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• 作者：韩信子@ShowMeAI
• 深度学习实战系列：www.showmeai.tech/tutorials/4…
• TensorFlow 实战系列：www.showmeai.tech/tutorials/4…
• 本文地址：www.showmeai.tech/article-det…
• 声明：版权一切，转载请联系渠道与作者并注明出处
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自 Transformers 呈现以来，依据它的结构已经颠覆了自然语言处理和计算机视觉，带来各种非结构化数据事务场景和使命的巨大作用突破，接着咱们把目光转向了结构化事务数据，它是否能在结构化表格数据上相同有惊人的作用表现呢？

答案是YES！亚马逊在论文中提出的 TabTransformer，是一种把结构调整后适应于结构化表格数据的网络结构，它更擅长于捕捉传统结构化表格数据中不同类型的数据信息，并将其结合以完结预估使命。下面ShowMeAI给咱们解说构建 TabTransformer 并将其运用于结构化数据上的进程。

环境设置

本篇运用到的深度学习框架为TensorFlow，咱们需要装置2.7或更高版别， 咱们还需要装置一下 TensorFlow插件addons，装置的进程咱们能够经过下述命令完结：

pip install -U tensorflow tensorflow-addons

关于本篇代码完成中运用到的TensorFlow工具库，咱们能够检查ShowMeAI制作的TensorFlow速查手册快学快用：

• AI笔直范畴工具库速查表 | TensorFlow2建模速查&运用速查

接下来咱们导入工具库

import math
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_addons as tfa
import matplotlib.pyplot as plt

数据阐明

ShowMeAI在本例中运用到的是 美国人口普查收入数据集，使命是依据人口基本信息猜测其年收入是否或许超越 50,000 美元，是一个二分类问题。

数据集能够在以下地址下载：

archive.ics.uci.edu/ml/datasets…

archive.ics.uci.edu/ml/machine-…

数据从美国1994年人口普查数据库抽取而来，能够用来猜测居民收入是否超越50K/year。该数据集类变量为年收入是否超越50k，特点变量包括年纪、工种、学历、工作、人种等重要信息，值得一提的是，14个特点变量中有7个类别型变量。数据集各特点是：其中序号0~13是特点，14是类别。

字段序号	字段名	意义	类型
0	age	年纪	Double
1	workclass	作业类型*	string
2	fnlwgt	序号	string
3	education	教育程度*	string
4	education_num	受教育时刻	double
5	maritial_status	婚姻状况*	string
6	occupation	工作*	string
7	relationship	关系*	string
8	race	种族*	string
9	sex	性别*	string
10	capital_gain	资本收益	string
11	capital_loss	资本损失	string
12	hours_per_week	每周作业小时数	double
13	native_country	原籍*	string
14(label)	income	收入标签	string

咱们先用pandas读取数据到dataframe中：

CSV_HEADER = [
    "age",
    "workclass",
    "fnlwgt",
    "education",
    "education_num",
    "marital_status",
    "occupation",
    "relationship",
    "race",
    "gender",
    "capital_gain",
    "capital_loss",
    "hours_per_week",
    "native_country",
    "income_bracket",
]
train_data_url = (
    "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
)
train_data = pd.read_csv(train_data_url, header=None, names=CSV_HEADER)
test_data_url = (
    "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test"
)
test_data = pd.read_csv(test_data_url, header=None, names=CSV_HEADER)
print(f"Train dataset shape: {train_data.shape}")
print(f"Test dataset shape: {test_data.shape}")
Train dataset shape: (32561, 15)
Test dataset shape: (16282, 15)

咱们做点数据清洗，把测验集第一条记录剔除（它不是有用的数据示例），把类标签中的尾随的“点”去掉。

test_data = test_data[1:]
test_data.income_bracket = test_data.income_bracket.apply(
    lambda value: value.replace(".", "")
)

再把练习集和测验集存回单独的 CSV 文件中。

train_data_file = "train_data.csv"
test_data_file = "test_data.csv"
train_data.to_csv(train_data_file, index=False, header=False)
test_data.to_csv(test_data_file, index=False, header=False)

模型原理

TabTransformer的模型架构如下所示：

咱们能够看到，类别型的特征，很适合在 embedding 后，送入 transformer 模块进行深度穿插组合与信息挖掘，得到的信息与右侧的接连值特征进行拼接，再送入全衔接的 MLP 模块进行组合和完结最终的使命（分类或者回归）。

模型完成

界说数据集元数据

要完成模型，咱们先对输入数据字段，区分不同的类型（数值型特征与类别型特征）。咱们会对不同类型的特征，运用不同的方法进行处理和完结特征工程（例如数值型的特征进行幅度缩放，类别型的特征进行编码处理）。

## 数值特征字段
NUMERIC_FEATURE_NAMES = [
    "age",
    "education_num",
    "capital_gain",
    "capital_loss",
    "hours_per_week",
]
## 类别型特征字段及其取值列表
CATEGORICAL_FEATURES_WITH_VOCABULARY = {
    "workclass": sorted(list(train_data["workclass"].unique())),
    "education": sorted(list(train_data["education"].unique())),
    "marital_status": sorted(list(train_data["marital_status"].unique())),
    "occupation": sorted(list(train_data["occupation"].unique())),
    "relationship": sorted(list(train_data["relationship"].unique())),
    "race": sorted(list(train_data["race"].unique())),
    "gender": sorted(list(train_data["gender"].unique())),
    "native_country": sorted(list(train_data["native_country"].unique())),
}
## 权重字段
WEIGHT_COLUMN_NAME = "fnlwgt"
## 类别型字段称号
CATEGORICAL_FEATURE_NAMES = list(CATEGORICAL_FEATURES_WITH_VOCABULARY.keys())
## 一切的输入特征
FEATURE_NAMES = NUMERIC_FEATURE_NAMES + CATEGORICAL_FEATURE_NAMES
## 默认填充的取值
COLUMN_DEFAULTS = [
    [0.0] if feature_name in NUMERIC_FEATURE_NAMES + [WEIGHT_COLUMN_NAME] else ["NA"]
    for feature_name in CSV_HEADER
]
## 方针字段
TARGET_FEATURE_NAME = "income_bracket"
## 方针字段取值
TARGET_LABELS = [" <=50K", " >50K"]

装备超参数

咱们为神经网络的结构和练习进程的超参数进行设置，如下。

# 学习率
LEARNING_RATE = 0.001
# 学习率衰减
WEIGHT_DECAY = 0.0001
# 随机失活 概率参数
DROPOUT_RATE = 0.2
# 批数据巨细
BATCH_SIZE = 265
# 总练习轮次数
NUM_EPOCHS = 15
# transformer块的数量
NUM_TRANSFORMER_BLOCKS = 3
# 注意力头的数量
NUM_HEADS = 4
# 类别型embedding嵌入的维度
EMBEDDING_DIMS = 16
# MLP隐层单元数量
MLP_HIDDEN_UNITS_FACTORS = [
    2,
    1,
]
# MLP块的数量
NUM_MLP_BLOCKS = 2

完成数据读取管道

下面咱们界说一个输入函数，它担任读取和解析文件，并对特征和标签处理，放入 tf.data.Dataset，以便后续练习和评价。

target_label_lookup = layers.StringLookup(
    vocabulary=TARGET_LABELS, mask_token=None, num_oov_indices=0
)
def prepare_example(features, target):
    target_index = target_label_lookup(target)
    weights = features.pop(WEIGHT_COLUMN_NAME)
    return features, target_index, weights
# 从csv中读取数据
def get_dataset_from_csv(csv_file_path, batch_size=128, shuffle=False):
    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,
        na_value="?",
        shuffle=shuffle,
    ).map(prepare_example, num_parallel_calls=tf.data.AUTOTUNE, deterministic=False)
    return dataset.cache()

模型构建与评价

def run_experiment(
    model,
    train_data_file,
    test_data_file,
    num_epochs,
    learning_rate,
    weight_decay,
    batch_size,
):
    # 优化器
    optimizer = tfa.optimizers.AdamW(
        learning_rate=learning_rate, weight_decay=weight_decay
    )
    # 模型编译
    model.compile(
        optimizer=optimizer,
        loss=keras.losses.BinaryCrossentropy(),
        metrics=[keras.metrics.BinaryAccuracy(name="accuracy")],
    )
    # 练习集与验证集
    train_dataset = get_dataset_from_csv(train_data_file, batch_size, shuffle=True)
    validation_dataset = get_dataset_from_csv(test_data_file, batch_size)
    # 模型练习
    print("Start training the model...")
    history = model.fit(
        train_dataset, epochs=num_epochs, validation_data=validation_dataset
    )
    print("Model training finished")
    # 模型评价
    _, accuracy = model.evaluate(validation_dataset, verbose=0)
    print(f"Validation accuracy: {round(accuracy * 100, 2)}%")
    return history

① 创立模型输入

依据 TensorFlow 的输入要求，咱们将模型的输入界说为字典，其中『key/键』是特征称号，『value/值』为 keras.layers.Input具有相应特征形状的张量和数据类型。

def create_model_inputs():
    inputs = {}
    for feature_name in FEATURE_NAMES:
        if feature_name in NUMERIC_FEATURE_NAMES:
            inputs[feature_name] = layers.Input(
                name=feature_name, shape=(), dtype=tf.float32
            )
        else:
            inputs[feature_name] = layers.Input(
                name=feature_name, shape=(), dtype=tf.string
            )
    return inputs

② 编码特征

咱们界说一个encode_inputs函数，返回encoded_categorical_feature_list和 numerical_feature_list。咱们将分类特征编码为嵌入，运用固定的embedding_dims对于一切功能， 无论他们的词汇量巨细。 这是 Transformer 模型所必需的。

def encode_inputs(inputs, embedding_dims):
    encoded_categorical_feature_list = []
    numerical_feature_list = []
    for feature_name in inputs:
        if feature_name in CATEGORICAL_FEATURE_NAMES:
            # 获取类别型特征的不同取值(vocabulary)
            vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]
            # 构建lookup table去构建 类别型取值 和 索引 的相互映射
            lookup = layers.StringLookup(
                vocabulary=vocabulary,
                mask_token=None,
                num_oov_indices=0,
                output_mode="int",
            )
            # 类别型字符串取值 转为 整型索引
            encoded_feature = lookup(inputs[feature_name])
            # 构建embedding层
            embedding = layers.Embedding(
                input_dim=len(vocabulary), output_dim=embedding_dims
            )
            # 为索引构建embedding嵌入
            encoded_categorical_feature = embedding(encoded_feature)
            encoded_categorical_feature_list.append(encoded_categorical_feature)
        else:
            # 数值型特征
            numerical_feature = tf.expand_dims(inputs[feature_name], -1)
            numerical_feature_list.append(numerical_feature)
    return encoded_categorical_feature_list, numerical_feature_list

③ MLP模块完成

网络中不可或缺的部分是 MLP 全衔接板块，下面是它的简略完成：

def create_mlp(hidden_units, dropout_rate, activation, normalization_layer, name=None):
    mlp_layers = []
    for units in hidden_units:
        mlp_layers.append(normalization_layer),
        mlp_layers.append(layers.Dense(units, activation=activation))
        mlp_layers.append(layers.Dropout(dropout_rate))
    return keras.Sequential(mlp_layers, name=name)

④ 模型完成1：基线模型

为了比照作用，咱们先简略运用MLP（多层前馈网络）进行建模，代码和注释如下。

def create_baseline_model(
    embedding_dims, num_mlp_blocks, mlp_hidden_units_factors, dropout_rate
):
    # 创立输入.
    inputs = create_model_inputs()
    # 特征编码
    encoded_categorical_feature_list, numerical_feature_list = encode_inputs(
        inputs, embedding_dims
    )
    # 拼接一切特征
    features = layers.concatenate(
        encoded_categorical_feature_list + numerical_feature_list
    )
    # 前向计算
    feedforward_units = [features.shape[-1]]
    # 构建全衔接，而且增加跳跃衔接(skip-connection)
    for layer_idx in range(num_mlp_blocks):
        features = create_mlp(
            hidden_units=feedforward_units,
            dropout_rate=dropout_rate,
            activation=keras.activations.gelu,
            normalization_layer=layers.LayerNormalization(epsilon=1e-6),
            name=f"feedforward_{layer_idx}",
        )(features)
    # MLP全衔接的隐层结果
    mlp_hidden_units = [
        factor * features.shape[-1] for factor in mlp_hidden_units_factors
    ]
    # 最终的MLP网络
    features = create_mlp(
        hidden_units=mlp_hidden_units,
        dropout_rate=dropout_rate,
        activation=keras.activations.selu,
        normalization_layer=layers.BatchNormalization(),
        name="MLP",
    )(features)
    # 增加sigmoid构建二分类器
    outputs = layers.Dense(units=1, activation="sigmoid", name="sigmoid")(features)
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model
# 完好的模型
baseline_model = create_baseline_model(
    embedding_dims=EMBEDDING_DIMS,
    num_mlp_blocks=NUM_MLP_BLOCKS,
    mlp_hidden_units_factors=MLP_HIDDEN_UNITS_FACTORS,
    dropout_rate=DROPOUT_RATE,
)
print("Total model weights:", baseline_model.count_params())
keras.utils.plot_model(baseline_model, show_shapes=True, rankdir="LR")
# Total model weights: 109629

上述模型构建完结之后，咱们经过plot_model操作，绘制出模型结构如下：

接下来咱们练习和评价一下基线模型：

history = run_experiment(
    model=baseline_model,
    train_data_file=train_data_file,
    test_data_file=test_data_file,
    num_epochs=NUM_EPOCHS,
    learning_rate=LEARNING_RATE,
    weight_decay=WEIGHT_DECAY,
    batch_size=BATCH_SIZE,
)

输出的练习进程日志如下：

Start training the model...
Epoch 1/15
123/123 [==============================] - 6s 25ms/step - loss: 110178.8203 - accuracy: 0.7478 - val_loss: 92703.0859 - val_accuracy: 0.7825
Epoch 2/15
123/123 [==============================] - 2s 14ms/step - loss: 90979.8125 - accuracy: 0.7675 - val_loss: 71798.9219 - val_accuracy: 0.8001
Epoch 3/15
123/123 [==============================] - 2s 14ms/step - loss: 77226.5547 - accuracy: 0.7902 - val_loss: 68581.0312 - val_accuracy: 0.8168
Epoch 4/15
123/123 [==============================] - 2s 14ms/step - loss: 72652.2422 - accuracy: 0.8004 - val_loss: 70084.0469 - val_accuracy: 0.7974
Epoch 5/15
123/123 [==============================] - 2s 14ms/step - loss: 71207.9375 - accuracy: 0.8033 - val_loss: 66552.1719 - val_accuracy: 0.8130
Epoch 6/15
123/123 [==============================] - 2s 14ms/step - loss: 69321.4375 - accuracy: 0.8091 - val_loss: 65837.0469 - val_accuracy: 0.8149
Epoch 7/15
123/123 [==============================] - 2s 14ms/step - loss: 68839.3359 - accuracy: 0.8099 - val_loss: 65613.0156 - val_accuracy: 0.8187
Epoch 8/15
123/123 [==============================] - 2s 14ms/step - loss: 68126.7344 - accuracy: 0.8124 - val_loss: 66155.8594 - val_accuracy: 0.8108
Epoch 9/15
123/123 [==============================] - 2s 14ms/step - loss: 67768.9844 - accuracy: 0.8147 - val_loss: 66705.8047 - val_accuracy: 0.8230
Epoch 10/15
123/123 [==============================] - 2s 14ms/step - loss: 67482.5859 - accuracy: 0.8151 - val_loss: 65668.3672 - val_accuracy: 0.8143
Epoch 11/15
123/123 [==============================] - 2s 14ms/step - loss: 66792.6875 - accuracy: 0.8181 - val_loss: 66536.3828 - val_accuracy: 0.8233
Epoch 12/15
123/123 [==============================] - 2s 14ms/step - loss: 65610.4531 - accuracy: 0.8229 - val_loss: 70377.7266 - val_accuracy: 0.8256
Epoch 13/15
123/123 [==============================] - 2s 14ms/step - loss: 63930.2500 - accuracy: 0.8282 - val_loss: 68294.8516 - val_accuracy: 0.8289
Epoch 14/15
123/123 [==============================] - 2s 14ms/step - loss: 63420.1562 - accuracy: 0.8323 - val_loss: 63050.5859 - val_accuracy: 0.8204
Epoch 15/15
123/123 [==============================] - 2s 14ms/step - loss: 62619.4531 - accuracy: 0.8345 - val_loss: 66933.7500 - val_accuracy: 0.8177
Model training finished
Validation accuracy: 81.77%

咱们能够看到基线模型(全衔接MLP网络)完成了约 82% 的验证准确度。

⑤ 模型完成2：TabTransformer

TabTransformer 架构的作业原理如下：

• 一切类别型特征都被编码为嵌入，运用相同的 embedding_dims。
• 将列嵌入（每个类别型特征的一个嵌入向量）增加类别型特征嵌入中。
• 嵌入的类别型特征被输入到一系列的 Transformer 块中。 每个 Transformer 块由一个多头自注意力层和一个前馈层组成。
• 最终 Transformer 层的输出， 与输入的数值型特征衔接，并输入到最终的 MLP 块中。
• 尾部由一个 softmax结构完结分类。

def create_tabtransformer_classifier(
    num_transformer_blocks,
    num_heads,
    embedding_dims,
    mlp_hidden_units_factors,
    dropout_rate,
    use_column_embedding=False,
):
    # 构建输入
    inputs = create_model_inputs()
    # 编码特征
    encoded_categorical_feature_list, numerical_feature_list = encode_inputs(
        inputs, embedding_dims
    )
    # 堆叠类别型特征的embeddings，为输入Tansformer做准备
    encoded_categorical_features = tf.stack(encoded_categorical_feature_list, axis=1)
    # 拼接数值型特征
    numerical_features = layers.concatenate(numerical_feature_list)
    # embedding
    if use_column_embedding:
        num_columns = encoded_categorical_features.shape[1]
        column_embedding = layers.Embedding(
            input_dim=num_columns, output_dim=embedding_dims
        )
        column_indices = tf.range(start=0, limit=num_columns, delta=1)
        encoded_categorical_features = encoded_categorical_features + column_embedding(
            column_indices
        )
    # 构建Transformer块
    for block_idx in range(num_transformer_blocks):
        # 多头自注意力
        attention_output = layers.MultiHeadAttention(
            num_heads=num_heads,
            key_dim=embedding_dims,
            dropout=dropout_rate,
            name=f"multihead_attention_{block_idx}",
        )(encoded_categorical_features, encoded_categorical_features)
        # 第1个跳接/Skip connection
        x = layers.Add(name=f"skip_connection1_{block_idx}")(
            [attention_output, encoded_categorical_features]
        )
        # 第1个层归一化/Layer normalization
        x = layers.LayerNormalization(name=f"layer_norm1_{block_idx}", epsilon=1e-6)(x)
        # 全衔接层
        feedforward_output = create_mlp(
            hidden_units=[embedding_dims],
            dropout_rate=dropout_rate,
            activation=keras.activations.gelu,
            normalization_layer=layers.LayerNormalization(epsilon=1e-6),
            name=f"feedforward_{block_idx}",
        )(x)
        # 第2个跳接/Skip connection
        x = layers.Add(name=f"skip_connection2_{block_idx}")([feedforward_output, x])
        # 第2个层归一化/Layer normalization
        encoded_categorical_features = layers.LayerNormalization(
            name=f"layer_norm2_{block_idx}", epsilon=1e-6
        )(x)
    # 展平embeddings
    categorical_features = layers.Flatten()(encoded_categorical_features)
    # 对数值型特征做层归一化
    numerical_features = layers.LayerNormalization(epsilon=1e-6)(numerical_features)
    # 拼接作为最终MLP的输入
    features = layers.concatenate([categorical_features, numerical_features])
    # 计算MLP隐层单元
    mlp_hidden_units = [
        factor * features.shape[-1] for factor in mlp_hidden_units_factors
    ]
    # 构建最终的MLP.
    features = create_mlp(
        hidden_units=mlp_hidden_units,
        dropout_rate=dropout_rate,
        activation=keras.activations.selu,
        normalization_layer=layers.BatchNormalization(),
        name="MLP",
    )(features)
    # 增加sigmoid构建二分类
    outputs = layers.Dense(units=1, activation="sigmoid", name="sigmoid")(features)
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model
tabtransformer_model = create_tabtransformer_classifier(
    num_transformer_blocks=NUM_TRANSFORMER_BLOCKS,
    num_heads=NUM_HEADS,
    embedding_dims=EMBEDDING_DIMS,
    mlp_hidden_units_factors=MLP_HIDDEN_UNITS_FACTORS,
    dropout_rate=DROPOUT_RATE,
)
print("Total model weights:", tabtransformer_model.count_params())
keras.utils.plot_model(tabtransformer_model, show_shapes=True, rankdir="LR")
#Total model weights: 87479

最终输出的模型结构示意图如下（由于模型结构较深，整体很长，点击放大）

下面咱们练习和评价一下TabTransformer 模型的作用：

history = run_experiment(
    model=tabtransformer_model,
    train_data_file=train_data_file,
    test_data_file=test_data_file,
    num_epochs=NUM_EPOCHS,
    learning_rate=LEARNING_RATE,
    weight_decay=WEIGHT_DECAY,
    batch_size=BATCH_SIZE,
)
Start training the model...
Epoch 1/15
123/123 [==============================] - 13s 61ms/step - loss: 82503.1641 - accuracy: 0.7944 - val_loss: 64260.2305 - val_accuracy: 0.8421
Epoch 2/15
123/123 [==============================] - 6s 51ms/step - loss: 68677.9375 - accuracy: 0.8251 - val_loss: 63819.8633 - val_accuracy: 0.8389
Epoch 3/15
123/123 [==============================] - 6s 51ms/step - loss: 66703.8984 - accuracy: 0.8301 - val_loss: 63052.8789 - val_accuracy: 0.8428
Epoch 4/15
123/123 [==============================] - 6s 51ms/step - loss: 65287.8672 - accuracy: 0.8342 - val_loss: 61593.1484 - val_accuracy: 0.8451
Epoch 5/15
123/123 [==============================] - 6s 52ms/step - loss: 63968.8594 - accuracy: 0.8379 - val_loss: 61385.4531 - val_accuracy: 0.8442
Epoch 6/15
123/123 [==============================] - 6s 51ms/step - loss: 63645.7812 - accuracy: 0.8394 - val_loss: 61332.3281 - val_accuracy: 0.8447
Epoch 7/15
123/123 [==============================] - 6s 51ms/step - loss: 62778.6055 - accuracy: 0.8412 - val_loss: 61342.5352 - val_accuracy: 0.8461
Epoch 8/15
123/123 [==============================] - 6s 51ms/step - loss: 62815.6992 - accuracy: 0.8398 - val_loss: 61220.8242 - val_accuracy: 0.8460
Epoch 9/15
123/123 [==============================] - 6s 52ms/step - loss: 62191.1016 - accuracy: 0.8416 - val_loss: 61055.9102 - val_accuracy: 0.8452
Epoch 10/15
123/123 [==============================] - 6s 51ms/step - loss: 61992.1602 - accuracy: 0.8439 - val_loss: 61251.8047 - val_accuracy: 0.8441
Epoch 11/15
123/123 [==============================] - 6s 50ms/step - loss: 61745.1289 - accuracy: 0.8429 - val_loss: 61364.7695 - val_accuracy: 0.8445
Epoch 12/15
123/123 [==============================] - 6s 51ms/step - loss: 61696.3477 - accuracy: 0.8445 - val_loss: 61074.3594 - val_accuracy: 0.8450
Epoch 13/15
123/123 [==============================] - 6s 51ms/step - loss: 61569.1719 - accuracy: 0.8436 - val_loss: 61844.9688 - val_accuracy: 0.8456
Epoch 14/15
123/123 [==============================] - 6s 51ms/step - loss: 61343.0898 - accuracy: 0.8445 - val_loss: 61702.8828 - val_accuracy: 0.8455
Epoch 15/15
123/123 [==============================] - 6s 51ms/step - loss: 61355.0547 - accuracy: 0.8504 - val_loss: 61272.2852 - val_accuracy: 0.8495
Model training finished
Validation accuracy: 84.55%

TabTransformer 模型完成了约 85% 的验证准确度，相比于直接运用全衔接网络作用有必定的提高。

参考资料

• TabTransformer：arxiv.org/abs/2012.06…
• TensorFlow插件addons：www.tensorflow.org/addons/over…
• AI笔直范畴工具库速查表 | TensorFlow2建模速查&运用速查：www.showmeai.tech/article-det…