"feature_dim_dict must be a dict like {"sparse":{"field_1":4,"field_2":3,"field_3":2},"dense":["field_5",]}")
if not isinstance(feature_dim_dict["sparse"], dict):
raise ValueError("feature_dim_dict["sparse"] must be a dict,cur is", type(
feature_dim_dict["sparse"]))
if not isinstance(feature_dim_dict["dense"], list):
raise ValueError("feature_dim_dict["dense"] must be a list,cur is", type(
feature_dim_dict["dense"]))
After Change
:param use_bn: bool. Whether use BatchNormalization before activation or not.in deep net
:return: A Keras model instance.
check_feature_config_dict(feature_dim_dict)
deep_emb_list, linear_logit, inputs_list = get_inputs_embedding(
feature_dim_dict, embedding_size, l2_reg_embedding, l2_reg_linear, init_std, seed)
