easy_rec/python/model/dssm.py [58:106]:
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    item_tower_emb = tf.layers.dense(
        inputs=item_tower_emb,
        units=last_item_hidden,
        kernel_regularizer=self._l2_reg,
        name='item_dnn/dnn_%d' % (num_item_dnn_layer - 1))

    if self._model_config.simi_func == Similarity.COSINE:
      user_tower_emb = self.norm(user_tower_emb)
      item_tower_emb = self.norm(item_tower_emb)
      temperature = self._model_config.temperature
    else:
      temperature = 1.0

    user_item_sim = self.sim(user_tower_emb, item_tower_emb) / temperature
    if self._model_config.scale_simi:
      sim_w = tf.get_variable(
          'sim_w',
          dtype=tf.float32,
          shape=(1),
          initializer=tf.ones_initializer())
      sim_b = tf.get_variable(
          'sim_b',
          dtype=tf.float32,
          shape=(1),
          initializer=tf.zeros_initializer())
      y_pred = user_item_sim * tf.abs(sim_w) + sim_b
    else:
      y_pred = user_item_sim

    if self._is_point_wise:
      y_pred = tf.reshape(y_pred, [-1])

    if self._loss_type == LossType.CLASSIFICATION:
      self._prediction_dict['logits'] = y_pred
      self._prediction_dict['probs'] = tf.nn.sigmoid(y_pred)
    elif self._loss_type == LossType.SOFTMAX_CROSS_ENTROPY:
      y_pred = self._mask_in_batch(y_pred)
      self._prediction_dict['logits'] = y_pred
      self._prediction_dict['probs'] = tf.nn.softmax(y_pred)
    else:
      self._prediction_dict['y'] = y_pred

    self._prediction_dict['user_tower_emb'] = user_tower_emb
    self._prediction_dict['item_tower_emb'] = item_tower_emb
    self._prediction_dict['user_emb'] = tf.reduce_join(
        tf.as_string(user_tower_emb), axis=-1, separator=',')
    self._prediction_dict['item_emb'] = tf.reduce_join(
        tf.as_string(item_tower_emb), axis=-1, separator=',')
    return self._prediction_dict
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easy_rec/python/model/dssm_senet.py [90:138]:
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    item_tower_emb = tf.layers.dense(
        inputs=item_tower_emb,
        units=last_item_hidden,
        kernel_regularizer=self._l2_reg,
        name='item_dnn/dnn_%d' % (num_item_dnn_layer - 1))

    if self._model_config.simi_func == Similarity.COSINE:
      user_tower_emb = self.norm(user_tower_emb)
      item_tower_emb = self.norm(item_tower_emb)
      temperature = self._model_config.temperature
    else:
      temperature = 1.0

    user_item_sim = self.sim(user_tower_emb, item_tower_emb) / temperature
    if self._model_config.scale_simi:
      sim_w = tf.get_variable(
          'sim_w',
          dtype=tf.float32,
          shape=(1),
          initializer=tf.ones_initializer())
      sim_b = tf.get_variable(
          'sim_b',
          dtype=tf.float32,
          shape=(1),
          initializer=tf.zeros_initializer())
      y_pred = user_item_sim * tf.abs(sim_w) + sim_b
    else:
      y_pred = user_item_sim

    if self._is_point_wise:
      y_pred = tf.reshape(y_pred, [-1])

    if self._loss_type == LossType.CLASSIFICATION:
      self._prediction_dict['logits'] = y_pred
      self._prediction_dict['probs'] = tf.nn.sigmoid(y_pred)
    elif self._loss_type == LossType.SOFTMAX_CROSS_ENTROPY:
      y_pred = self._mask_in_batch(y_pred)
      self._prediction_dict['logits'] = y_pred
      self._prediction_dict['probs'] = tf.nn.softmax(y_pred)
    else:
      self._prediction_dict['y'] = y_pred

    self._prediction_dict['user_tower_emb'] = user_tower_emb
    self._prediction_dict['item_tower_emb'] = item_tower_emb
    self._prediction_dict['user_emb'] = tf.reduce_join(
        tf.as_string(user_tower_emb), axis=-1, separator=',')
    self._prediction_dict['item_emb'] = tf.reduce_join(
        tf.as_string(item_tower_emb), axis=-1, separator=',')
    return self._prediction_dict
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