diff --git a/doc/__init__.py b/doc/__init__.py old mode 100644 new mode 100755 diff --git a/doc/imgs/coding-gif.png b/doc/imgs/coding-gif.png old mode 100644 new mode 100755 diff --git a/doc/imgs/logo.png b/doc/imgs/logo.png old mode 100644 new mode 100755 diff --git a/fleet_rec/core/trainers/single_trainer.py b/fleet_rec/core/trainers/single_trainer.py index 4db2947172a32f0443d0d74975eda44e74accdd3..1aab4def0fefd14567b4084a23c90f5ceb4cb05b 100755 --- a/fleet_rec/core/trainers/single_trainer.py +++ b/fleet_rec/core/trainers/single_trainer.py @@ -116,17 +116,19 @@ class SingleTrainer(TranspileTrainer): context['status'] = 'infer_pass' def infer(self, context): + logger.info("Run in infer pass") infer_program = fluid.Program() startup_program = fluid.Program() with fluid.unique_name.guard(): with fluid.program_guard(infer_program, startup_program): self.model.infer_net() - + logger.info("End build infer net") if self.model._infer_data_loader is None: context['status'] = 'terminal_pass' return reader = self._get_dataloader("Evaluate") + logger.info("End Get data loader") metrics_varnames = [] metrics_format = [] @@ -142,7 +144,8 @@ class SingleTrainer(TranspileTrainer): self._exe.run(startup_program) for (epoch, model_dir) in self.increment_models: - print("Begin to infer epoch {}, model_dir: {}".format(epoch, model_dir)) + logger.info( + "Begin to infer epoch {}, model_dir: {}".format(epoch, model_dir)) program = infer_program.clone() fluid.io.load_persistables(self._exe, model_dir, program) reader.start() @@ -161,7 +164,7 @@ class SingleTrainer(TranspileTrainer): batch_id += 1 except fluid.core.EOFException: reader.reset() - + context['status'] = 'terminal_pass' def terminal(self, context): diff --git a/fleet_rec/core/trainers/transpiler_trainer.py b/fleet_rec/core/trainers/transpiler_trainer.py index 4279cbc25948f3b72bf7f1ae1ce4d87168fbddd9..d3848152cf0bea88301d96dfcfc5f2c3dd7fe8a5 100755 --- a/fleet_rec/core/trainers/transpiler_trainer.py +++ b/fleet_rec/core/trainers/transpiler_trainer.py @@ -48,12 +48,13 @@ class TranspileTrainer(Trainer): batch_size = envs.get_global_env("batch_size", None, namespace) reader_class = envs.get_global_env("class", None, namespace) + print("batch_size: {}".format(batch_size)) + reader = dataloader_instance.dataloader( + reader_class, state, self._config_yaml) - reader = dataloader_instance.dataloader(reader_class, state, self._config_yaml) - reader_class = envs.lazy_instance_by_fliename(reader_class, class_name) reader_ins = reader_class(self._config_yaml) - if hasattr(reader_ins,'generate_batch_from_trainfiles'): + if hasattr(reader_ins, 'generate_batch_from_trainfiles'): dataloader.set_sample_list_generator(reader) else: dataloader.set_sample_generator(reader, batch_size) @@ -63,23 +64,27 @@ class TranspileTrainer(Trainer): if state == "TRAIN": inputs = self.model.get_inputs() namespace = "train.reader" - train_data_path = envs.get_global_env("train_data_path", None, namespace) + train_data_path = envs.get_global_env( + "train_data_path", None, namespace) else: inputs = self.model.get_infer_inputs() namespace = "evaluate.reader" - train_data_path = envs.get_global_env("test_data_path", None, namespace) + train_data_path = envs.get_global_env( + "test_data_path", None, namespace) threads = int(envs.get_runtime_environ("train.trainer.threads")) batch_size = envs.get_global_env("batch_size", None, namespace) reader_class = envs.get_global_env("class", None, namespace) abs_dir = os.path.dirname(os.path.abspath(__file__)) reader = os.path.join(abs_dir, '../utils', 'dataset_instance.py') - pipe_cmd = "python {} {} {} {}".format(reader, reader_class, state, self._config_yaml) + pipe_cmd = "python {} {} {} {}".format( + reader, reader_class, state, self._config_yaml) if train_data_path.startswith("fleetrec::"): package_base = envs.get_runtime_environ("PACKAGE_BASE") assert package_base is not None - train_data_path = os.path.join(package_base, train_data_path.split("::")[1]) + train_data_path = os.path.join( + package_base, train_data_path.split("::")[1]) dataset = fluid.DatasetFactory().create_dataset() dataset.set_use_var(inputs) @@ -105,18 +110,23 @@ class TranspileTrainer(Trainer): return epoch_id % epoch_interval == 0 def save_inference_model(): - save_interval = envs.get_global_env("save.inference.epoch_interval", -1, namespace) + save_interval = envs.get_global_env( + "save.inference.epoch_interval", -1, namespace) if not need_save(epoch_id, save_interval, False): return - + print("save inference model is not supported now.") return - feed_varnames = envs.get_global_env("save.inference.feed_varnames", None, namespace) - fetch_varnames = envs.get_global_env("save.inference.fetch_varnames", None, namespace) - fetch_vars = [fluid.default_main_program().global_block().vars[varname] for varname in fetch_varnames] - dirname = envs.get_global_env("save.inference.dirname", None, namespace) + feed_varnames = envs.get_global_env( + "save.inference.feed_varnames", None, namespace) + fetch_varnames = envs.get_global_env( + "save.inference.fetch_varnames", None, namespace) + fetch_vars = [fluid.default_main_program().global_block().vars[varname] + for varname in fetch_varnames] + dirname = envs.get_global_env( + "save.inference.dirname", None, namespace) assert dirname is not None dirname = os.path.join(dirname, str(epoch_id)) @@ -124,16 +134,19 @@ class TranspileTrainer(Trainer): if is_fleet: fleet.save_inference_model(dirname, feed_varnames, fetch_vars) else: - fluid.io.save_inference_model(dirname, feed_varnames, fetch_vars, self._exe) + fluid.io.save_inference_model( + dirname, feed_varnames, fetch_vars, self._exe) self.inference_models.append((epoch_id, dirname)) def save_persistables(): - save_interval = envs.get_global_env("save.increment.epoch_interval", -1, namespace) + save_interval = envs.get_global_env( + "save.increment.epoch_interval", -1, namespace) if not need_save(epoch_id, save_interval, False): return - dirname = envs.get_global_env("save.increment.dirname", None, namespace) + dirname = envs.get_global_env( + "save.increment.dirname", None, namespace) assert dirname is not None dirname = os.path.join(dirname, str(epoch_id)) @@ -146,7 +159,6 @@ class TranspileTrainer(Trainer): save_persistables() save_inference_model() - def instance(self, context): models = envs.get_global_env("train.model.models") diff --git a/models/__init__.py b/models/__init__.py old mode 100644 new mode 100755 diff --git a/models/match/__init__.py b/models/match/__init__.py old mode 100644 new mode 100755 diff --git a/models/match/dssm/config.yaml b/models/match/dssm/config.yaml old mode 100644 new mode 100755 diff --git a/models/match/dssm/data/train/sample_train.txt b/models/match/dssm/data/train/sample_train.txt old mode 100644 new mode 100755 diff --git a/models/match/dssm/model.py b/models/match/dssm/model.py old mode 100644 new mode 100755 diff --git a/models/match/dssm/synthetic_evaluate_reader.py b/models/match/dssm/synthetic_evaluate_reader.py old mode 100644 new mode 100755 diff --git a/models/match/dssm/synthetic_reader.py b/models/match/dssm/synthetic_reader.py old mode 100644 new mode 100755 diff --git a/models/rank/__init__.py b/models/rank/__init__.py old mode 100644 new mode 100755 diff --git a/models/rank/criteo_reader.py b/models/rank/criteo_reader.py old mode 100644 new mode 100755 diff --git a/models/rank/dcn/__init__.py b/models/rank/dcn/__init__.py old mode 100644 new mode 100755 diff --git a/models/rank/dcn/config.yaml b/models/rank/dcn/config.yaml old mode 100644 new mode 100755 diff --git a/models/rank/dcn/criteo_reader.py b/models/rank/dcn/criteo_reader.py old mode 100644 new mode 100755 diff --git a/models/rank/dcn/data/download.py b/models/rank/dcn/data/download.py old mode 100644 new mode 100755 diff --git a/models/rank/dcn/data/preprocess.py b/models/rank/dcn/data/preprocess.py old mode 100644 new mode 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b/models/rank/dnn/data/test/sample_test.txt old mode 100644 new mode 100755 diff --git a/models/rank/dnn/data/train/sample_train.txt b/models/rank/dnn/data/train/sample_train.txt old mode 100644 new mode 100755 diff --git a/models/rank/dnn/model.py b/models/rank/dnn/model.py old mode 100644 new mode 100755 diff --git a/models/rank/readme.md b/models/rank/readme.md old mode 100644 new mode 100755 diff --git a/models/rank/wide_deep/__init__.py b/models/rank/wide_deep/__init__.py old mode 100644 new mode 100755 diff --git a/models/rank/wide_deep/config.yaml b/models/rank/wide_deep/config.yaml old mode 100644 new mode 100755 diff --git a/models/rank/wide_deep/create_data.sh b/models/rank/wide_deep/create_data.sh old mode 100644 new mode 100755 diff --git a/models/rank/wide_deep/model.py b/models/rank/wide_deep/model.py old mode 100644 new mode 100755 diff --git a/models/rank/wide_deep/reader.py b/models/rank/wide_deep/reader.py old mode 100644 new mode 100755 diff --git a/models/rank/xdeepfm/__init__.py b/models/rank/xdeepfm/__init__.py old mode 100644 new mode 100755 diff --git a/models/rank/xdeepfm/config.yaml b/models/rank/xdeepfm/config.yaml old mode 100644 new mode 100755 diff --git a/models/rank/xdeepfm/criteo_reader.py b/models/rank/xdeepfm/criteo_reader.py old mode 100644 new mode 100755 diff --git a/models/rank/xdeepfm/data/download.py b/models/rank/xdeepfm/data/download.py old mode 100644 new mode 100755 diff --git a/models/rank/xdeepfm/model.py b/models/rank/xdeepfm/model.py old mode 100644 new mode 100755 diff --git a/models/recall/__init__.py b/models/recall/__init__.py old mode 100644 new mode 100755 diff --git a/models/recall/gnn/config.yaml b/models/recall/gnn/config.yaml old mode 100644 new mode 100755 diff --git a/models/recall/gnn/data/config.txt b/models/recall/gnn/data/config.txt old mode 100644 new mode 100755 diff --git a/models/recall/gnn/data/test/test.txt b/models/recall/gnn/data/test/test.txt old mode 100644 new mode 100755 diff --git a/models/recall/gnn/data/train/train.txt b/models/recall/gnn/data/train/train.txt old mode 100644 new mode 100755 diff --git a/models/recall/gnn/data_process.sh b/models/recall/gnn/data_process.sh old mode 100644 new mode 100755 diff --git a/models/recall/gnn/model.py b/models/recall/gnn/model.py old mode 100644 new mode 100755 diff --git a/models/recall/gnn/raw_data/download.py b/models/recall/gnn/raw_data/download.py old mode 100644 new mode 100755 diff --git a/models/recall/multiview-simnet/config.yaml b/models/recall/multiview-simnet/config.yaml old mode 100644 new mode 100755 diff --git a/models/recall/multiview-simnet/data/test/test.txt b/models/recall/multiview-simnet/data/test/test.txt old mode 100644 new mode 100755 diff --git a/models/recall/multiview-simnet/data/train/train.txt b/models/recall/multiview-simnet/data/train/train.txt old mode 100644 new mode 100755 diff --git a/models/recall/multiview-simnet/data_process.sh b/models/recall/multiview-simnet/data_process.sh old mode 100644 new mode 100755 diff --git a/models/recall/multiview-simnet/generate_synthetic_data.py b/models/recall/multiview-simnet/generate_synthetic_data.py old mode 100644 new mode 100755 diff --git a/models/recall/multiview-simnet/model.py b/models/recall/multiview-simnet/model.py old mode 100644 new mode 100755 diff --git a/models/recall/tdm/__init__.py b/models/recall/tdm/__init__.py old mode 100644 new mode 100755 diff --git a/models/recall/tdm/config.yaml b/models/recall/tdm/config.yaml old mode 100644 new mode 100755 index 0e575804d373dbadb2462b881b3fe8fb5cde1643..bd870490f79646e7e250a65bda302d726ff61473 --- a/models/recall/tdm/config.yaml +++ b/models/recall/tdm/config.yaml @@ -17,7 +17,7 @@ train: # for cluster training strategy: "async" - epochs: 4 + epochs: 2 workspace: "fleetrec.models.recall.tdm" reader: @@ -65,9 +65,16 @@ train: save: increment: dirname: "increment" - epoch_interval: 2 + epoch_interval: 1 save_last: True inference: dirname: "inference" - epoch_interval: 4 + epoch_interval: 10 save_last: True + +evaluate: + workspace: "fleetrec.models.recall.tdm" + reader: + batch_size: 1 + class: "{workspace}/tdm_evaluate_reader.py" + test_data_path: "{workspace}/data/test" diff --git a/models/recall/tdm/model.py b/models/recall/tdm/model.py old mode 100644 new mode 100755 index 2edd9e4c8d7838af35c00984fd47b69f6ae1b778..ee96b260c10e85901242ac527f653e40573fefb9 --- a/models/recall/tdm/model.py +++ b/models/recall/tdm/model.py @@ -37,9 +37,9 @@ class Model(ModelBase): "tree_parameters.layer_node_num_list", [ 2, 4, 7, 12], self._namespace) self.child_nums = envs.get_global_env( - "tree_parameters.node_nums", 2, self._namespace) - self.tree_layer_init_path = envs.get_global_env( - "tree_parameters.tree_layer_init_path", None, self._namespace) + "tree_parameters.child_nums", 2, self._namespace) + self.tree_layer_path = envs.get_global_env( + "tree.tree_layer_path", None, "train.startup") # model training hyper parameter self.node_emb_size = envs.get_global_env( @@ -56,7 +56,7 @@ class Model(ModelBase): self.topK = envs.get_global_env( "hyper_parameters.node_nums", 1, self._namespace) self.batch_size = envs.get_global_env( - "batch_size", 32, "train.reader") + "batch_size", 1, "evaluate.reader") def train_net(self): self.train_input() @@ -287,16 +287,15 @@ class Model(ModelBase): shape=[self.input_emb_size], dtype="float32", ) - self._data_var.append(input_emb) + self._infer_data_var.append(input_emb) - if self._platform != "LINUX": - self._data_loader = fluid.io.DataLoader.from_generator( - feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False) + self._infer_data_loader = fluid.io.DataLoader.from_generator( + feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False) def get_layer_list(self): """get layer list from layer_list.txt""" layer_list = [] - with open(self.tree_layer_init_path, 'r') as fin: + with open(self.tree_layer_path, 'r') as fin: for line in fin.readlines(): l = [] layer = (line.split('\n'))[0].split(',') @@ -304,7 +303,7 @@ class Model(ModelBase): if node: l.append(node) layer_list.append(l) - return layer_list + self.layer_list = layer_list def create_first_layer(self): """decide which layer to start infer""" @@ -318,16 +317,15 @@ class Model(ModelBase): self.first_layer_idx = first_layer_id node_list = [] mask_list = [] - for id in node_list: + for id in first_layer_node: node_list.append(fluid.layers.fill_constant( - [self.batch_size, 1], value=id, dtype='int64')) + [self.batch_size, 1], value=int(id), dtype='int64')) mask_list.append(fluid.layers.fill_constant( [self.batch_size, 1], value=0, dtype='int64')) - self.first_layer_node = fluid.layers.concat(node_list, axis=1) self.first_layer_node_mask = fluid.layers.concat(mask_list, axis=1) - def tdm_infer_net(self, inputs): + def tdm_infer_net(self): """ infer的主要流程 infer的基本逻辑是：从上层开始（具体层idx由树结构及TopK值决定） @@ -336,14 +334,13 @@ class Model(ModelBase): 3、循环1、2步骤，遍历完所有层，得到每一层筛选结果的集合 4、将筛选结果集合中的叶子节点，拿出来再做一次topK，得到最终的召回输出 """ - input_emb = self._data_var[0] - + input_emb = self._infer_data_var[0] node_score = [] node_list = [] current_layer_node = self.first_layer_node current_layer_node_mask = self.first_layer_node_mask - input_trans_emb = self.input_trans_net.input_fc_infer(input_emb) + input_trans_emb = self.input_fc_infer(input_emb) for layer_idx in range(self.first_layer_idx, self.max_layers): # 确定当前层的需要计算的节点数 @@ -357,10 +354,9 @@ class Model(ModelBase): current_layer_node, [-1, current_layer_node_num]) current_layer_node_mask = fluid.layers.reshape( current_layer_node_mask, [-1, current_layer_node_num]) - node_emb = fluid.embedding( input=current_layer_node, - size=[self.node_nums, self.node_embed_size], + size=[self.node_nums, self.node_emb_size], param_attr=fluid.ParamAttr(name="TDM_Tree_Emb")) input_fc_out = self.layer_fc_infer( @@ -434,6 +430,7 @@ class Model(ModelBase): res_item = fluid.layers.slice( res_node_emb, axes=[2], starts=[0], ends=[1]) self.res_item_re = fluid.layers.reshape(res_item, [-1, self.topK]) + self._infer_results["item"] = self.res_item_re def input_fc_infer(self, input_emb): """ diff --git a/models/recall/tdm/tdm_evaluate_reader.py b/models/recall/tdm/tdm_evaluate_reader.py new file mode 100644 index 0000000000000000000000000000000000000000..915389ba3328e3e0ba60052532f4f93eca8faa4f --- /dev/null +++ b/models/recall/tdm/tdm_evaluate_reader.py @@ -0,0 +1,41 @@ +# -*- coding=utf8 -*- +""" +# 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 __future__ import print_function + +from fleetrec.core.reader import Reader + + +class EvaluateReader(Reader): + def init(self): + pass + + def generate_sample(self, line): + """ + Read the data line by line and process it as a dictionary + """ + def reader(): + """ + This function needs to be implemented by the user, based on data format + """ + features = (line.strip('\n')).split('\t') + input_emb = map(float, features[0].split(' ')) + + feature_name = ["input_emb"] + yield zip(feature_name, [input_emb]) + + return reader diff --git a/models/recall/tdm/tdm_reader.py b/models/recall/tdm/tdm_reader.py old mode 100644 new mode 100755 diff --git a/models/recall/word2vec/config.yaml b/models/recall/word2vec/config.yaml old mode 100644 new mode 100755 diff --git a/models/recall/word2vec/data/dict/word_count_dict.txt b/models/recall/word2vec/data/dict/word_count_dict.txt old mode 100644 new mode 100755 diff --git a/models/recall/word2vec/data/dict/word_id_dict.txt b/models/recall/word2vec/data/dict/word_id_dict.txt old mode 100644 new mode 100755 diff --git a/models/recall/word2vec/data/test/sample.txt b/models/recall/word2vec/data/test/sample.txt old mode 100644 new mode 100755 diff --git a/models/recall/word2vec/data/train/convert_sample.txt b/models/recall/word2vec/data/train/convert_sample.txt old mode 100644 new mode 100755 diff --git a/models/recall/word2vec/model.py b/models/recall/word2vec/model.py old mode 100644 new mode 100755 index 9c35cb81d292318af4236c5ba3d8eaf0e9dcaf5f..7899710a19740926fc43691276600611b515d1fb --- a/models/recall/word2vec/model.py +++ b/models/recall/word2vec/model.py @@ -25,14 +25,19 @@ class Model(ModelBase): ModelBase.__init__(self, config) def input(self): - neg_num = int(envs.get_global_env("hyper_parameters.neg_num", None, self._namespace)) - self.input_word = fluid.data(name="input_word", shape=[None, 1], dtype='int64') - self.true_word = fluid.data(name='true_label', shape=[None, 1], dtype='int64') + neg_num = int(envs.get_global_env( + "hyper_parameters.neg_num", None, self._namespace)) + self.input_word = fluid.data(name="input_word", shape=[ + None, 1], dtype='int64') + self.true_word = fluid.data(name='true_label', shape=[ + None, 1], dtype='int64') self._data_var.append(self.input_word) self._data_var.append(self.true_word) - with_shuffle_batch = bool(int(envs.get_global_env("hyper_parameters.with_shuffle_batch", None, self._namespace))) + with_shuffle_batch = bool(int(envs.get_global_env( + "hyper_parameters.with_shuffle_batch", None, self._namespace))) if not with_shuffle_batch: - self.neg_word = fluid.data(name="neg_label", shape=[None, neg_num], dtype='int64') + self.neg_word = fluid.data(name="neg_label", shape=[ + None, neg_num], dtype='int64') self._data_var.append(self.neg_word) if self._platform != "LINUX": @@ -41,10 +46,14 @@ class Model(ModelBase): def net(self): is_distributed = True if envs.get_trainer() == "CtrTrainer" else False - neg_num = int(envs.get_global_env("hyper_parameters.neg_num", None, self._namespace)) - 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) - with_shuffle_batch = bool(int(envs.get_global_env("hyper_parameters.with_shuffle_batch", None, self._namespace))) + neg_num = int(envs.get_global_env( + "hyper_parameters.neg_num", None, self._namespace)) + 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) + with_shuffle_batch = bool(int(envs.get_global_env( + "hyper_parameters.with_shuffle_batch", None, self._namespace))) def embedding_layer(input, table_name, emb_dim, initializer_instance=None, squeeze=False): emb = fluid.embedding( @@ -65,28 +74,38 @@ class Model(ModelBase): emb_initializer = fluid.initializer.Uniform(-init_width, init_width) emb_w_initializer = fluid.initializer.Constant(value=0.0) - input_emb = embedding_layer(self.input_word, "emb", sparse_feature_dim, emb_initializer, True) - true_emb_w = embedding_layer(self.true_word, "emb_w", sparse_feature_dim, emb_w_initializer, True) - true_emb_b = embedding_layer(self.true_word, "emb_b", 1, emb_w_initializer, True) - + input_emb = embedding_layer( + self.input_word, "emb", sparse_feature_dim, emb_initializer, True) + true_emb_w = embedding_layer( + self.true_word, "emb_w", sparse_feature_dim, emb_w_initializer, True) + true_emb_b = embedding_layer( + self.true_word, "emb_b", 1, emb_w_initializer, True) + if with_shuffle_batch: neg_emb_w_list = [] for i in range(neg_num): - neg_emb_w_list.append(fluid.contrib.layers.shuffle_batch(true_emb_w)) # shuffle true_word - neg_emb_w_concat = fluid.layers.concat(neg_emb_w_list, axis=0) - neg_emb_w = fluid.layers.reshape(neg_emb_w_concat, shape=[-1, neg_num, sparse_feature_dim]) - + neg_emb_w_list.append(fluid.contrib.layers.shuffle_batch( + true_emb_w)) # shuffle true_word + neg_emb_w_concat = fluid.layers.concat(neg_emb_w_list, axis=0) + neg_emb_w = fluid.layers.reshape( + neg_emb_w_concat, shape=[-1, neg_num, sparse_feature_dim]) + neg_emb_b_list = [] for i in range(neg_num): - neg_emb_b_list.append(fluid.contrib.layers.shuffle_batch(true_emb_b)) # shuffle true_word + neg_emb_b_list.append(fluid.contrib.layers.shuffle_batch( + true_emb_b)) # shuffle true_word neg_emb_b = fluid.layers.concat(neg_emb_b_list, axis=0) - neg_emb_b_vec = fluid.layers.reshape(neg_emb_b, shape=[-1, neg_num]) - + neg_emb_b_vec = fluid.layers.reshape( + neg_emb_b, shape=[-1, neg_num]) + else: - neg_emb_w = embedding_layer(self.neg_word, "emb_w", sparse_feature_dim, emb_w_initializer) - neg_emb_b = embedding_layer(self.neg_word, "emb_b", 1, emb_w_initializer) - neg_emb_b_vec = fluid.layers.reshape(neg_emb_b, shape=[-1, neg_num]) - + neg_emb_w = embedding_layer( + self.neg_word, "emb_w", sparse_feature_dim, emb_w_initializer) + neg_emb_b = embedding_layer( + self.neg_word, "emb_b", 1, emb_w_initializer) + neg_emb_b_vec = fluid.layers.reshape( + neg_emb_b, shape=[-1, neg_num]) + true_logits = fluid.layers.elementwise_add( fluid.layers.reduce_sum( fluid.layers.elementwise_mul(input_emb, true_emb_w), @@ -95,17 +114,18 @@ class Model(ModelBase): true_emb_b) input_emb_re = fluid.layers.reshape( - input_emb, shape=[-1, 1, sparse_feature_dim]) - neg_matmul = fluid.layers.matmul(input_emb_re, neg_emb_w, transpose_y=True) + input_emb, shape=[-1, 1, sparse_feature_dim]) + neg_matmul = fluid.layers.matmul( + input_emb_re, neg_emb_w, transpose_y=True) neg_logits = fluid.layers.elementwise_add( fluid.layers.reshape(neg_matmul, shape=[-1, neg_num]), neg_emb_b_vec) - - label_ones = fluid.layers.fill_constant_batch_size_like( + + label_ones = fluid.layers.fill_constant_batch_size_like( true_logits, shape=[-1, 1], value=1.0, dtype='float32') label_zeros = fluid.layers.fill_constant_batch_size_like( true_logits, shape=[-1, neg_num], value=0.0, dtype='float32') - + true_xent = fluid.layers.sigmoid_cross_entropy_with_logits(true_logits, label_ones) neg_xent = fluid.layers.sigmoid_cross_entropy_with_logits(neg_logits, @@ -116,10 +136,12 @@ class Model(ModelBase): fluid.layers.reduce_sum( neg_xent, dim=1)) self.avg_cost = fluid.layers.reduce_mean(cost) - global_right_cnt = fluid.layers.create_global_var(name="global_right_cnt", persistable=True, dtype='float32', shape=[1], value=0) - global_total_cnt = fluid.layers.create_global_var(name="global_total_cnt", persistable=True, dtype='float32', shape=[1], value=0) + global_right_cnt = fluid.layers.create_global_var( + name="global_right_cnt", persistable=True, dtype='float32', shape=[1], value=0) + global_total_cnt = fluid.layers.create_global_var( + name="global_total_cnt", persistable=True, dtype='float32', shape=[1], value=0) global_right_cnt.stop_gradient = True - global_total_cnt.stop_gradient = True + global_total_cnt.stop_gradient = True def avg_loss(self): self._cost = self.avg_cost @@ -134,9 +156,12 @@ class Model(ModelBase): self.metrics() def optimizer(self): - learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace) - decay_steps = envs.get_global_env("hyper_parameters.decay_steps", None, self._namespace) - decay_rate = envs.get_global_env("hyper_parameters.decay_rate", None, self._namespace) + learning_rate = envs.get_global_env( + "hyper_parameters.learning_rate", None, self._namespace) + decay_steps = envs.get_global_env( + "hyper_parameters.decay_steps", None, self._namespace) + decay_rate = envs.get_global_env( + "hyper_parameters.decay_rate", None, self._namespace) optimizer = fluid.optimizer.SGD( learning_rate=fluid.layers.exponential_decay( learning_rate=learning_rate, @@ -146,19 +171,27 @@ class Model(ModelBase): return optimizer def analogy_input(self): - sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self._namespace) - self.analogy_a = fluid.data(name="analogy_a", shape=[None], dtype='int64') - self.analogy_b = fluid.data(name="analogy_b", shape=[None], dtype='int64') - self.analogy_c = fluid.data(name="analogy_c", shape=[None], dtype='int64') - self.analogy_d = fluid.data(name="analogy_d", shape=[None], dtype='int64') - self._infer_data_var = [self.analogy_a, self.analogy_b, self.analogy_c, self.analogy_d] + sparse_feature_number = envs.get_global_env( + "hyper_parameters.sparse_feature_number", None, self._namespace) + self.analogy_a = fluid.data( + name="analogy_a", shape=[None], dtype='int64') + self.analogy_b = fluid.data( + name="analogy_b", shape=[None], dtype='int64') + self.analogy_c = fluid.data( + name="analogy_c", shape=[None], dtype='int64') + self.analogy_d = fluid.data( + name="analogy_d", shape=[None], dtype='int64') + self._infer_data_var = [self.analogy_a, + self.analogy_b, self.analogy_c, self.analogy_d] self._infer_data_loader = fluid.io.DataLoader.from_generator( feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False) - + def infer_net(self): - sparse_feature_dim = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace) - 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) + sparse_feature_number = envs.get_global_env( + "hyper_parameters.sparse_feature_number", None, self._namespace) def embedding_layer(input, table_name, initializer_instance=None): emb = fluid.embedding( @@ -166,30 +199,36 @@ class Model(ModelBase): size=[sparse_feature_number, sparse_feature_dim], param_attr=table_name) return emb - + self.analogy_input() - all_label = np.arange(sparse_feature_number).reshape(sparse_feature_number).astype('int32') - self.all_label = fluid.layers.cast(x=fluid.layers.assign(all_label), dtype='int64') + all_label = np.arange(sparse_feature_number).reshape( + sparse_feature_number).astype('int32') + self.all_label = fluid.layers.cast( + x=fluid.layers.assign(all_label), dtype='int64') emb_all_label = embedding_layer(self.all_label, "emb") emb_a = embedding_layer(self.analogy_a, "emb") emb_b = embedding_layer(self.analogy_b, "emb") emb_c = embedding_layer(self.analogy_c, "emb") - + target = fluid.layers.elementwise_add( fluid.layers.elementwise_sub(emb_b, emb_a), emb_c) emb_all_label_l2 = fluid.layers.l2_normalize(x=emb_all_label, axis=1) - dist = fluid.layers.matmul(x=target, y=emb_all_label_l2, transpose_y=True) + dist = fluid.layers.matmul( + x=target, y=emb_all_label_l2, transpose_y=True) values, pred_idx = fluid.layers.topk(input=dist, k=4) - label = fluid.layers.expand(fluid.layers.unsqueeze(self.analogy_d, axes=[1]), expand_times=[1, 4]) + label = fluid.layers.expand(fluid.layers.unsqueeze( + self.analogy_d, axes=[1]), expand_times=[1, 4]) label_ones = fluid.layers.fill_constant_batch_size_like( label, shape=[-1, 1], value=1.0, dtype='float32') right_cnt = fluid.layers.reduce_sum( input=fluid.layers.cast(fluid.layers.equal(pred_idx, label), dtype='float32')) total_cnt = fluid.layers.reduce_sum(label_ones) - global_right_cnt = fluid.layers.create_global_var(name="global_right_cnt", persistable=True, dtype='float32', shape=[1], value=0) - global_total_cnt = fluid.layers.create_global_var(name="global_total_cnt", persistable=True, dtype='float32', shape=[1], value=0) + global_right_cnt = fluid.layers.create_global_var( + name="global_right_cnt", persistable=True, dtype='float32', shape=[1], value=0) + global_total_cnt = fluid.layers.create_global_var( + name="global_total_cnt", persistable=True, dtype='float32', shape=[1], value=0) global_right_cnt.stop_gradient = True global_total_cnt.stop_gradient = True @@ -197,6 +236,7 @@ class Model(ModelBase): fluid.layers.assign(tmp1, global_right_cnt) tmp2 = fluid.layers.elementwise_add(total_cnt, global_total_cnt) fluid.layers.assign(tmp2, global_total_cnt) - - acc = fluid.layers.elementwise_div(global_right_cnt, global_total_cnt, name="total_acc") + + acc = fluid.layers.elementwise_div( + global_right_cnt, global_total_cnt, name="total_acc") self._infer_results['acc'] = acc