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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(
self,
pretrained_embedding=None,
freeze_embedding=False,
vocab_size=None,
embedding_dim=None,
n_filters=None,
filter_sizes=None,
output_dim=None,
dropout=None):
super().__init__()
if pretrained_embedding is not None:
self.vocab_size, self.embedding_dim = pretrained_embedding.shape
self.embedding = nn.Embedding.from_pretrained(
pretrained_embedding, freeze=freeze_embedding)
else:
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.convs = nn.ModuleList([
nn.Conv1d(in_channels=embedding_dim,
out_channels=n_filters,
kernel_size=fs)
for fs in filter_sizes
])
self.fc = nn.Linear(len(filter_sizes) * n_filters, output_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, text):
# text = [batch size, sent len]
embedded = self.embedding(text)
# embedded = [batch size, sent len, emb dim]
embedded = embedded.permute(0, 2, 1)
# embedded = [batch size, emb dim, sent len]
conved = [F.relu(conv(embedded)) for conv in self.convs]
# conved_n = [batch size, n_filters, sent len - filter_sizes[n] + 1]
pooled = [F.max_pool1d(conv, conv.shape[2]).squeeze(2)
for conv in conved]
# pooled_n = [batch size, n_filters]
cat = self.dropout(torch.cat(pooled, dim=1))
# cat = [batch size, n_filters * len(filter_sizes)]
return self.fc(cat)
def reset_weights(m):
# Try resetting model weights to avoid
# weight leakage.
for layer in m.children():
if hasattr(layer, 'reset_parameters'):
print(f'Reset trainable parameters of layer = {layer}')
layer.reset_parameters()