# 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. from collections import OrderedDict 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 init_network(self): self.cross_num = envs.get_global_env("hyper_parameters.cross_num", None, self._namespace) self.dnn_hidden_units = envs.get_global_env("hyper_parameters.dnn_hidden_units", None, self._namespace) self.l2_reg_cross = envs.get_global_env("hyper_parameters.l2_reg_cross", None, self._namespace) self.dnn_use_bn = envs.get_global_env("hyper_parameters.dnn_use_bn", None, self._namespace) self.clip_by_norm = envs.get_global_env("hyper_parameters.clip_by_norm", None, self._namespace) cat_feat_num = envs.get_global_env("hyper_parameters.cat_feat_num", None, self._namespace) self.sparse_inputs = self._sparse_data_var[1:] self.dense_inputs = self._dense_data_var self.target_input = self._sparse_data_var[0] cat_feat_dims_dict = OrderedDict() for line in open(cat_feat_num): spls = line.strip().split() assert len(spls) == 2 cat_feat_dims_dict[spls[0]] = int(spls[1]) self.cat_feat_dims_dict = cat_feat_dims_dict if cat_feat_dims_dict else OrderedDict( ) self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse", None, self._namespace) self.dense_feat_names = [i.name for i in self.dense_inputs] self.sparse_feat_names = [i.name for i in self.sparse_inputs] # {feat_name: dims} self.feat_dims_dict = OrderedDict( [(feat_name, 1) for feat_name in self.dense_feat_names]) self.feat_dims_dict.update(self.cat_feat_dims_dict) self.net_input = None self.loss = None def _create_embedding_input(self): # sparse embedding sparse_emb_dict = OrderedDict() for var in self.sparse_inputs: sparse_emb_dict[var.name] = fluid.embedding(input=var, size=[self.feat_dims_dict[var.name] + 1, 6 * int(pow(self.feat_dims_dict[var.name], 0.25)) ],is_sparse=self.is_sparse) # combine dense and sparse_emb dense_input_list = self.dense_inputs sparse_emb_list = list(sparse_emb_dict.values()) sparse_input = fluid.layers.concat(sparse_emb_list, axis=-1) sparse_input = fluid.layers.flatten(sparse_input) dense_input = fluid.layers.concat(dense_input_list, axis=-1) dense_input = fluid.layers.flatten(dense_input) dense_input = fluid.layers.cast(dense_input, 'float32') net_input = fluid.layers.concat([dense_input, sparse_input], axis=-1) return net_input def _deep_net(self, input, hidden_units, use_bn=False, is_test=False): for units in hidden_units: input = fluid.layers.fc(input=input, size=units) if use_bn: input = fluid.layers.batch_norm(input, is_test=is_test) input = fluid.layers.relu(input) return input def _cross_layer(self, x0, x, prefix): input_dim = x0.shape[-1] w = fluid.layers.create_parameter( [input_dim], dtype='float32', name=prefix + "_w") b = fluid.layers.create_parameter( [input_dim], dtype='float32', name=prefix + "_b") xw = fluid.layers.reduce_sum(x * w, dim=1, keep_dim=True) # (N, 1) return x0 * xw + b + x, w def _cross_net(self, input, num_corss_layers): x = x0 = input l2_reg_cross_list = [] for i in range(num_corss_layers): x, w = self._cross_layer(x0, x, "cross_layer_{}".format(i)) l2_reg_cross_list.append(self._l2_loss(w)) l2_reg_cross_loss = fluid.layers.reduce_sum( fluid.layers.concat( l2_reg_cross_list, axis=-1)) return x, l2_reg_cross_loss def _l2_loss(self, w): return fluid.layers.reduce_sum(fluid.layers.square(w)) def train_net(self): self.model._init_slots() self.init_network() self.net_input = self._create_embedding_input() deep_out = self._deep_net(self.net_input, self.dnn_hidden_units, self.dnn_use_bn, False) cross_out, l2_reg_cross_loss = self._cross_net(self.net_input, self.cross_num) last_out = fluid.layers.concat([deep_out, cross_out], axis=-1) logit = fluid.layers.fc(last_out, 1) self.prob = fluid.layers.sigmoid(logit) # auc prob_2d = fluid.layers.concat([1 - self.prob, self.prob], 1) label_int = fluid.layers.cast(self.target_input, 'int64') auc_var, batch_auc_var, self.auc_states = fluid.layers.auc( input=prob_2d, label=label_int, slide_steps=0) self._metrics["AUC"] = auc_var self._metrics["BATCH_AUC"] = batch_auc_var # logloss logloss = fluid.layers.log_loss(self.prob, fluid.layers.cast(self.target_input, dtype='float32')) self.avg_logloss = fluid.layers.reduce_mean(logloss) # reg_coeff * l2_reg_cross l2_reg_cross_loss = self.l2_reg_cross * l2_reg_cross_loss self.loss = self.avg_logloss + l2_reg_cross_loss self._cost = self.loss 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, parameter_list): self.model._init_slots() self.deepfm_net()