pyabsa.networks.dynamic_rnn

Module Contents

Classes

DynamicLSTM

Base class for all neural network modules.

class pyabsa.networks.dynamic_rnn.DynamicLSTM(input_size, hidden_size, num_layers=1, bias=True, batch_first=True, dropout=0, bidirectional=False, only_use_last_hidden_state=False, rnn_type='LSTM')

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

forward(x, x_len)

sequence -> sort -> pad and pack ->process using RNN -> unpack ->unsort

Parameters:

• x – sequence embedding vectors

• x_len – numpy/tensor list

Returns: