nn-lm-batch.py 文件源码

def calc_score_of_histories(words, dropout=0.0):
  # This will change from a list of histories, to a list of words in each history position
  words = np.transpose(words)
  # Lookup the embeddings and concatenate them
  emb = dy.concatenate([dy.lookup_batch(W_emb, x) for x in words])
  # Create the hidden layer
  W_h = dy.parameter(W_h_p)
  b_h = dy.parameter(b_h_p)
  h = dy.tanh(dy.affine_transform([b_h, W_h, emb]))
  # Perform dropout
  if dropout != 0.0:
    h = dy.dropout(h, dropout)
  # Calculate the score and return
  W_sm = dy.parameter(W_sm_p)
  b_sm = dy.parameter(b_sm_p)
  return dy.affine_transform([b_sm, W_sm, h])

# Calculate the loss value for the entire sentence