from neuralogic.core.constructs.factories import R, V
from neuralogic.core.constructs.function import Combination, Transformation
from neuralogic.nn.module.general.rnn import RNNCell
from neuralogic.nn.module.module import Module
[docs]
class LSTMCell(Module):
r"""
Parameters
----------
input_size : int
Input feature size.
hidden_size : int
Output and hidden feature size.
output_name : str
Output (head) predicate name of the module.
input_name : str
Input feature predicate name to get features from.
hidden_input_name : str
Predicate name to get hidden state from.
cell_state_0_name : str
Predicate name to get initial cell state from.
arity : int
Arity of the input and output predicate. Default: ``1``
"""
def __init__(
self,
input_size: int,
hidden_size: int,
output_name: str,
input_name: str,
hidden_input_name: str,
cell_state_0_name: str,
arity: int = 1,
):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_name = output_name
self.input_name = input_name
self.hidden_input_name = hidden_input_name
self.cell_state_0_name = cell_state_0_name
self.arity = arity
def __call__(self):
terms = [f"X{i}" for i in range(self.arity)]
z_terms = [*terms, V.Z]
t_terms = [*terms, V.T]
i_name = f"{self.output_name}__i"
f_name = f"{self.output_name}__f"
g_name = f"{self.output_name}__n"
o_name = f"{self.output_name}__o"
c_left_name = f"{self.output_name}__left"
c_right_name = f"{self.output_name}__right"
c_name = f"{self.output_name}__c"
next_rel = R.special.next(V.Z, V.T)
cell_args = [
self.input_size,
self.hidden_size,
i_name,
self.input_name,
self.hidden_input_name,
Transformation.SIGMOID,
self.arity,
]
i = RNNCell(*cell_args)
cell_args[2] = f_name
f = RNNCell(*cell_args)
cell_args[2] = o_name
o = RNNCell(*cell_args)
cell_args[2] = g_name
cell_args[-2] = Transformation.TANH
g = RNNCell(*cell_args)
c_left = R.get(c_left_name)(t_terms) <= (R.get(f_name)(t_terms), R.get(c_name)(z_terms), next_rel)
c_right = R.get(c_right_name)(t_terms) <= (R.get(i_name)(t_terms), R.get(g_name)(t_terms))
c = R.get(c_name)(t_terms) <= (R.get(c_left_name)(t_terms), R.get(c_right_name)(t_terms))
h = R.get(self.output_name)(t_terms) <= (R.get(o_name)(t_terms), Transformation.TANH(R.get(c_name)(t_terms)))
return [
*i(),
*f(),
*o(),
*g(),
c_left | [Combination.ELPRODUCT],
c_right | [Combination.ELPRODUCT],
c,
R.get(c_name)([*terms, 0]) <= R.get(self.cell_state_0_name)(terms),
h | [Combination.ELPRODUCT],
]
[docs]
class LSTM(Module):
r"""
One-layer Long Short-Term Memory (LSTM) RNN module which is computed as:
.. math::
i_t = \sigma(\mathbf{W}_{xi} \mathbf{x}_t + \mathbf{W}_{hi} \mathbf{h}_{t-1})
.. math::
f_t = \sigma(\mathbf{W}_{xf} \mathbf{x}_t + \mathbf{W}_{hf} \mathbf{h}_{t-1})
.. math::
o_t = \sigma(\mathbf{W}_{xo} \mathbf{x}_t + \mathbf{W}_{ho} \mathbf{h}_{t-1})
.. math::
g_t = \tanh(\mathbf{W}_{xg} \mathbf{x}_t + \mathbf{W}_{hg} \mathbf{h}_{t-1}) \\
.. math::
c_t = f_t \odot c_{t-1} + i_t \odot g_t
.. math::
h_t = o_t \odot \tanh(c_t)
Parameters
----------
input_size : int
Input feature size.
hidden_size : int
Output and hidden feature size.
output_name : str
Output (head) predicate name of the module.
input_name : str
Input feature predicate name to get features from.
hidden_0_name : str
Predicate name to get initial hidden state from.
cell_state_0_name : str
Predicate name to get initial cell state from.
arity : int
Arity of the input and output predicate. Default: ``1``
"""
def __init__(
self,
input_size: int,
hidden_size: int,
output_name: str,
input_name: str,
hidden_0_name: str,
cell_state_0_name: str,
arity: int = 1,
):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_name = output_name
self.input_name = input_name
self.hidden_0_name = hidden_0_name
self.cell_state_0_name = cell_state_0_name
self.arity = arity
def __call__(self):
recursive_cell = LSTMCell(
self.input_size,
self.hidden_size,
self.output_name,
self.input_name,
self.output_name,
self.cell_state_0_name,
self.arity,
)
terms = [f"X{i}" for i in range(self.arity)]
return [
R.get(self.output_name)([*terms, 0]) <= R.get(self.hidden_0_name)(terms),
*recursive_cell(),
]