摘要：
要创建要素列，请调用列模块的tf feature_ Function。该模块中常用的九个功能如下图所示。所有九个函数都将返回CategoryColumn或DenseColumn对象，但不会返回从这两个类继承的bucketized_Column。numeric_列是最常用的数字列。categorical_column_with_标识分类标识列，一个pot代码，相当于bucket列，其中每个bucket都是整数。categorical_ column_with_ vocabulary_List是一个带有一个pot代码的分类词汇列。字典由列表指定。categorical_column_with_hash_Bucket哈希列，当整数或字典较大时使用。indicator_列指示符列，由CategoricalColumn生成，一锅代码嵌入_列嵌入在列中，由CategorialColumn生成。需要学习嵌入的向量分布参数。crossed_Column交叉列，可按category_Column_with_hash_存储桶的任何类别列划分。
特征列 通常用于对结构化数据实施特征工程时候使用，图像或者文本数据一般不会用到特征列。
一，特征列用法概述
使用特征列可以将类别特征转换为one-hot编码特征，将连续特征构建分桶特征，以及对多个特征生成交叉特征等等。
要创建特征列，请调用 tf.feature_column 模块的函数。该模块中常用的九个函数如下图所示，所有九个函数都会返回一个 Categorical-Column 或一个 Dense-Column 对象，但却不会返回 bucketized_column，后者继承自这两个类。
注意：所有的Catogorical Column类型最终都要通过indicator_column转换成Dense Column类型才能传入模型！
- numeric_column 数值列，最常用。
- bucketized_column 分桶列，由数值列生成，可以由一个数值列出多个特征，one-hot编码。
- categorical_column_with_identity 分类标识列，one-hot编码，相当于分桶列每个桶为1个整数的情况。
- categorical_column_with_vocabulary_list 分类词汇列，one-hot编码，由list指定词典。
- categorical_column_with_vocabulary_file 分类词汇列，由文件file指定词典。
- categorical_column_with_hash_bucket 哈希列，整数或词典较大时采用。
- indicator_column 指标列，由Categorical Column生成，one-hot编码
- embedding_column 嵌入列，由Categorical Column生成，嵌入矢量分布参数需要学习。嵌入矢量维数建议取类别数量的 4 次方根。
- crossed_column 交叉列，可以由除categorical_column_with_hash_bucket的任意分类列构成。
二，特征列使用范例
以下是一个使用特征列解决Titanic生存问题的完整范例。
import datetime import numpy as np import pandas as pd from matplotlib import pyplot as plt import tensorflow as tf from tensorflow.keras import layers,models # 打印日志 def printlog(info): nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') print(" "+"=========="*8 + "%s"%nowtime) print(info+'... ') # ================================================================================ # 一，构建数据管道 # ================================================================================ printlog("step1: prepare dataset...") dftrain_raw = pd.read_csv("./data/titanic/train.csv") dftest_raw = pd.read_csv("./data/titanic/test.csv") dfraw = pd.concat([dftrain_raw,dftest_raw]) def prepare_dfdata(dfraw): dfdata = dfraw.copy() dfdata.columns = [x.lower() for x in dfdata.columns] dfdata = dfdata.rename(columns={'survived':'label'}) dfdata = dfdata.drop(['passengerid','name'],axis = 1) for col,dtype in dict(dfdata.dtypes).items(): # 判断是否包含缺失值 if dfdata[col].hasnans: # 添加标识是否缺失列 dfdata[col + '_nan'] = pd.isna(dfdata[col]).astype('int32') # 填充 if dtype not in [np.object,np.str,np.unicode]: dfdata[col].fillna(dfdata[col].mean(),inplace = True) else: dfdata[col].fillna('',inplace = True) return(dfdata) dfdata = prepare_dfdata(dfraw) dftrain = dfdata.iloc[0:len(dftrain_raw),:] dftest = dfdata.iloc[len(dftrain_raw):,:] # 从 dataframe 导入数据 def df_to_dataset(df, shuffle=True, batch_size=32): dfdata = df.copy() if 'label' not in dfdata.columns: ds = tf.data.Dataset.from_tensor_slices(dfdata.to_dict(orient = 'list')) else: labels = dfdata.pop('label') ds = tf.data.Dataset.from_tensor_slices((dfdata.to_dict(orient = 'list'), labels)) if shuffle: ds = ds.shuffle(buffer_size=len(dfdata)) ds = ds.batch(batch_size) return ds ds_train = df_to_dataset(dftrain) ds_test = df_to_dataset(dftest) # ================================================================================ # 二，定义特征列 # ================================================================================ printlog("step2: make feature columns...") feature_columns = [] # 数值列 for col in ['age','fare','parch','sibsp'] + [ c for c in dfdata.columns if c.endswith('_nan')]: feature_columns.append(tf.feature_column.numeric_column(col)) # 分桶列 age = tf.feature_column.numeric_column('age') age_buckets = tf.feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65]) feature_columns.append(age_buckets) # 类别列 # 注意：所有的Catogorical Column类型最终都要通过indicator_column转换成Dense Column类型才能传入模型！！ sex = tf.feature_column.indicator_column( tf.feature_column.categorical_column_with_vocabulary_list( key='sex',vocabulary_list=["male", "female"])) feature_columns.append(sex) pclass = tf.feature_column.indicator_column( tf.feature_column.categorical_column_with_vocabulary_list( key='pclass',vocabulary_list=[1,2,3])) feature_columns.append(pclass) ticket = tf.feature_column.indicator_column( tf.feature_column.categorical_column_with_hash_bucket('ticket',3)) feature_columns.append(ticket) embarked = tf.feature_column.indicator_column( tf.feature_column.categorical_column_with_vocabulary_list( key='embarked',vocabulary_list=['S','C','B'])) feature_columns.append(embarked) # 嵌入列 cabin = tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_hash_bucket('cabin',32),2) feature_columns.append(cabin) # 交叉列 pclass_cate = tf.feature_column.categorical_column_with_vocabulary_list( key='pclass',vocabulary_list=[1,2,3]) crossed_feature = tf.feature_column.indicator_column( tf.feature_column.crossed_column([age_buckets, pclass_cate],hash_bucket_size=15)) feature_columns.append(crossed_feature) # ================================================================================ # 三，定义模型 # ================================================================================ printlog("step3: define model...") tf.keras.backend.clear_session() model = tf.keras.Sequential([ layers.DenseFeatures(feature_columns), #将特征列放入到tf.keras.layers.DenseFeatures中!!! layers.Dense(64, activation='relu'), layers.Dense(64, activation='relu'), layers.Dense(1, activation='sigmoid') ]) # ================================================================================ # 四，训练模型 # ================================================================================ printlog("step4: train model...") model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) history = model.fit(ds_train, validation_data=ds_test, epochs=10) # ================================================================================ # 五，评估模型 # ================================================================================ printlog("step5: eval model...") model.summary() %matplotlib inline %config InlineBackend.figure_format = 'svg' import matplotlib.pyplot as plt def plot_metric(history, metric): train_metrics = history.history[metric] val_metrics = history.history['val_'+metric] epochs = range(1, len(train_metrics) + 1) plt.plot(epochs, train_metrics, 'bo--') plt.plot(epochs, val_metrics, 'ro-') plt.title('Training and validation '+ metric) plt.xlabel("Epochs") plt.ylabel(metric) plt.legend(["train_"+metric, 'val_'+metric]) plt.show() plot_metric(history,"accuracy")
================================================================================2020-04-13 02:29:07 step1: prepare dataset...... ================================================================================2020-04-13 02:29:08 step2: make feature columns...... ================================================================================2020-04-13 02:29:08 step3: define model...... ================================================================================2020-04-13 02:29:08 step4: train model...... Epoch 1/10 23/23 [==============================] - 0s 21ms/step - loss: 0.7117 - accuracy: 0.6615 - val_loss: 0.5706 - val_accuracy: 0.7039 Epoch 2/10 23/23 [==============================] - 0s 3ms/step - loss: 0.5920 - accuracy: 0.7022 - val_loss: 0.6129 - val_accuracy: 0.6648 Epoch 3/10 23/23 [==============================] - 0s 3ms/step - loss: 0.6388 - accuracy: 0.7079 - val_loss: 0.5196 - val_accuracy: 0.7374 Epoch 4/10 23/23 [==============================] - 0s 3ms/step - loss: 0.5950 - accuracy: 0.7219 - val_loss: 0.5028 - val_accuracy: 0.7318 Epoch 5/10 23/23 [==============================] - 0s 3ms/step - loss: 0.5166 - accuracy: 0.7486 - val_loss: 0.4975 - val_accuracy: 0.7318 Epoch 6/10 23/23 [==============================] - 0s 3ms/step - loss: 0.5260 - accuracy: 0.7612 - val_loss: 0.5045 - val_accuracy: 0.7821 Epoch 7/10 23/23 [==============================] - 0s 3ms/step - loss: 0.4957 - accuracy: 0.7697 - val_loss: 0.4756 - val_accuracy: 0.7709 Epoch 8/10 23/23 [==============================] - 0s 3ms/step - loss: 0.4848 - accuracy: 0.7837 - val_loss: 0.4532 - val_accuracy: 0.8045 Epoch 9/10 23/23 [==============================] - 0s 3ms/step - loss: 0.4636 - accuracy: 0.8006 - val_loss: 0.4561 - val_accuracy: 0.7989 Epoch 10/10 23/23 [==============================] - 0s 3ms/step - loss: 0.4784 - accuracy: 0.7907 - val_loss: 0.4722 - val_accuracy: 0.7821 ================================================================================2020-04-13 02:29:11 step5: eval model...... Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_features (DenseFeature multiple 64 _________________________________________________________________ dense (Dense) multiple 3008 _________________________________________________________________ dense_1 (Dense) multiple 4160 _________________________________________________________________ dense_2 (Dense) multiple 65 ================================================================= Total params: 7,297 Trainable params: 7,297 Non-trainable params: 0 _________________________________________________________________
参考：
开源电子书地址：https://lyhue1991.github.io/eat_tensorflow2_in_30_days/
GitHub 项目地址：https://github.com/lyhue1991/eat_tensorflow2_in_30_days