deep_combine_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, 
                   dropout=False, keep_prob=None, noise_level=None,
                   num_frames=None,
                   **unused_params):

    num_supports = FLAGS.num_supports
    num_layers = FLAGS.deep_chain_layers
    relu_cells = FLAGS.deep_chain_relu_cells
    relu_type = FLAGS.deep_chain_relu_type
    use_length = FLAGS.deep_chain_use_length

    next_input = model_input
    support_predictions = []
    for layer in xrange(num_layers):
      sub_prediction = self.sub_model(next_input, vocab_size, sub_scope=sub_scope+"prediction-%d"%layer, dropout=dropout, keep_prob=keep_prob, noise_level=noise_level)
      sub_activation = slim.fully_connected(
          sub_prediction,
          relu_cells,
          activation_fn=None,
          weights_regularizer=slim.l2_regularizer(l2_penalty),
          scope=sub_scope+"relu-%d"%layer)

      if relu_type == "elu":
        sub_relu = tf.nn.elu(sub_activation)
      else: # default: relu
        sub_relu = tf.nn.relu(sub_activation)

      if noise_level is not None:
        print "adding noise to sub_relu, level = ", noise_level
        sub_relu = sub_relu + tf.random_normal(tf.shape(sub_relu), mean=0.0, stddev=noise_level)

      relu_norm = tf.nn.l2_normalize(sub_relu, dim=1)
      next_input = tf.concat([next_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}