mirror of
https://github.com/wassname/Castor.git
synced 2026-09-28 13:42:00 +08:00
gauravbaruah
f91f105568
Because of random word embedding for out of vocabulary words, the performance of the final saved model was variable. This is now fixed.
88 lines
2.6 KiB
Python
88 lines
2.6 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.autograd import Variable
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import numpy as np
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import os
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# logging setup
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import logging
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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ch = logging.StreamHandler()
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ch.setLevel(logging.DEBUG)
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formatter = logging.Formatter('%(levelname)s - %(message)s')
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ch.setFormatter(formatter)
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logger.addHandler(ch)
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class QAModel(nn.Module):
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@staticmethod
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def save(model, out_folder, model_fname):
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torch.save(model, os.path.join(out_folder, model_fname))
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@staticmethod
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def load(in_folder, model_fname):
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return torch.load(os.path.join(in_folder, model_fname))
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def __init__(self, input_n_dim, filter_width, conv_filters=100, no_ext_feats=False, ext_feats_size=4, n_classes=2):
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super(QAModel, self).__init__()
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self.no_ext_feats = no_ext_feats
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self.conv_channels = conv_filters
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n_hidden = 2*self.conv_channels + 1
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self.conv_q = nn.Sequential(
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nn.Conv1d(input_n_dim, self.conv_channels, filter_width, padding=filter_width-1),
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nn.Tanh()
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)
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self.conv_a = nn.Sequential(
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nn.Conv1d(input_n_dim, self.conv_channels, filter_width, padding=filter_width-1),
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nn.Tanh()
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)
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self.combined_feature_vector = nn.Linear(2*self.conv_channels + (0 if no_ext_feats else ext_feats_size), n_hidden)
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#TODO: add +1 to Linear layer^. Will need change in forward function
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self.combined_features_activation = nn.Tanh()
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self.dropout = nn.Dropout(0.5)
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self.hidden = nn.Linear(n_hidden, n_classes)
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self.logsoftmax = nn.LogSoftmax()
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def forward(self, question, answer, ext_feats):
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q = self.conv_q.forward(question)
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q = F.max_pool1d(q, q.size()[2])
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q = q.view(-1, self.conv_channels)
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# logger.debug('forward q: {}'.format(q))
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a = self.conv_a.forward(answer)
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a = F.max_pool1d(a, a.size()[2])
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a = a.view(-1, self.conv_channels)
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x = None
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if self.no_ext_feats:
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x = torch.cat([q, a], 1)
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# logger.debug('no_ext_feats')
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else:
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x = torch.cat([q, a, ext_feats], 1)
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# logger.debug('with ext_feats')
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# logger.debug('featvec x: {}'.format(x))
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# logger.debug(x.creator)
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x = self.combined_feature_vector.forward(x)
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x = self.combined_features_activation.forward(x)
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x = self.dropout(x)
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x = self.hidden(x)
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x = self.logsoftmax(x)
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return x
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