torch_model_basic.py

quora_torch_model

class Model(nn.Module):
    def __init__(self, embedding_matrix, hidden_unit = 64):
        super(Model, self).__init__();
        vocab_size = embeddings_tensor.shape[0];
        embedding_dim = embeddings_tensor.shape[1];
        
        self.embedding_layer = nn.Embedding(vocab_size, embedding_dim);
        self.embedding_layer.weight = nn.Parameter(embeddings_tensor);
        self.embedding_layer.weight.requires_grad = True;
        
        self.lstm_1 = nn.LSTM(embedding_dim, hidden_unit, bidirectional=True);
        
        self.fc_1 = nn.Linear(hidden_unit*2, hidden_unit*2);
        
        self.lstm_2 = nn.LSTM(hidden_unit*2, hidden_unit, bidirectional=True);
        
        self.fc_2 = nn.Linear(hidden_unit * 2 * 2, 1);
        
    def forward(self, x):
        out = self.embedding_layer(x);
        
        out, _ = self.lstm_1(out);
        
        out = self.fc_1(out);
        
        out = torch.relu(out);
        
        out, _ = self.lstm_2(out);
        
        out_avg, out_max = torch.mean(out, 1), torch.max(out, 1)[0];
        out = torch.cat((out_avg, out_max), 1);
        
        out = self.fc_2(out);
        return out;