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',
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)
