# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import paddle.fluid as fluid from paddlerec.core.utils import envs from paddlerec.core.model import Model as ModelBase class Model(ModelBase): def __init__(self, config): ModelBase.__init__(self, config) def deepfm_net(self): init_value_ = 0.1 is_distributed = True if envs.get_trainer() == "CtrTrainer" else False sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self._namespace) sparse_feature_dim = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace) # ------------------------- network input -------------------------- num_field = envs.get_global_env("hyper_parameters.num_field", None, self._namespace) raw_feat_idx = self._sparse_data_var[1] raw_feat_value = self._dense_data_var[0] self.label = self._sparse_data_var[0] feat_idx = raw_feat_idx feat_value = fluid.layers.reshape(raw_feat_value, [-1, num_field, 1]) # None * num_field * 1 reg = envs.get_global_env("hyper_parameters.reg", 1e-4, self._namespace) first_weights_re = fluid.embedding( input=feat_idx, is_sparse=True, is_distributed=is_distributed, dtype='float32', size=[sparse_feature_number + 1, 1], padding_idx=0, param_attr=fluid.ParamAttr( initializer=fluid.initializer.TruncatedNormalInitializer( loc=0.0, scale=init_value_), regularizer=fluid.regularizer.L1DecayRegularizer(reg))) first_weights = fluid.layers.reshape( first_weights_re, shape=[-1, num_field, 1]) # None * num_field * 1 y_first_order = fluid.layers.reduce_sum((first_weights * feat_value), 1) # ------------------------- second order term -------------------------- feat_embeddings_re = fluid.embedding( input=feat_idx, is_sparse=True, is_distributed=is_distributed, dtype='float32', size=[sparse_feature_number + 1, sparse_feature_dim], padding_idx=0, param_attr=fluid.ParamAttr( initializer=fluid.initializer.TruncatedNormalInitializer( loc=0.0, scale=init_value_ / math.sqrt(float(sparse_feature_dim))))) feat_embeddings = fluid.layers.reshape( feat_embeddings_re, shape=[-1, num_field, sparse_feature_dim]) # None * num_field * embedding_size feat_embeddings = feat_embeddings * feat_value # None * num_field * embedding_size # sum_square part summed_features_emb = fluid.layers.reduce_sum(feat_embeddings, 1) # None * embedding_size summed_features_emb_square = fluid.layers.square( summed_features_emb) # None * embedding_size # square_sum part squared_features_emb = fluid.layers.square( feat_embeddings) # None * num_field * embedding_size squared_sum_features_emb = fluid.layers.reduce_sum( squared_features_emb, 1) # None * embedding_size y_second_order = 0.5 * fluid.layers.reduce_sum( summed_features_emb_square - squared_sum_features_emb, 1, keep_dim=True) # None * 1 # ------------------------- DNN -------------------------- layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes", None, self._namespace) act = envs.get_global_env("hyper_parameters.act", None, self._namespace) y_dnn = fluid.layers.reshape(feat_embeddings, [-1, num_field * sparse_feature_dim]) for s in layer_sizes: y_dnn = fluid.layers.fc( input=y_dnn, size=s, act=act, param_attr=fluid.ParamAttr( initializer=fluid.initializer.TruncatedNormalInitializer( loc=0.0, scale=init_value_ / math.sqrt(float(10)))), bias_attr=fluid.ParamAttr( initializer=fluid.initializer.TruncatedNormalInitializer( loc=0.0, scale=init_value_))) y_dnn = fluid.layers.fc( input=y_dnn, size=1, act=None, param_attr=fluid.ParamAttr( initializer=fluid.initializer.TruncatedNormalInitializer( loc=0.0, scale=init_value_)), bias_attr=fluid.ParamAttr( initializer=fluid.initializer.TruncatedNormalInitializer( loc=0.0, scale=init_value_))) # ------------------------- DeepFM -------------------------- self.predict = fluid.layers.sigmoid(y_first_order + y_second_order + y_dnn) def train_net(self): self._init_slots() self.deepfm_net() # ------------------------- Cost(logloss) -------------------------- cost = fluid.layers.log_loss(input=self.predict, label=fluid.layers.cast(self.label, "float32")) avg_cost = fluid.layers.reduce_sum(cost) self._cost = avg_cost # ------------------------- Metric(Auc) -------------------------- predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1) label_int = fluid.layers.cast(self.label, 'int64') auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d, label=label_int, slide_steps=0) self._metrics["AUC"] = auc_var self._metrics["BATCH_AUC"] = batch_auc_var def optimizer(self): learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace) optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True) return optimizer def infer_net(self): self.train_net()