):
def __init__(self, *args, **kwargs):
super(TestRecursive, self).__init__(*args, **kwargs)
self.input_dim = 2
self.state_dim = 2
self.model = Recursive(return_sequences=True)
self.model.add_input('input', ndim=3) # Input is 3D tensor
self.model.add_state('h', dim=self.state_dim)
self.model.add_node(Dense(self.input_dim + self.state_dim,
self.state_dim,
init='one'),
name='rec',
inputs=['input', 'h'],
return_state='h')
self.model.add_node(Activation('linear'),
name='out',
input='rec',
create_output=True)
self.model2 = Sequential()
self.model2.add(
SimpleRNN(input_dim=self.input_dim,
activation='linear',
inner_init='one',
output_dim=self.state_dim,
init='one',
return_sequences=True))
def test_step(self):
XX = T.matrix()
HH = T.matrix()
A = self.model._step(XX, HH)
F = function([XX, HH], A)
x = np.ones((1, 2))
h = np.ones((1, 2))
y = F(x, h)
r = np.asarray([[4., 4.]])
assert_allclose([r, r], y)
def test_get_get_output(self):
X = self.model.get_input()
Y = self.model._get_output()
F = function([X], Y, allow_input_downcast=True)
x = np.ones((3, 5, self.input_dim))
y = F(x)
print y
X2 = self.model2.get_input()
Y2 = self.model2.get_output()
F2 = function([X2], Y2)
y2 = F2(x)
assert_allclose(y2, y[1])
