# 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 paddle.fluid as fluid from paddlerec.core.utils import envs from paddlerec.core.model import ModelBase class Model(ModelBase): def __init__(self, config): ModelBase.__init__(self, config) def _init_hyper_parameters(self): self.feature_size = envs.get_global_env( "hyper_parameters.feature_size") self.expert_num = envs.get_global_env("hyper_parameters.expert_num") self.gate_num = envs.get_global_env("hyper_parameters.gate_num") self.expert_size = envs.get_global_env("hyper_parameters.expert_size") self.tower_size = envs.get_global_env("hyper_parameters.tower_size") def input_data(self, is_infer=False, **kwargs): inputs = fluid.data( name="input", shape=[-1, self.feature_size], dtype="float32") label_income = fluid.data( name="label_income", shape=[-1, 2], dtype="float32", lod_level=0) label_marital = fluid.data( name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0) if is_infer: return [inputs, label_income, label_marital] else: return [inputs, label_income, label_marital] def net(self, inputs, is_infer=False): input_data = inputs[0] label_income = inputs[1] label_marital = inputs[2] # f_{i}(x) = activation(W_{i} * x + b), where activation is ReLU according to the paper expert_outputs = [] for i in range(0, self.expert_num): expert_output = fluid.layers.fc( input=input_data, size=self.expert_size, act='relu', bias_attr=fluid.ParamAttr(learning_rate=1.0), name='expert_' + str(i)) expert_outputs.append(expert_output) expert_concat = fluid.layers.concat(expert_outputs, axis=1) expert_concat = fluid.layers.reshape( expert_concat, [-1, self.expert_num, self.expert_size]) # g^{k}(x) = activation(W_{gk} * x + b), where activation is softmax according to the paper output_layers = [] for i in range(0, self.gate_num): cur_gate = fluid.layers.fc( input=input_data, size=self.expert_num, act='softmax', bias_attr=fluid.ParamAttr(learning_rate=1.0), name='gate_' + str(i)) # f^{k}(x) = sum_{i=1}^{n}(g^{k}(x)_{i} * f_{i}(x)) cur_gate_expert = fluid.layers.elementwise_mul( expert_concat, cur_gate, axis=0) cur_gate_expert = fluid.layers.reduce_sum(cur_gate_expert, dim=1) # Build tower layer cur_tower = fluid.layers.fc(input=cur_gate_expert, size=self.tower_size, act='relu', name='task_layer_' + str(i)) out = fluid.layers.fc(input=cur_tower, size=2, act='softmax', name='out_' + str(i)) output_layers.append(out) pred_income = fluid.layers.clip( output_layers[0], min=1e-15, max=1.0 - 1e-15) pred_marital = fluid.layers.clip( output_layers[1], min=1e-15, max=1.0 - 1e-15) label_income_1 = fluid.layers.slice( label_income, axes=[1], starts=[1], ends=[2]) label_marital_1 = fluid.layers.slice( label_marital, axes=[1], starts=[1], ends=[2]) auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc( input=pred_income, label=fluid.layers.cast( x=label_income_1, dtype='int64')) auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc( input=pred_marital, label=fluid.layers.cast( x=label_marital_1, dtype='int64')) if is_infer: self._infer_results["AUC_income"] = auc_income self._infer_results["AUC_marital"] = auc_marital return cost_income = fluid.layers.cross_entropy( input=pred_income, label=label_income, soft_label=True) cost_marital = fluid.layers.cross_entropy( input=pred_marital, label=label_marital, soft_label=True) avg_cost_income = fluid.layers.mean(x=cost_income) avg_cost_marital = fluid.layers.mean(x=cost_marital) cost = avg_cost_income + avg_cost_marital self._cost = cost self._metrics["AUC_income"] = auc_income self._metrics["BATCH_AUC_income"] = batch_auc_1 self._metrics["AUC_marital"] = auc_marital self._metrics["BATCH_AUC_marital"] = batch_auc_2 def infer_net(self): pass