Rnn

GenericRNN

Bases: Module

Source code in src/notochord/rnn.py

class GenericRNN(nn.Module):
    kind_cls = {
        'gru':GRU,
        'lstm':LSTM,
        'elman':RNN,
        'exprnn':ExpRNN
        }
    def __init__(self, kind, *a, **kw):
        super().__init__()
        if kw.get('bidirectional'): raise ValueError("""
            bidirectional GenericRNN not supported.
            """)
        cls = GenericRNN.kind_cls[kind]
        self.kind = kind
        self.rnn = cls(*a, **kw)

    def __getattr__(self, a):
        try:
            return  super().__getattr__(a)
        except AttributeError:
            return getattr(self.rnn, a)

    def forward(self, x, initial_state):
        """
        Args:
            x: Tensor[batch x time x channel] if batch_first else [time x batch x channel]
            initial_state: List[Tensor[layers x batch x hidden]]], list of components 
            with 0 being hidden state (e.g. 1 is cell state for LSTM). 
        Returns:
            hidden: hidden states of top layers Tensor[batch x time x hidden]
                or [time x batch x hidden]
            new_states: List[Tensor[layers x batch x hidden]]
        """
        hidden, final_state = self.rnn.forward(x, initial_state)  #forward or __call__?
        return hidden, final_state

forward(x, initial_state)

Parameters:

Name	Type	Description	Default
x		Tensor[batch x time x channel] if batch_first else [time x batch x channel]	required
initial_state		List[Tensor[layers x batch x hidden]]], list of components	required

Returns: hidden: hidden states of top layers Tensor[batch x time x hidden] or [time x batch x hidden] new_states: List[Tensor[layers x batch x hidden]]

Source code in src/notochord/rnn.py

def forward(self, x, initial_state):
    """
    Args:
        x: Tensor[batch x time x channel] if batch_first else [time x batch x channel]
        initial_state: List[Tensor[layers x batch x hidden]]], list of components 
        with 0 being hidden state (e.g. 1 is cell state for LSTM). 
    Returns:
        hidden: hidden states of top layers Tensor[batch x time x hidden]
            or [time x batch x hidden]
        new_states: List[Tensor[layers x batch x hidden]]
    """
    hidden, final_state = self.rnn.forward(x, initial_state)  #forward or __call__?
    return hidden, final_state

rnn_shim(cls)

LSTM API for GRU and RNN.

hidden state is first element of state tuple

Source code in src/notochord/rnn.py

def rnn_shim(cls):
    """LSTM API for GRU and RNN.

    hidden state is first element of state tuple"""
    class shim(cls):
        def forward(self, input, states=(None,)):
            assert len(states)==1
            out, h = super().forward(input, *states)
            return out, (h,)
    return shim