► 代码示例 / 结构化数据 / 使用门控残差和变量选择网络进行分类

使用门控残差和变量选择网络进行分类

作者： Khalid Salama
创建日期 2021/02/10
最后修改日期 2025/01/08
描述： 使用门控残差和变量选择网络进行收入水平预测。

ⓘ 此示例使用 Keras 2

在 Colab 中查看 • GitHub 源码

简介

此示例演示了 Bryan Lim 等人在 用于可解释多水平时间序列预测的时间融合转换器 (TFT) 中提出的门控残差网络 (GRN) 和变量选择网络 (VSN) 在结构化数据分类中的应用。GRN 使模型可以灵活地仅在需要的地方应用非线性处理。VSN 允许模型软删除任何不必要的噪声输入，这些输入可能会对性能产生负面影响。这些技术共同帮助提高了深度神经网络模型的学习能力。

请注意，此示例仅实现了论文中描述的 GRN 和 VSN 组件，而不是整个 TFT 模型，因为 GRN 和 VSN 本身对于结构化数据学习任务也很有用。

要运行代码，您需要使用 TensorFlow 2.3 或更高版本。

数据集

此示例使用 美国人口普查收入数据集，该数据集由 加州大学欧文分校机器学习存储库 提供。该任务是二元分类，以确定一个人的年收入是否超过 5 万美元。

该数据集包含约 30 万个实例，具有 41 个输入特征：7 个数值特征和 34 个类别特征。

设置

import os
import subprocess
import tarfile

os.environ["KERAS_BACKEND"] = "torch"  # or jax, or tensorflow

import numpy as np
import pandas as pd
import keras
from keras import layers

准备数据

首先，我们将数据从 UCI 机器学习存储库加载到 Pandas DataFrame 中。

# Column names.
CSV_HEADER = [
    "age",
    "class_of_worker",
    "detailed_industry_recode",
    "detailed_occupation_recode",
    "education",
    "wage_per_hour",
    "enroll_in_edu_inst_last_wk",
    "marital_stat",
    "major_industry_code",
    "major_occupation_code",
    "race",
    "hispanic_origin",
    "sex",
    "member_of_a_labor_union",
    "reason_for_unemployment",
    "full_or_part_time_employment_stat",
    "capital_gains",
    "capital_losses",
    "dividends_from_stocks",
    "tax_filer_stat",
    "region_of_previous_residence",
    "state_of_previous_residence",
    "detailed_household_and_family_stat",
    "detailed_household_summary_in_household",
    "instance_weight",
    "migration_code-change_in_msa",
    "migration_code-change_in_reg",
    "migration_code-move_within_reg",
    "live_in_this_house_1_year_ago",
    "migration_prev_res_in_sunbelt",
    "num_persons_worked_for_employer",
    "family_members_under_18",
    "country_of_birth_father",
    "country_of_birth_mother",
    "country_of_birth_self",
    "citizenship",
    "own_business_or_self_employed",
    "fill_inc_questionnaire_for_veterans_admin",
    "veterans_benefits",
    "weeks_worked_in_year",
    "year",
    "income_level",
]

data_url = "https://archive.ics.uci.edu/static/public/117/census+income+kdd.zip"
keras.utils.get_file(origin=data_url, extract=True)

'/home/humbulani/.keras/datasets/census+income+kdd.zip'

确定下载的 .tar.gz 文件的路径，并从下载的 .tar.gz 文件中提取文件

extracted_path = os.path.join(
    os.path.expanduser("~"), ".keras", "datasets", "census+income+kdd.zip"
)
for root, dirs, files in os.walk(extracted_path):
    for file in files:
        if file.endswith(".tar.gz"):
            tar_gz_path = os.path.join(root, file)
            with tarfile.open(tar_gz_path, "r:gz") as tar:
                tar.extractall(path=root)

train_data_path = os.path.join(
    os.path.expanduser("~"),
    ".keras",
    "datasets",
    "census+income+kdd.zip",
    "census-income.data",
)
test_data_path = os.path.join(
    os.path.expanduser("~"),
    ".keras",
    "datasets",
    "census+income+kdd.zip",
    "census-income.test",
)

data = pd.read_csv(train_data_path, header=None, names=CSV_HEADER)
test_data = pd.read_csv(test_data_path, header=None, names=CSV_HEADER)

print(f"Data shape: {data.shape}")
print(f"Test data shape: {test_data.shape}")

Data shape: (199523, 42)
Test data shape: (99762, 42)

我们将目标列从字符串转换为整数。

data["income_level"] = data["income_level"].apply(
    lambda x: 0 if x == " - 50000." else 1
)
test_data["income_level"] = test_data["income_level"].apply(
    lambda x: 0 if x == " - 50000." else 1
)

然后，我们将数据集拆分为训练集和验证集。

random_selection = np.random.rand(len(data.index)) <= 0.85
train_data = data[random_selection]
valid_data = data[~random_selection]

最后，我们将训练和测试数据分割本地存储到 CSV 文件中。

train_data_file = "train_data.csv"
valid_data_file = "valid_data.csv"
test_data_file = "test_data.csv"

train_data.to_csv(train_data_file, index=False, header=False)
valid_data.to_csv(valid_data_file, index=False, header=False)
test_data.to_csv(test_data_file, index=False, header=False)

定义数据集元数据

在这里，我们定义数据集的元数据，这些元数据对于将数据读取和解析为输入特征，以及根据输入特征的类型对其进行编码非常有用。

# Target feature name.
TARGET_FEATURE_NAME = "income_level"
# Weight column name.
WEIGHT_COLUMN_NAME = "instance_weight"
# Numeric feature names.
NUMERIC_FEATURE_NAMES = [
    "age",
    "wage_per_hour",
    "capital_gains",
    "capital_losses",
    "dividends_from_stocks",
    "num_persons_worked_for_employer",
    "weeks_worked_in_year",
]
# Categorical features and their vocabulary lists.
# Note that we add 'v=' as a prefix to all categorical feature values to make
# sure that they are treated as strings.
CATEGORICAL_FEATURES_WITH_VOCABULARY = {
    feature_name: sorted([str(value) for value in list(data[feature_name].unique())])
    for feature_name in CSV_HEADER
    if feature_name
    not in list(NUMERIC_FEATURE_NAMES + [WEIGHT_COLUMN_NAME, TARGET_FEATURE_NAME])
}
# All features names.
FEATURE_NAMES = NUMERIC_FEATURE_NAMES + list(
    CATEGORICAL_FEATURES_WITH_VOCABULARY.keys()
)
# Feature default values.
COLUMN_DEFAULTS = [
    (
        [0.0]
        if feature_name
        in NUMERIC_FEATURE_NAMES + [TARGET_FEATURE_NAME, WEIGHT_COLUMN_NAME]
        else ["NA"]
    )
    for feature_name in CSV_HEADER
]

为训练和评估创建一个 tf.data.Dataset

我们创建一个输入函数来读取和解析文件，并将特征和标签转换为 [tf.data.Dataset](https://tensorflowcn.cn/api_docs/python/tf/data/Dataset) 以进行训练和评估。

# Tensorflow required for tf.data.Datasets
import tensorflow as tf


# We process our datasets elements here (categorical) and convert them to indices to avoid this step
# during model training since only tensorflow support strings.
def process(features, target):
    for feature_name in features:
        if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:
            # Cast categorical feature values to string.
            features[feature_name] = tf.cast(features[feature_name], "string")
            vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]
            # Create a lookup to convert a string values to an integer indices.
            # Since we are not using a mask token nor expecting any out of vocabulary
            # (oov) token, we set mask_token to None and  num_oov_indices to 0.
            index = layers.StringLookup(
                vocabulary=vocabulary,
                mask_token=None,
                num_oov_indices=0,
                output_mode="int",
            )
            # Convert the string input values into integer indices.
            value_index = index(features[feature_name])
            features[feature_name] = value_index
        else:
            # Do nothing for numerical features
            pass

    # Get the instance weight.
    weight = features.pop(WEIGHT_COLUMN_NAME)
    # Change features from OrderedDict to Dict to match Inputs as they are Dict.
    return dict(features), target, weight


def get_dataset_from_csv(csv_file_path, shuffle=False, batch_size=128):
    dataset = tf.data.experimental.make_csv_dataset(
        csv_file_path,
        batch_size=batch_size,
        column_names=CSV_HEADER,
        column_defaults=COLUMN_DEFAULTS,
        label_name=TARGET_FEATURE_NAME,
        num_epochs=1,
        header=False,
        shuffle=shuffle,
    ).map(process)

    return dataset

创建模型输入

def create_model_inputs():
    inputs = {}
    for feature_name in FEATURE_NAMES:
        if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:
            # Make them int64, they are Categorical (whole units)
            inputs[feature_name] = layers.Input(
                name=feature_name, shape=(), dtype="int64"
            )
        else:
            # Make them float32, they are Real numbers
            inputs[feature_name] = layers.Input(
                name=feature_name, shape=(), dtype="float32"
            )
    return inputs

实现门控线性单元

门控线性单元 (GLU) 提供了抑制与给定任务无关的输入的灵活性。

class GatedLinearUnit(layers.Layer):
    def __init__(self, units):
        super().__init__()
        self.linear = layers.Dense(units)
        self.sigmoid = layers.Dense(units, activation="sigmoid")

    def call(self, inputs):
        return self.linear(inputs) * self.sigmoid(inputs)

    # Remove build warnings
    def build(self):
        self.built = True

实现门控残差网络

门控残差网络 (GRN) 的工作方式如下

  1. 对输入应用非线性 ELU 变换。
  2. 应用线性变换，然后应用 dropout。
  3. 应用 GLU 并将原始输入添加到 GLU 的输出中以执行跳过（残差）连接。
  4. 应用层归一化并生成输出。
class GatedResidualNetwork(layers.Layer):
    def __init__(self, units, dropout_rate):
        super().__init__()
        self.units = units
        self.elu_dense = layers.Dense(units, activation="elu")
        self.linear_dense = layers.Dense(units)
        self.dropout = layers.Dropout(dropout_rate)
        self.gated_linear_unit = GatedLinearUnit(units)
        self.layer_norm = layers.LayerNormalization()
        self.project = layers.Dense(units)

    def call(self, inputs):
        x = self.elu_dense(inputs)
        x = self.linear_dense(x)
        x = self.dropout(x)
        if inputs.shape[-1] != self.units:
            inputs = self.project(inputs)
        x = inputs + self.gated_linear_unit(x)
        x = self.layer_norm(x)
        return x

    # Remove build warnings
    def build(self):
        self.built = True

实现变量选择网络

变量选择网络 (VSN) 的工作方式如下

  1. 对每个特征单独应用 GRN。
  2. 在所有特征的串联上应用 GRN，然后应用 softmax 来生成特征权重。
  3. 生成单个 GRN 输出的加权和。

请注意，VSN 的输出是 [batch_size, encoding_size]，与输入特征的数量无关。

对于类别特征，我们使用 layers.Embedding 对它们进行编码，使用 encoding_size 作为嵌入维度。对于数值特征，我们使用 layers.Dense 应用线性变换，将每个特征投影到 encoding_size 维向量中。因此，所有编码后的特征都将具有相同的维度。

class VariableSelection(layers.Layer):
    def __init__(self, num_features, units, dropout_rate):
        super().__init__()
        self.units = units
        # Create an embedding layers with the specified dimensions
        self.embeddings = dict()
        for input_ in CATEGORICAL_FEATURES_WITH_VOCABULARY:
            vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[input_]
            embedding_encoder = layers.Embedding(
                input_dim=len(vocabulary), output_dim=self.units, name=input_
            )
            self.embeddings[input_] = embedding_encoder

        # Projection layers for numeric features
        self.proj_layer = dict()
        for input_ in NUMERIC_FEATURE_NAMES:
            proj_layer = layers.Dense(units=self.units)
            self.proj_layer[input_] = proj_layer

        self.grns = list()
        # Create a GRN for each feature independently
        for idx in range(num_features):
            grn = GatedResidualNetwork(units, dropout_rate)
            self.grns.append(grn)
        # Create a GRN for the concatenation of all the features
        self.grn_concat = GatedResidualNetwork(units, dropout_rate)
        self.softmax = layers.Dense(units=num_features, activation="softmax")

    def call(self, inputs):
        concat_inputs = []
        for input_ in inputs:
            if input_ in CATEGORICAL_FEATURES_WITH_VOCABULARY:
                max_index = self.embeddings[input_].input_dim - 1  # Clamp the indices
                # torch had some index errors during embedding hence the clip function
                embedded_feature = self.embeddings[input_](
                    keras.ops.clip(inputs[input_], 0, max_index)
                )
                concat_inputs.append(embedded_feature)
            else:
                # Project the numeric feature to encoding_size using linear transformation.
                proj_feature = keras.ops.expand_dims(inputs[input_], -1)
                proj_feature = self.proj_layer[input_](proj_feature)
                concat_inputs.append(proj_feature)

        v = layers.concatenate(concat_inputs)
        v = self.grn_concat(v)
        v = keras.ops.expand_dims(self.softmax(v), axis=-1)
        x = []
        for idx, input in enumerate(concat_inputs):
            x.append(self.grns[idx](input))
        x = keras.ops.stack(x, axis=1)
        return keras.ops.squeeze(
            keras.ops.matmul(keras.ops.transpose(v, axes=[0, 2, 1]), x), axis=1
        )

    # to remove the build warnings
    def build(self):
        self.built = True

创建门控残差和变量选择网络模型

def create_model(encoding_size):
    inputs = create_model_inputs()
    num_features = len(inputs)
    features = VariableSelection(num_features, encoding_size, dropout_rate)(inputs)
    outputs = layers.Dense(units=1, activation="sigmoid")(features)
    # Functional model
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model

编译、训练和评估模型

learning_rate = 0.001
dropout_rate = 0.15
batch_size = 265
num_epochs = 20  # may be adjusted to a desired value
encoding_size = 16

model = create_model(encoding_size)
model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=learning_rate),
    loss=keras.losses.BinaryCrossentropy(),
    metrics=[keras.metrics.BinaryAccuracy(name="accuracy")],
)

让我们可视化连接图

# `rankdir='LR'` is to make the graph horizontal.
keras.utils.plot_model(model, show_shapes=True, show_layer_names=True, rankdir="LR")


# Create an early stopping callback.
early_stopping = keras.callbacks.EarlyStopping(
    monitor="val_loss", patience=5, restore_best_weights=True
)

print("Start training the model...")
train_dataset = get_dataset_from_csv(
    train_data_file, shuffle=True, batch_size=batch_size
)
valid_dataset = get_dataset_from_csv(valid_data_file, batch_size=batch_size)
model.fit(
    train_dataset,
    epochs=num_epochs,
    validation_data=valid_dataset,
    callbacks=[early_stopping],
)
print("Model training finished.")

print("Evaluating model performance...")
test_dataset = get_dataset_from_csv(test_data_file, batch_size=batch_size)
_, accuracy = model.evaluate(test_dataset)
print(f"Test accuracy: {round(accuracy * 100, 2)}%")

Start training the model...

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408/Unknown  89s 216ms/step - accuracy: 0.9345 - loss: 324.9518


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479/Unknown  106s 220ms/step - accuracy: 0.9360 - loss: 316.0910


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484/Unknown  107s 220ms/step - accuracy: 0.9361 - loss: 315.5415


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506/Unknown  112s 220ms/step - accuracy: 0.9364 - loss: 313.2194


507/Unknown  112s 220ms/step - accuracy: 0.9365 - loss: 313.1174


508/Unknown  112s 220ms/step - accuracy: 0.9365 - loss: 313.0163


509/Unknown  113s 220ms/step - accuracy: 0.9365 - loss: 312.9149


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513/Unknown  113s 220ms/step - accuracy: 0.9366 - loss: 312.5108


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519/Unknown  115s 221ms/step - accuracy: 0.9367 - loss: 311.9154


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532/Unknown  118s 221ms/step - accuracy: 0.9369 - loss: 310.6666


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573/Unknown  128s 222ms/step - accuracy: 0.9375 - loss: 306.9967


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609/Unknown  137s 223ms/step - accuracy: 0.9379 - loss: 304.1025


610/Unknown  137s 223ms/step - accuracy: 0.9379 - loss: 304.0259


611/Unknown  137s 223ms/step - accuracy: 0.9380 - loss: 303.9494


612/Unknown  137s 223ms/step - accuracy: 0.9380 - loss: 303.8730


613/Unknown  137s 223ms/step - accuracy: 0.9380 - loss: 303.7966


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617/Unknown  138s 224ms/step - accuracy: 0.9380 - loss: 303.4917


618/Unknown  139s 224ms/step - accuracy: 0.9380 - loss: 303.4161


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620/Unknown  139s 224ms/step - accuracy: 0.9381 - loss: 303.2652


621/Unknown  140s 224ms/step - accuracy: 0.9381 - loss: 303.1904


622/Unknown  140s 224ms/step - accuracy: 0.9381 - loss: 303.1159


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624/Unknown  140s 224ms/step - accuracy: 0.9381 - loss: 302.9678


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626/Unknown  141s 224ms/step - accuracy: 0.9381 - loss: 302.8201


627/Unknown  141s 224ms/step - accuracy: 0.9381 - loss: 302.7465


628/Unknown  141s 224ms/step - accuracy: 0.9382 - loss: 302.6730


629/Unknown  141s 224ms/step - accuracy: 0.9382 - loss: 302.5998


630/Unknown  142s 224ms/step - accuracy: 0.9382 - loss: 302.5268


631/Unknown  142s 224ms/step - accuracy: 0.9382 - loss: 302.4539


632/Unknown  142s 224ms/step - accuracy: 0.9382 - loss: 302.3810


633/Unknown  142s 224ms/step - accuracy: 0.9382 - loss: 302.3086


634/Unknown  143s 224ms/step - accuracy: 0.9382 - loss: 302.2364


635/Unknown  143s 224ms/step - accuracy: 0.9382 - loss: 302.1645


636/Unknown  143s 224ms/step - accuracy: 0.9383 - loss: 302.0930


637/Unknown  143s 224ms/step - accuracy: 0.9383 - loss: 302.0216


638/Unknown  144s 224ms/step - accuracy: 0.9383 - loss: 301.9502


639/Unknown  144s 224ms/step - accuracy: 0.9383 - loss: 301.8791

/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/trainers/epoch_iterator.py:151: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.
  self._interrupted_warning()



639/639 ━━━━━━━━━━━━━━━━━━━━ 160s 249ms/step - accuracy: 0.9383 - loss: 301.8082 - val_accuracy: 0.9485 - val_loss: 235.7996

Model training finished.
Evaluating model performance...

  1/Unknown  0s 331ms/step - accuracy: 0.9623 - loss: 160.6135


  2/Unknown  0s 119ms/step - accuracy: 0.9557 - loss: 181.4366


  3/Unknown  1s 131ms/step - accuracy: 0.9524 - loss: 198.4659


  4/Unknown  1s 129ms/step - accuracy: 0.9502 - loss: 209.3009


  5/Unknown  1s 133ms/step - accuracy: 0.9499 - loss: 215.6982


  6/Unknown  1s 131ms/step - accuracy: 0.9499 - loss: 219.7466


  7/Unknown  1s 132ms/step - accuracy: 0.9502 - loss: 220.2296


  8/Unknown  1s 132ms/step - accuracy: 0.9504 - loss: 219.6000


  9/Unknown  1s 133ms/step - accuracy: 0.9506 - loss: 218.5403


 10/Unknown  2s 133ms/step - accuracy: 0.9507 - loss: 217.4007


 11/Unknown  2s 134ms/step - accuracy: 0.9507 - loss: 216.4865


 12/Unknown  2s 133ms/step - accuracy: 0.9504 - loss: 215.7090


 13/Unknown  2s 135ms/step - accuracy: 0.9502 - loss: 215.4628


 14/Unknown  2s 135ms/step - accuracy: 0.9500 - loss: 215.0735


 15/Unknown  2s 134ms/step - accuracy: 0.9499 - loss: 214.8078


 16/Unknown  2s 134ms/step - accuracy: 0.9500 - loss: 214.3558


 17/Unknown  2s 134ms/step - accuracy: 0.9500 - loss: 213.9521


 18/Unknown  3s 135ms/step - accuracy: 0.9501 - loss: 213.9012


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 22/Unknown  3s 135ms/step - accuracy: 0.9500 - loss: 214.8618


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 68/Unknown  9s 136ms/step - accuracy: 0.9490 - loss: 221.7653


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377/377 ━━━━━━━━━━━━━━━━━━━━ 53s 139ms/step - accuracy: 0.9486 - loss: 229.2270

Test accuracy: 94.94%

您应该在测试集上获得超过 95% 的准确率。

要增加模型的学习能力，您可以尝试增加 encoding_size 值，或在 VSN 层之上堆叠多个 GRN 层。这可能还需要增加 dropout_rate 值以避免过拟合。

HuggingFace 上提供的示例

训练模型 演示
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使用门控残差和变量选择网络进行分类
◆ 简介
◆ 数据集
◆ 设置
◆ 准备数据
◆ 定义数据集元数据
◆ 为训练和评估创建一个 tf.data.Dataset
◆ 创建模型输入
◆ 实现门控线性单元
◆ 实现门控残差网络
◆ 实现变量选择网络
◆ 创建门控残差和变量选择网络模型
◆ 编译、训练和评估模型