def __init__(
self,
input_dim,
h0_dim,
s0_dim,
h1_dim,
output_dim,
):
super(SeqToSeqLSTM, self).__init__()
self.h0__input = MLP(
[Tanh()],
dims=[
input_dim,
h0_dim
],
weights_init=init.IsotropicGaussian(0.01),
biases_init=init.IsotropicGaussian(0.3),
name='MLP:h0__input'
)
self.s0__h0_input = LSTMLayer(
input_dim=h0_dim + input_dim,
state_dim=s0_dim,
name='LSTMLayer:s0__h0_input'
)
self.h1__s0_h0_input = MLP(
[Tanh()],
dims=[
s0_dim + h0_dim + input_dim,
h1_dim
],
weights_init=init.IsotropicGaussian(0.01),
biases_init=init.Constant(0.0),
name='MLP:h1__s0_h0_input'
)
self.output__h1_s0_h0_input = Linear(
input_dim=h1_dim + s0_dim + h0_dim + input_dim,
output_dim=output_dim,
weights_init=init.IsotropicGaussian(0.01),
biases_init=init.Constant(0.0),
name='Linear:output__h1_s0_h0_input'
)
self.children = [
self.h0__input,
self.s0__h0_input,
self.h1__s0_h0_input,
self.output__h1_s0_h0_input
]
class SeqToSeqLSTM(Initializable):
def __init__(
self,
input_dim,
h0_dim,
s0_dim,
h1_dim,
output_dim,
):
super(SeqToSeqLSTM, self).__init__()
self.h0__input = MLP(
[Tanh()],
dims=[
input_dim,
h0_dim
],
weights_init=init.IsotropicGaussian(0.01),
biases_init=init.IsotropicGaussian(0.3),
name='MLP:h0__input'
)
self.s0__h0_input = LSTMLayer(
input_dim=h0_dim + input_dim,
state_dim=s0_dim,
name='LSTMLayer:s0__h0_input'
)
self.h1__s0_h0_input = MLP(
[Tanh()],
dims=[
s0_dim + h0_dim + input_dim,
h1_dim
],
weights_init=init.IsotropicGaussian(0.01),
biases_init=init.Constant(0.0),
name='MLP:h1__s0_h0_input'
)
self.output__h1_s0_h0_input = Linear(
input_dim=h1_dim + s0_dim + h0_dim + input_dim,
output_dim=output_dim,
weights_init=init.IsotropicGaussian(0.01),
biases_init=init.Constant(0.0),
name='Linear:output__h1_s0_h0_input'
)
self.children = [
self.h0__input,
self.s0__h0_input,
self.h1__s0_h0_input,
self.output__h1_s0_h0_input
]
def apply(self, input_):
h0 = self.h0__input.apply(input_)
s0 = self.s0__h0_input.apply(
tensor.concatenate([h0, input_], axis=2)
)
h1 = self.h1__s0_h0_input.apply(
tensor.concatenate([s0, h0, input_], axis=2)
)
output = self.output__h1_s0_h0_input.apply(
tensor.concatenate([h1, s0, h0, input_], axis=2)
)
return output