如何使用tensorflow feature_columns作为keras模型的输入 [英] How to use tensorflow feature_columns as input to a keras model

问题描述

Tensorflow的 feature_columns API 对于非数值特征处理非常有用.但是，当前的API文档更多是关于在tensorflow Estimator中使用feature_columns.是否可以使用feature_columns进行分类要素表示，然后基于tf.keras构建模型?

Tensorflow's feature_columns API is quite useful for non-numerical feature processing. However, the current API doc is more about using feature_columns with tensorflow Estimator. Is there a possible way to use feature_columns for categorical features representation and then build a model based on tf.keras?

我找到的唯一参考文献是以下教程.它显示了如何将要素列提供给Keras顺序模型:链接

The only reference I found is the following tutorial. It shows how to feed feature columns to a Keras Sequential model: Link

代码段如下:

from tensorflow.python.feature_column import feature_column_v2 as fc

feature_columns = [fc.embedding_column(ccv, dimension=3), ...]
feature_layer = fc.FeatureLayer(feature_columns)
model = tf.keras.Sequential([
    feature_layer,
    tf.keras.layers.Dense(128, activation=tf.nn.relu),
    tf.keras.layers.Dense(64, activation=tf.nn.relu),
    tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)
])
...
model.fit(dataset, steps_per_epoch=8) # dataset is created from tensorflow Dataset API

问题是如何在keras功能模型API中使用自定义模型.我尝试了以下操作，但不起作用(tensorflow版本1.12)

The question is how to use a customed model with keras functional model API. I tried the following, but it did not work (tensorflow version 1.12)

feature_layer = fc.FeatureLayer(feature_columns)
dense_features = feature_layer(features) # features is a dict of ndarrays in dataset
layer1 = tf.keras.layers.Dense(128, activation=tf.nn.relu)(dense_features)
layer2 = tf.keras.layers.Dense(64, activation=tf.nn.relu)(layer1)
output = tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)(layer2)
model = Model(inputs=dense_features, outputs=output)

错误日志:

ValueError: Input tensors to a Model must come from `tf.layers.Input`. Received: Tensor("feature_layer/concat:0", shape=(4, 3), dtype=float32) (missing previous layer metadata).

我不知道如何将要素列转换为keras模型的输入.

I don't kown how to transform feature columns to keras model's input.

推荐答案

可以实现所需的行为，并且可以将tf.feature_column和keras functional API组合在一起.而且，实际上，TF文档中没有提及.

The behavior you desire could be achieved and it's able to combine tf.feature_column and keras functional API. And, actually, is not mentioned in TF docs.

此功能至少在TF 2.0.0-beta1中有效，但在以后的发行版中可能会更改甚至简化.

This works at least in TF 2.0.0-beta1, but may being changed or even simplified in further releases.

请在TensorFlow github存储库中检出问题无法将FeatureColumn与Keras Functional API#27416一起使用.在那里，您会找到有关tf.feature_column和Keras Functional API的有用注释.

Please check out issue in TensorFlow github repository Unable to use FeatureColumn with Keras Functional API #27416. There you will find useful comments about tf.feature_column and Keras Functional API.

因为您询问一般方法，所以我只复制上面链接中的示例片段. 更新:下面的代码应该可用

Because you ask about general approach I would just copy the snippet with example from the link above. update: the code below should work

from __future__ import absolute_import, division, print_function

import numpy as np
import pandas as pd

#!pip install tensorflow==2.0.0-alpha0
import tensorflow as tf

from tensorflow import feature_column
from tensorflow import keras
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split

URL = 'https://storage.googleapis.com/applied-dl/heart.csv'
dataframe = pd.read_csv(URL, nrows = 10000)
dataframe.head()

train, test = train_test_split(dataframe, test_size=0.2)
train, val = train_test_split(train, test_size=0.2)
print(len(train), 'train examples')
print(len(val), 'validation examples')
print(len(test), 'test examples')

# Define method to create tf.data dataset from Pandas Dataframe
# This worked with tf 2.0 but does not work with tf 2.2
def df_to_dataset_tf_2_0(dataframe, label_column, shuffle=True, batch_size=32):
    dataframe = dataframe.copy()
    #labels = dataframe.pop(label_column)
    labels = dataframe[label_column]

    ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
    if shuffle:
        ds = ds.shuffle(buffer_size=len(dataframe))
    ds = ds.batch(batch_size)
    return ds

def df_to_dataset(dataframe, label_column, shuffle=True, batch_size=32):
    dataframe = dataframe.copy()
    labels = dataframe.pop(label_column)
    #labels = dataframe[label_column]

    ds = tf.data.Dataset.from_tensor_slices((dataframe.to_dict(orient='list'), labels))
    if shuffle:
        ds = ds.shuffle(buffer_size=len(dataframe))
    ds = ds.batch(batch_size)
    return ds


batch_size = 5 # A small batch sized is used for demonstration purposes
train_ds = df_to_dataset(train, label_column = 'target', batch_size=batch_size)
val_ds = df_to_dataset(val,label_column = 'target',  shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, label_column = 'target', shuffle=False, batch_size=batch_size)

age = feature_column.numeric_column("age")

feature_columns = []
feature_layer_inputs = {}

# numeric cols
for header in ['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'slope', 'ca']:
  feature_columns.append(feature_column.numeric_column(header))
  feature_layer_inputs[header] = tf.keras.Input(shape=(1,), name=header)

# bucketized cols
age_buckets = feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35])
feature_columns.append(age_buckets)

# indicator cols
thal = feature_column.categorical_column_with_vocabulary_list(
      'thal', ['fixed', 'normal', 'reversible'])
thal_one_hot = feature_column.indicator_column(thal)
feature_columns.append(thal_one_hot)
feature_layer_inputs['thal'] = tf.keras.Input(shape=(1,), name='thal', dtype=tf.string)

# embedding cols
thal_embedding = feature_column.embedding_column(thal, dimension=8)
feature_columns.append(thal_embedding)

# crossed cols
crossed_feature = feature_column.crossed_column([age_buckets, thal], hash_bucket_size=1000)
crossed_feature = feature_column.indicator_column(crossed_feature)
feature_columns.append(crossed_feature)



feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
feature_layer_outputs = feature_layer(feature_layer_inputs)

x = layers.Dense(128, activation='relu')(feature_layer_outputs)
x = layers.Dense(64, activation='relu')(x)

baggage_pred = layers.Dense(1, activation='sigmoid')(x)

model = keras.Model(inputs=[v for v in feature_layer_inputs.values()], outputs=baggage_pred)

model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])

model.fit(train_ds)

这篇关于如何使用tensorflow feature_columns作为keras模型的输入的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！