From fee1b6c147e02282b82338b1b53eb3249bfd92a5 Mon Sep 17 00:00:00 2001 From: "weisu.yxd" Date: Wed, 17 Apr 2024 18:09:00 +0800 Subject: [PATCH 1/2] fix bug of text cnn --- docs/source/feature/feature.rst | 4 ++ easy_rec/python/layers/input_layer.py | 9 ++-- easy_rec/python/layers/keras/__init__.py | 1 + easy_rec/python/layers/keras/blocks.py | 53 +++++++++++++++++++ easy_rec/python/protos/feature_config.proto | 8 +-- easy_rec/python/protos/keras_layer.proto | 1 + easy_rec/python/protos/layer.proto | 8 +++ easy_rec/python/utils/shape_utils.py | 22 ++++++++ .../contrastive_learning_on_movielens.config | 1 + .../configs/dcn_backbone_on_movielens.config | 1 + examples/configs/dcn_on_movielens.config | 1 + examples/configs/deepfm_on_movielens.config | 1 + examples/configs/fibinet_on_movielens.config | 1 + examples/configs/masknet_on_movielens.config | 1 + examples/configs/mlp_on_movielens.config | 2 + .../configs/multi_tower_on_movielens.config | 1 + ...wide_and_deep_backbone_on_movielens.config | 1 + .../configs/wide_and_deep_on_movielens.config | 1 + .../model_config/highway_on_movielens.config | 1 + 19 files changed, 108 insertions(+), 10 deletions(-) diff --git a/docs/source/feature/feature.rst b/docs/source/feature/feature.rst index e67bce81c..a41b42a53 100644 --- a/docs/source/feature/feature.rst +++ b/docs/source/feature/feature.rst @@ -340,12 +340,16 @@ Sequence类特征格式一般为“XX\|XX\|XX”，如用户行为序列特征 text_cnn: { filter_sizes: [2, 3, 4] num_filters: [16, 8, 8] + pad_sequence_length: 128 + activation: 'relu' } } } - num_filters: 卷积核个数列表 - filter_sizes: 卷积核步长列表 +- pad_sequence_length: 序列补齐或截断的长度 +- activation: 卷积操作的激活函数，默认为relu TextCNN网络是2014年提出的用来做文本分类的卷积神经网络，由于其结构简单、效果好，在文本分类、推荐等NLP领域应用广泛。 从直观上理解，TextCNN通过一维卷积来获取句子中`N gram`的特征表示。 diff --git a/easy_rec/python/layers/input_layer.py b/easy_rec/python/layers/input_layer.py index 44bf71f5f..b704190ce 100644 --- a/easy_rec/python/layers/input_layer.py +++ b/easy_rec/python/layers/input_layer.py @@ -14,7 +14,8 @@ from easy_rec.python.feature_column.feature_group import FeatureGroup from easy_rec.python.layers import sequence_feature_layer from easy_rec.python.layers import variational_dropout_layer -from easy_rec.python.layers.common_layers import text_cnn +from easy_rec.python.layers.keras import TextCNN +from easy_rec.python.layers.utils import Parameter from easy_rec.python.protos.feature_config_pb2 import WideOrDeep from easy_rec.python.utils import conditional from easy_rec.python.utils import shape_utils @@ -285,9 +286,9 @@ def single_call_input_layer(self, seq_features.append(seq_feature) cols_to_output_tensors[column] = seq_feature elif sequence_combiner.WhichOneof('combiner') == 'text_cnn': - num_filters = sequence_combiner.text_cnn.num_filters - filter_sizes = sequence_combiner.text_cnn.filter_sizes - cnn_feature = text_cnn(seq_feature, filter_sizes, num_filters) + params = Parameter.make_from_pb(sequence_combiner.text_cnn) + text_cnn_layer = TextCNN(params, name=column.name + '_text_cnn') + cnn_feature = text_cnn_layer((seq_feature, seq_len)) seq_features.append(cnn_feature) cols_to_output_tensors[column] = cnn_feature else: diff --git a/easy_rec/python/layers/keras/__init__.py b/easy_rec/python/layers/keras/__init__.py index cbe36b5ca..0e59090ce 100644 --- a/easy_rec/python/layers/keras/__init__.py +++ b/easy_rec/python/layers/keras/__init__.py @@ -2,6 +2,7 @@ from .blocks import MLP from .blocks import Gate from .blocks import Highway +from .blocks import TextCNN from .bst import BST from .data_augment import SeqAugment from .din import DIN diff --git a/easy_rec/python/layers/keras/blocks.py b/easy_rec/python/layers/keras/blocks.py index 9f13082a7..57d7ae32d 100644 --- a/easy_rec/python/layers/keras/blocks.py +++ b/easy_rec/python/layers/keras/blocks.py @@ -6,6 +6,8 @@ import tensorflow as tf from easy_rec.python.layers.keras.activation import activation_layer +from easy_rec.python.layers.utils import Parameter +from easy_rec.python.utils.shape_utils import pad_or_truncate_sequence from easy_rec.python.utils.tf_utils import add_elements_to_collection if tf.__version__ >= '2.0': @@ -161,3 +163,54 @@ def call(self, inputs, **kwargs): output += weights[:, j, None] * x j += 1 return output + + +class TextCNN(tf.keras.layers.Layer): + """Text CNN Model. + + References + - [Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882) + """ + + def __init__(self, params, name='text_cnn', reuse=None, **kwargs): + super(TextCNN, self).__init__(name, **kwargs) + self.config = params.get_pb_config() + self.pad_seq_length = self.config.pad_sequence_length + if self.pad_seq_length <= 0: + logging.warning( + 'run text cnn with pad_sequence_length <= 0, the predict of model may be unstable' + ) + self.conv_layers = [] + self.pool_layer = tf.keras.layers.GlobalMaxPool1D() + self.concat_layer = tf.keras.layers.Concatenate(axis=-1) + for size, filters in zip(self.config.filter_sizes, self.config.num_filters): + conv = tf.keras.layers.Conv1D( + filters=int(filters), kernel_size=int(size), activation=self.config.activation) + self.conv_layers.append(conv) + if self.config.HasField('mlp'): + p = Parameter.make_from_pb(self.config.mlp) + p.l2_regularizer = params.l2_regularizer + self.mlp = MLP(p, name='mlp', reuse=reuse) + else: + self.mlp = None + + def call(self, inputs, training=None, **kwargs): + """Input shape: 3D tensor with shape: `(batch_size, steps, input_dim).""" + assert isinstance(inputs, (list, tuple)) + assert len(inputs) >= 2 + seq_emb, seq_len = inputs[:2] + + if self.pad_seq_length > 0: + seq_emb, seq_len = pad_or_truncate_sequence(seq_emb, seq_len, + self.pad_seq_length) + pooled_outputs = [] + for layer in self.conv_layers: + conv = layer(seq_emb) + pooled = self.pool_layer(conv) + pooled_outputs.append(pooled) + net = self.concat_layer(pooled_outputs) + if self.mlp is not None: + output = self.mlp(net) + else: + output = net + return output diff --git a/easy_rec/python/protos/feature_config.proto b/easy_rec/python/protos/feature_config.proto index 3dba84072..19a09f131 100644 --- a/easy_rec/python/protos/feature_config.proto +++ b/easy_rec/python/protos/feature_config.proto @@ -3,6 +3,7 @@ package protos; import "easy_rec/python/protos/hyperparams.proto"; import "easy_rec/python/protos/dnn.proto"; +import "easy_rec/python/protos/layer.proto"; enum WideOrDeep { DEEP = 0; WIDE = 1; @@ -15,16 +16,11 @@ message AttentionCombiner { message MultiHeadAttentionCombiner { } -message TextCnnCombiner { - repeated uint32 filter_sizes = 1; - repeated uint32 num_filters = 2; -} - message SequenceCombiner { oneof combiner { AttentionCombiner attention = 1; MultiHeadAttentionCombiner multi_head_attention = 2; - TextCnnCombiner text_cnn = 3; + TextCNN text_cnn = 3; } } diff --git a/easy_rec/python/protos/keras_layer.proto b/easy_rec/python/protos/keras_layer.proto index 4d2cf9213..3b7c0d34d 100644 --- a/easy_rec/python/protos/keras_layer.proto +++ b/easy_rec/python/protos/keras_layer.proto @@ -25,5 +25,6 @@ message KerasLayer { MMoELayer mmoe = 14; SequenceAugment seq_aug = 15; PPNet ppnet = 16; + TextCNN text_cnn = 17; } } diff --git a/easy_rec/python/protos/layer.proto b/easy_rec/python/protos/layer.proto index 7c9dc9b51..df51009bc 100644 --- a/easy_rec/python/protos/layer.proto +++ b/easy_rec/python/protos/layer.proto @@ -88,3 +88,11 @@ message PPNet { required string mode = 3 [default = 'eager']; optional bool full_gate_input = 4 [default = true]; } + +message TextCNN { + repeated uint32 filter_sizes = 1; + repeated uint32 num_filters = 2; + required uint32 pad_sequence_length = 3; + optional string activation = 4 [default = 'relu']; + optional MLP mlp = 5; +} diff --git a/easy_rec/python/utils/shape_utils.py b/easy_rec/python/utils/shape_utils.py index 53eaac4b0..f54521513 100644 --- a/easy_rec/python/utils/shape_utils.py +++ b/easy_rec/python/utils/shape_utils.py @@ -408,3 +408,25 @@ def keep(seq_embed, seq_length): return tf.cond(max_seq_len > limited_len, lambda: truncate(seq_emb, seq_len), lambda: keep(seq_emb, seq_len)) + + +def pad_or_truncate_sequence(seq_emb, seq_len, fixed_len): + padding_length = fixed_len - tf.shape(seq_emb)[1] + + def padding(): + paddings = tf.stack([[0, 0], [0, padding_length], [0, 0]]) + padded = tf.pad(seq_emb, paddings) + return padded, seq_len + + def truncate(): + sliced = tf.slice(seq_emb, [0, 0, 0], [-1, fixed_len, -1]) + length = tf.where(seq_len < fixed_len, seq_len, + tf.ones_like(seq_len) * + fixed_len) if seq_len is not None else None + return sliced, length + + def keep(): + return seq_emb, seq_len + + return tf.cond(padding_length > 0, padding, + lambda: tf.cond(padding_length < 0, truncate, keep)) diff --git a/examples/configs/contrastive_learning_on_movielens.config b/examples/configs/contrastive_learning_on_movielens.config index 59c9c3afd..11e45d317 100644 --- a/examples/configs/contrastive_learning_on_movielens.config +++ b/examples/configs/contrastive_learning_on_movielens.config @@ -137,6 +137,7 @@ feature_config: { text_cnn: { filter_sizes: [2, 3, 4] num_filters: [16, 8, 8] + pad_sequence_length: 14 } } } diff --git a/examples/configs/dcn_backbone_on_movielens.config b/examples/configs/dcn_backbone_on_movielens.config index 2717a5c4c..dbffb76b7 100644 --- a/examples/configs/dcn_backbone_on_movielens.config +++ b/examples/configs/dcn_backbone_on_movielens.config @@ -137,6 +137,7 @@ feature_config: { text_cnn: { filter_sizes: [2, 3, 4] num_filters: [16, 8, 8] + pad_sequence_length: 14 } } } diff --git a/examples/configs/dcn_on_movielens.config b/examples/configs/dcn_on_movielens.config index 9effe62e7..4556fef88 100644 --- a/examples/configs/dcn_on_movielens.config +++ b/examples/configs/dcn_on_movielens.config @@ -138,6 +138,7 @@ feature_config: { text_cnn: { filter_sizes: [2, 3, 4] num_filters: [16, 8, 8] + pad_sequence_length: 14 } } } diff --git a/examples/configs/deepfm_on_movielens.config b/examples/configs/deepfm_on_movielens.config index 0468ae12f..f87b7c894 100644 --- a/examples/configs/deepfm_on_movielens.config +++ b/examples/configs/deepfm_on_movielens.config @@ -138,6 +138,7 @@ feature_config: { text_cnn: { filter_sizes: [2, 3, 4] num_filters: [8, 4, 4] + pad_sequence_length: 14 } } } diff --git a/examples/configs/fibinet_on_movielens.config b/examples/configs/fibinet_on_movielens.config index 9c583354b..5b0feb072 100644 --- a/examples/configs/fibinet_on_movielens.config +++ b/examples/configs/fibinet_on_movielens.config @@ -137,6 +137,7 @@ feature_config: { text_cnn: { filter_sizes: [2, 3, 4] num_filters: [16, 8, 8] + pad_sequence_length: 14 } } } diff --git a/examples/configs/masknet_on_movielens.config b/examples/configs/masknet_on_movielens.config index 2d3c0da21..4dd54c914 100644 --- a/examples/configs/masknet_on_movielens.config +++ b/examples/configs/masknet_on_movielens.config @@ -137,6 +137,7 @@ feature_config: { text_cnn: { filter_sizes: [2, 3, 4] num_filters: [16, 8, 8] + pad_sequence_length: 14 } } } diff --git a/examples/configs/mlp_on_movielens.config b/examples/configs/mlp_on_movielens.config index 4660125a3..814a09e00 100644 --- a/examples/configs/mlp_on_movielens.config +++ b/examples/configs/mlp_on_movielens.config @@ -137,6 +137,7 @@ feature_config: { text_cnn: { filter_sizes: [2, 3, 4] num_filters: [16, 8, 8] + pad_sequence_length: 14 } } } @@ -158,6 +159,7 @@ model_config: { feature_names: 'gender' feature_names: 'year' feature_names: 'genres' + feature_names: 'title' wide_deep: DEEP } backbone { diff --git a/examples/configs/multi_tower_on_movielens.config b/examples/configs/multi_tower_on_movielens.config index 2c0c77619..aa6c27b87 100644 --- a/examples/configs/multi_tower_on_movielens.config +++ b/examples/configs/multi_tower_on_movielens.config @@ -137,6 +137,7 @@ feature_config: { text_cnn: { filter_sizes: [2, 3, 4] num_filters: [16, 8, 8] + pad_sequence_length: 14 } } } diff --git a/examples/configs/wide_and_deep_backbone_on_movielens.config b/examples/configs/wide_and_deep_backbone_on_movielens.config index d3c069611..93957485c 100644 --- a/examples/configs/wide_and_deep_backbone_on_movielens.config +++ b/examples/configs/wide_and_deep_backbone_on_movielens.config @@ -137,6 +137,7 @@ feature_config: { text_cnn: { filter_sizes: [2, 3, 4] num_filters: [16, 8, 8] + pad_sequence_length: 14 } } } diff --git a/examples/configs/wide_and_deep_on_movielens.config b/examples/configs/wide_and_deep_on_movielens.config index 71bb28e2b..94d00cade 100644 --- a/examples/configs/wide_and_deep_on_movielens.config +++ b/examples/configs/wide_and_deep_on_movielens.config @@ -138,6 +138,7 @@ feature_config: { text_cnn: { filter_sizes: [2, 3, 4] num_filters: [16, 8, 8] + pad_sequence_length: 14 } } } diff --git a/samples/model_config/highway_on_movielens.config b/samples/model_config/highway_on_movielens.config index 43888eff3..191d386aa 100644 --- a/samples/model_config/highway_on_movielens.config +++ b/samples/model_config/highway_on_movielens.config @@ -159,6 +159,7 @@ feature_config: { text_cnn: { filter_sizes: [2, 3, 4] num_filters: [16, 8, 8] + pad_sequence_length: 14 } } } From f0afe9dc84a5641ff0b87a6a8d41be879e6ec0e8 Mon Sep 17 00:00:00 2001 From: "weisu.yxd" Date: Wed, 17 Apr 2024 19:28:53 +0800 Subject: [PATCH 2/2] fix bug of text cnn, add TextCNN component and model --- docs/source/component/backbone.md | 4 +- docs/source/component/component.md | 12 ++ docs/source/models/text_cnn.md | 79 +++++++++ easy_rec/python/layers/keras/blocks.py | 4 +- .../model_config/text_cnn_on_movielens.config | 162 ++++++++++++++++++ 5 files changed, 259 insertions(+), 2 deletions(-) create mode 100644 docs/source/models/text_cnn.md create mode 100644 samples/model_config/text_cnn_on_movielens.config diff --git a/docs/source/component/backbone.md b/docs/source/component/backbone.md index 29df913bf..2a0ec03a5 100644 --- a/docs/source/component/backbone.md +++ b/docs/source/component/backbone.md @@ -951,7 +951,7 @@ DBMTL模型需要在`model_params`里为每个子任务的Tower配置`relation_d ```protobuf model_config: { model_name: 'MaskNet + PPNet + MMoE' - model_class: 'RankModel' + model_class: "MultiTaskModel" feature_groups: { group_name: 'memorize' feature_names: 'user_id' @@ -971,6 +971,7 @@ model_config: { name: "mask_net" inputs { feature_group_name: "general" + input_fn: "lambda x: [x, x]" } repeat { num_repeat: 3 @@ -1104,6 +1105,7 @@ MovieLens-1M数据集效果： | Gate | 门控 | 多个输入的加权求和 | [Cross Decoupling Network](../models/cdn.html#id2) | | PeriodicEmbedding | 周期激活函数 | 数值特征Embedding | [案例5](#dlrm-embedding) | | AutoDisEmbedding | 自动离散化 | 数值特征Embedding | [dlrm_on_criteo_with_autodis.config](https://github.com/alibaba/EasyRec/tree/master/examples/configs/dlrm_on_criteo_with_autodis.config) | +| TextCNN | 文本卷积 | 提取文本序列的特征 | [text_cnn_on_movielens.config](https://github.com/alibaba/EasyRec/tree/master/examples/configs/text_cnn_on_movielens.config) | **备注**：Gate组件的第一个输入是权重向量，后面的输入拼凑成一个列表，权重向量的长度应等于列表的长度 diff --git a/docs/source/component/component.md b/docs/source/component/component.md index 89d3819fa..897e53162 100644 --- a/docs/source/component/component.md +++ b/docs/source/component/component.md @@ -49,6 +49,18 @@ | output_tensor_list | bool | false | 是否同时输出embedding列表 | | output_3d_tensor | bool | false | 是否同时输出3d tensor, `output_tensor_list=true`时该参数不生效 | +- TextCNN + +| 参数 | 类型 | 默认值 | 说明 | +| ------------------- | ------------ | ---- | ---------------- | +| num_filters | list | | 卷积核个数列表 | +| filter_sizes | list | | 卷积核步长列表 | +| activation | string | relu | 卷积操作的激活函数 | +| pad_sequence_length | uint32 | | 序列补齐或截断的长度 | +| mlp | MLP | | protobuf message | + +备注：pad_sequence_length 参数必须要配置，否则模型predict的分数可能不稳定 + ## 2.特征交叉组件 - Bilinear diff --git a/docs/source/models/text_cnn.md b/docs/source/models/text_cnn.md new file mode 100644 index 000000000..b29917456 --- /dev/null +++ b/docs/source/models/text_cnn.md @@ -0,0 +1,79 @@ +# TextCNN + +### 简介 + +TextCNN网络是2014年提出的用来做文本分类的卷积神经网络，由于其结构简单、效果好，在文本分类、推荐等NLP领域应用广泛。 +从直观上理解，TextCNN通过一维卷积来获取句子中`N gram`的特征表示。 + +在推荐模型中，可以用TextCNN网络来提取文本类型的特征。 + +### 配置说明 + +```protobuf +model_config: { + model_name: 'TextCNN' + model_class: 'RankModel' + feature_groups: { + group_name: 'text_seq' + feature_names: 'title' + wide_deep: DEEP + } + backbone { + blocks { + name: 'text_seq' + inputs { + feature_group_name: 'text_seq' + } + input_layer { + output_seq_and_normal_feature: true + } + } + blocks { + name: 'textcnn' + inputs { + block_name: 'text_seq' + } + keras_layer { + class_name: 'TextCNN' + text_cnn { + filter_sizes: [2, 3, 4] + num_filters: [16, 8, 8] + pad_sequence_length: 14 + mlp { + hidden_units: [256, 128, 64] + } + } + } + } + } + model_params { + l2_regularization: 1e-6 + } + embedding_regularization: 1e-6 +} +``` + +- model_name: 任意自定义字符串，仅有注释作用 +- model_class: 'RankModel', 不需要修改, 通过组件化方式搭建的单目标排序模型都叫这个名字 +- feature_groups: 配置一组特征。 +- backbone: 通过组件化的方式搭建的主干网络，[参考文档](../component/backbone.md) + - blocks: 由多个`组件块`组成的一个有向无环图（DAG），框架负责按照DAG的拓扑排序执行个`组件块`关联的代码逻辑，构建TF Graph的一个子图 + - name/inputs: 每个`block`有一个唯一的名字（name），并且有一个或多个输入(inputs)和输出 + - keras_layer: 加载由`class_name`指定的自定义或系统内置的keras layer，执行一段代码逻辑；[参考文档](../component/backbone.md#keraslayer) + - TextCNN: 调用TextCNN组件。组件的参数，详见[参考文档](../component/component.md#id2) + - num_filters: 卷积核个数列表 + - filter_sizes: 卷积核步长列表 + - pad_sequence_length: 序列补齐或截断的长度 + - activation: 卷积操作的激活函数，默认为relu + - concat_blocks: DAG的输出节点由`concat_blocks`配置项定义，如果不配置`concat_blocks`，框架会自动拼接DAG的所有叶子节点并输出。 +- model_params: + - l2_regularization: (可选) 对DNN参数的regularization, 减少overfit +- embedding_regularization: 对embedding部分加regularization, 减少overfit + +### 示例Config + +[text_cnn_on_movielens.config](https://github.com/alibaba/EasyRec/tree/master/samples/model_config/text_cnn_on_movielens.config) + +### 参考论文 + +[Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882) diff --git a/easy_rec/python/layers/keras/blocks.py b/easy_rec/python/layers/keras/blocks.py index 57d7ae32d..06ce11cbf 100644 --- a/easy_rec/python/layers/keras/blocks.py +++ b/easy_rec/python/layers/keras/blocks.py @@ -185,7 +185,9 @@ def __init__(self, params, name='text_cnn', reuse=None, **kwargs): self.concat_layer = tf.keras.layers.Concatenate(axis=-1) for size, filters in zip(self.config.filter_sizes, self.config.num_filters): conv = tf.keras.layers.Conv1D( - filters=int(filters), kernel_size=int(size), activation=self.config.activation) + filters=int(filters), + kernel_size=int(size), + activation=self.config.activation) self.conv_layers.append(conv) if self.config.HasField('mlp'): p = Parameter.make_from_pb(self.config.mlp) diff --git a/samples/model_config/text_cnn_on_movielens.config b/samples/model_config/text_cnn_on_movielens.config new file mode 100644 index 000000000..87dbee5ed --- /dev/null +++ b/samples/model_config/text_cnn_on_movielens.config @@ -0,0 +1,162 @@ +train_input_path: "data/test/movielens_1m/ml_train_data" +eval_input_path: "data/test/movielens_1m/ml_test_data" +model_dir: "experiments/text_cnn_movielens_ckpt" + +train_config { + optimizer_config: { + adam_optimizer: { + learning_rate: { + constant_learning_rate { + learning_rate: 0.0001 + } + } + beta1: 0.9 + beta2: 0.999 + } + use_moving_average: false + } + log_step_count_steps: 100 + save_checkpoints_steps: 100 + sync_replicas: true + num_steps: 100 +} + +eval_config { + metrics_set: { + gauc { + uid_field: 'user_id' + } + } + metrics_set: { + auc {} + } + metrics_set: { + max_f1 {} + } +} + +data_config { + input_fields { + input_name:'rating' + input_type: INT32 + } + input_fields { + input_name:'label' + input_type: INT32 + } + input_fields { + input_name:'user_id' + input_type: INT32 + } + input_fields { + input_name:'movie_id' + input_type: INT32 + } + input_fields { + input_name:'gender' + input_type: INT32 + } + input_fields { + input_name: 'age' + input_type: INT32 + } + input_fields { + input_name: 'occupation' + input_type: INT32 + } + input_fields { + input_name: 'zip_id' + input_type: INT32 + default_val: '0' + } + input_fields { + input_name: 'genres' + input_type: STRING + default_val: 'unknown' + } + input_fields { + input_name: 'title' + input_type: STRING + default_val: 'unknown' + } + input_fields { + input_name: 'movie_year_bin' + input_type: INT32 + } + input_fields { + input_name: 'score_year_diff' + input_type: INT32 + default_val: '0' + } + input_fields { + input_name: 'score_time' + input_type: DOUBLE + } + input_fields { + input_name: 'embedding' + input_type: STRING + default_val: '' + } + + label_fields: 'label' + batch_size: 128 + num_epochs: 10000 + prefetch_size: 1 + input_type: CSVInput +} + +feature_config: { + features: { + input_names: 'title' + feature_type: SequenceFeature + separator: ' ' + embedding_dim: 16 + hash_bucket_size: 20000 + } +} +model_config: { + model_name: 'TextCNN' + model_class: 'RankModel' + feature_groups: { + group_name: 'text_seq' + feature_names: 'title' + wide_deep: DEEP + } + backbone { + blocks { + name: 'text_seq' + inputs { + feature_group_name: 'text_seq' + } + input_layer { + output_seq_and_normal_feature: true + } + } + blocks { + name: 'textcnn' + inputs { + block_name: 'text_seq' + } + keras_layer { + class_name: 'TextCNN' + text_cnn { + filter_sizes: [2, 3, 4] + num_filters: [16, 8, 8] + pad_sequence_length: 14 + mlp { + hidden_units: [256, 128, 64] + } + } + } + } + } + model_params { + l2_regularization: 1e-6 + } + embedding_regularization: 1e-6 +} +export_config { + exporter_type: "best" + best_exporter_metric: "gauc" + exports_to_keep: 1 +}