import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from . import SyllableBaseModel
class Model(SyllableBaseModel):
def __init__(self, no_vocabs, embedding_dim=16, window_size=1):
super(Model, self).__init__()
self.input_size = 2*window_size+1
self.embeddings = nn.ModuleList([
nn.Embedding(
no_vocabs,
embedding_dim,
padding_idx=0
) for i in range(self.input_size)
])
self.pooling = nn.AvgPool1d(embedding_dim)
self.linear1 = nn.Linear(embedding_dim, 8)
self.linear2 = nn.Linear(8, 1)
def forward(self, inputs):
embeds = list(map(
lambda p: p[1](inputs[:, p[0]]), zip(range(self.input_size), self.embeddings)
))
x = torch.stack(embeds, 2)
x = self.pooling(x).view(x.size()[0], -1)
out = F.relu(self.linear1(x))
out = self.linear2(out)
return out
