hidden_chain_model.py 文件源码

def create_model(self, model_input, vocab_size, num_mixtures=None,
                   l2_penalty=1e-8, sub_scope="", original_input=None, **unused_params):
    num_supports = FLAGS.num_supports
    num_layers = FLAGS.hidden_chain_layers
    relu_cells = FLAGS.hidden_chain_relu_cells

    next_input = model_input
    support_predictions = []
    for layer in xrange(num_layers):
      sub_relu = slim.fully_connected(
          next_input,
          relu_cells,
          activation_fn=tf.nn.relu,
          weights_regularizer=slim.l2_regularizer(l2_penalty),
          scope=sub_scope+"relu-%d"%layer)
      sub_prediction = self.sub_model(sub_relu, vocab_size, sub_scope=sub_scope+"prediction-%d"%layer)
      relu_norm = tf.nn.l2_normalize(sub_relu, dim=1)
      next_input = tf.concat([model_input, relu_norm], axis=1)
      support_predictions.append(sub_prediction)
    main_predictions = self.sub_model(next_input, vocab_size, sub_scope=sub_scope+"-main")
    support_predictions = tf.concat(support_predictions, axis=1)
    return {"predictions": main_predictions, "support_predictions": support_predictions}