| from torch.nn.modules.module import Module |
| import torch.nn.functional as F |
|
|
| from isrlu import ISRLU, ISRU_sigmoid, ISRU_tanh |
| from bipolar import Bipolar |
| from softexp import SoftExp |
| from ternary import TernaryTanh |
| activations = [ISRLU, ISRU_sigmoid, ISRU_tanh, Bipolar, SoftExp, TernaryTanh] |
|
|
|
|
| |
| class RNNCellBase(Module): |
| def __repr__(self): |
| s = '{name}({input_size}, {hidden_size}' |
| if 'bias' in self.__dict__ and self.bias is not True: |
| s += ', bias={bias}' |
| if 'nonlinearity' in self.__dict__ and self.nonlinearity != "tanh": |
| s += ', nonlinearity={nonlinearity}' |
| s += ')' |
| return s.format(name=self.__class__.__name__, **self.__dict__) |
|
|
| def get_activation(self, activation): |
| if hasattr(activations, activation): |
| return getattr(activations, activation) |
| return getattr(F, activation) |
|
|
| def check_forward_input(self, input_data): |
| if input_data.size(1) != self.input_size: |
| raise RuntimeError( |
| "input has inconsistent input_size: got {}, expected {}".format( |
| input_data.size(1), self.input_size)) |
|
|
| def check_forward_hidden(self, input_data, hx, hidden_label=''): |
| if input_data.size(0) != hx.size(0): |
| raise RuntimeError( |
| "Input batch size {} doesn't match hidden{} batch size {}".format( |
| input_data.size(0), hidden_label, hx.size(0))) |
|
|
| if hx.size(1) != self.hidden_size: |
| raise RuntimeError( |
| "hidden{} has inconsistent hidden_size: got {}, expected {}".format( |
| hidden_label, input_data.size(1), self.input_size)) |
| |
|
|
