title: What is the meaning of hidden_dim and embed_size in LSTM?
body: <p>I am trying to learn RNN and LSTM. I cam across a tutorial for sentiment analysis. Below is the code I in the tutorial where word2idx is a dictionary with word to index mapping</p>
  
  <pre><code>    class SentimentNet(nn.Module):
          def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim, n_layers, drop_prob=0.5):
          super(SentimentNet, self).__init__()
          self.output_size = output_size
          self.n_layers = n_layers
          self.hidden_dim = hidden_dim
  
          self.embedding = nn.Embedding(vocab_size, embedding_dim)
          self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers, dropout=drop_prob, batch_first=True)
          self.dropout = nn.Dropout(drop_prob)
          self.fc = nn.Linear(hidden_dim, output_size)
          self.sigmoid = nn.Sigmoid()
  
       vocab_size = len(word2idx) + 1
       output_size = 1
       embedding_dim = 400
       hidden_dim = 512
       n_layers = 2
  </code></pre>
  
  <p>Can anyone please tell me the meaning of vocal_size, embedding_dim, hidden_dim?</p>