如何在特征列中填充序列，以及在dimension中如何填充feature_column。
我正在使用Tensorflow 2.0并实现一个文本摘要示例。机器学习，深度学习和TensorFlow尚不成熟。
我遇到了feature_column，发现它们很有用，因为我认为它们可以嵌入模型的处理管道中。
在不使用feature_column的经典场景中，我可以预处理文本，将其标记化，将其转换为数字序列，然后将其填充到一个{100}的maxlen中。使用feature_column时无法完成此操作。
下面是我写的沙发。
train_dataset = tf.data.experimental.make_csv_dataset(
'assets/train_dataset.csv', label_name=LABEL, num_epochs=1, shuffle=True, shuffle_buffer_size=10000, batch_size=1, ignore_errors=True)
vocabulary = ds.get_vocabulary()
def text_demo(feature_column):
feature_layer = tf.keras.experimental.SequenceFeatures(feature_column)
article, _ = next(iter(train_dataset.take(1)))
tokenizer = tf_text.WhitespaceTokenizer()
tokenized = tokenizer.tokenize(article['Text'])
sequence_input, sequence_length = feature_layer({'Text':tokenized.to_tensor()})
print(sequence_input)
def categorical_column(feature_column):
dense_column = tf.keras.layers.DenseFeatures(feature_column)
article, _ = next(iter(train_dataset.take(1)))
lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(
filters='')
lang_tokenizer.fit_on_texts(article)
tensor = lang_tokenizer.texts_to_sequences(article)
tensor = tf.keras.preprocessing.sequence.pad_sequences(tensor,
padding='post', maxlen=50)
print(dense_column(tensor).numpy())
text_seq_vocab_list = tf.feature_column.sequence_categorical_column_with_vocabulary_list(key='Text', vocabulary_list=list(vocabulary))
text_embedding = tf.feature_column.embedding_column(text_seq_vocab_list, dimension=8)
text_demo(text_embedding)
numerical_voacb_list = tf.feature_column.categorical_column_with_vocabulary_list(key='Text', vocabulary_list=list(vocabulary))
embedding = tf.feature_column.embedding_column(numerical_voacb_list, dimension=8)
categorical_column(embedding)
对于在这里使用什么sequence_categorical_column_with_vocabulary_list或categorical_column_with_vocabulary_list，我也感到困惑。在文档中，SequenceFeatures也没有说明，尽管我知道它是一个实验性功能。
我也无法理解dimension参数的作用？
答案 0 :(得分：1)
实际上，这个
我也很困惑在这里使用什么， sequence_categorical_column_with_vocabulary_list或 categorical_column_with_vocabulary_list。
应该是第一个问题，因为它会影响对主题名称的解释。
还不清楚您对文本摘要的含义。您要将处理过的文本传递到哪种类型的模型\图层？
顺便说一句，这很重要，因为tf.keras.layers.DenseFeatures和tf.keras.experimental.SequenceFeatures支持不同的网络体系结构和方法。
SequenceFeatures layer的文档说SequenceFeatures层的输出应该馈入序列网络，例如RNN。
DenseFeatures产生密集的Tensor作为输出，因此适用于其他类型的网络。
在代码段中执行标记化时，将在模型中使用嵌入。 然后，您有两个选择：
第一个选项需要使用：
tf.keras.layers.DenseFeatures
tf.feature_column.categorical_column_*()之一tf.feature_column.embedding_column() 第二个选项需要使用：
tf.keras.experimental.SequenceFeatures
tf.feature_column.sequence_categorical_column_*()之一tf.feature_column.embedding_column() 以下是示例。 这两个选项的预处理和培训部分相同：
import tensorflow as tf
print(tf.__version__)
from tensorflow import feature_column
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import text_to_word_sequence
import tensorflow.keras.utils as ku
from tensorflow.keras.utils import plot_model
import pandas as pd
from sklearn.model_selection import train_test_split
DATA_PATH = 'C:\SoloLearnMachineLearning\Stackoverflow\TextDataset.csv'
#it is just two column csv, like:
# text;label
# A wiki is run using wiki software;0
# otherwise known as a wiki engine.;1
dataframe = pd.read_csv(DATA_PATH, delimiter = ';')
dataframe.head()
# Preprocessing before feature_clolumn includes
# - getting the vocabulary
# - tokenization, which means only splitting on tokens.
# Encoding sentences with vocablary will be done by feature_column!
# - padding
# - truncating
# Build vacabulary
vocab_size = 100
oov_tok = '<OOV>'
sentences = dataframe['text'].to_list()
tokenizer = Tokenizer(num_words = vocab_size, oov_token="<OOV>")
tokenizer.fit_on_texts(sentences)
word_index = tokenizer.word_index
# if word_index shorter then default value of vocab_size we'll save actual size
vocab_size=len(word_index)
print("vocab_size = word_index = ",len(word_index))
# Split sentensec on tokens. here token = word
# text_to_word_sequence() has good default filter for
# charachters include basic punctuation, tabs, and newlines
dataframe['text'] = dataframe['text'].apply(text_to_word_sequence)
dataframe.head()
max_length = 6
# paddind and trancating setnences
# do that directly with strings without using tokenizer.texts_to_sequences()
# the feature_colunm will convert strings into numbers
dataframe['text']=dataframe['text'].apply(lambda x, N=max_length: (x + N * [''])[:N])
dataframe['text']=dataframe['text'].apply(lambda x, N=max_length: x[:N])
dataframe.head()
# Define method to create tf.data dataset from Pandas Dataframe
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((dict(dataframe), labels))
if shuffle:
ds = ds.shuffle(buffer_size=len(dataframe))
ds = ds.batch(batch_size)
return ds
# Split dataframe into train and validation sets
train_df, val_df = train_test_split(dataframe, test_size=0.2)
print(len(train_df), 'train examples')
print(len(val_df), 'validation examples')
batch_size = 32
ds = df_to_dataset(dataframe, 'label',shuffle=False,batch_size=batch_size)
train_ds = df_to_dataset(train_df, 'label', shuffle=False, batch_size=batch_size)
val_ds = df_to_dataset(val_df, 'label', shuffle=False, batch_size=batch_size)
# and small batch for demo
example_batch = next(iter(ds))[0]
example_batch
# Helper methods to print exxample outputs of for defined feature_column
def demo(feature_column):
feature_layer = tf.keras.layers.DenseFeatures(feature_column)
print(feature_layer(example_batch).numpy())
def seqdemo(feature_column):
sequence_feature_layer = tf.keras.experimental.SequenceFeatures(feature_column)
print(sequence_feature_layer(example_batch))
当我们不使用单词顺序学习时，这里是第一个选项
# Define categorical colunm for our text feature,
# which is preprocessed into lists of tokens
# Note that key name should be the same as original column name in dataframe
text_column = feature_column.
categorical_column_with_vocabulary_list(key='text',
vocabulary_list=list(word_index))
#indicator_column produce one-hot-encoding. These lines just to compare with embedding
#print(demo(feature_column.indicator_column(payment_description_3)))
#print(payment_description_2,'\n')
# argument dimention here is exactly the dimension of the space in which tokens
# will be presented during model's learning
# see the tutorial at https://www.tensorflow.org/beta/tutorials/text/word_embeddings
text_embedding = feature_column.embedding_column(text_column, dimension=8)
print(demo(text_embedding))
# The define the layers and model it self
# This example uses Keras Functional API instead of Sequential just for more generallity
# Define DenseFeatures layer to pass feature_columns into Keras model
feature_layer = tf.keras.layers.DenseFeatures(text_embedding)
# Define inputs for each feature column.
# See https://github.com/tensorflow/tensorflow/issues/27416#issuecomment-502218673
feature_layer_inputs = {}
# Here we have just one column
# Important to define tf.keras.Input with shape
# corresponding to lentgh of our sequence of words
feature_layer_inputs['text'] = tf.keras.Input(shape=(max_length,),
name='text',
dtype=tf.string)
print(feature_layer_inputs)
# Define outputs of DenseFeatures layer
# And accually use them as first layer of the model
feature_layer_outputs = feature_layer(feature_layer_inputs)
print(feature_layer_outputs)
# Add consequences layers.
# See https://keras.io/getting-started/functional-api-guide/
x = tf.keras.layers.Dense(256, activation='relu')(feature_layer_outputs)
x = tf.keras.layers.Dropout(0.2)(x)
# This example supposes binary classification, as labels are 0 or 1
x = tf.keras.layers.Dense(1, activation='sigmoid')(x)
model = tf.keras.models.Model(inputs=[v for v in feature_layer_inputs.values()],
outputs=x)
model.summary()
# This example supposes binary classification, as labels are 0 or 1
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy']
#run_eagerly=True
)
# Note that fit() method looking up features in train_ds and valdation_ds by name in
# tf.keras.Input(shape=(max_length,), name='text'
# This model of cause will learn nothing because of fake data.
num_epochs = 5
history = model.fit(train_ds,
validation_data=val_ds,
epochs=num_epochs,
verbose=1
)
还有第二种选择，当我们注意单词顺序并学习它的模型时。
# Define categorical colunm for our text feature,
# which is preprocessed into lists of tokens
# Note that key name should be the same as original column name in dataframe
text_column = feature_column.
sequence_categorical_column_with_vocabulary_list(key='text',
vocabulary_list=list(word_index))
# arguemnt dimention here is exactly the dimension of the space in
# which tokens will be presented during model's learning
# see the tutorial at https://www.tensorflow.org/beta/tutorials/text/word_embeddings
text_embedding = feature_column.embedding_column(text_column, dimension=8)
print(seqdemo(text_embedding))
# The define the layers and model it self
# This example uses Keras Functional API instead of Sequential
# just for more generallity
# Define SequenceFeatures layer to pass feature_columns into Keras model
sequence_feature_layer = tf.keras.experimental.SequenceFeatures(text_embedding)
# Define inputs for each feature column. See
# см. https://github.com/tensorflow/tensorflow/issues/27416#issuecomment-502218673
feature_layer_inputs = {}
sequence_feature_layer_inputs = {}
# Here we have just one column
sequence_feature_layer_inputs['text'] = tf.keras.Input(shape=(max_length,),
name='text',
dtype=tf.string)
print(sequence_feature_layer_inputs)
# Define outputs of SequenceFeatures layer
# And accually use them as first layer of the model
# Note here that SequenceFeatures layer produce tuple of two tensors as output.
# We need just first to pass next.
sequence_feature_layer_outputs, _ = sequence_feature_layer(sequence_feature_layer_inputs)
print(sequence_feature_layer_outputs)
# Add consequences layers. See https://keras.io/getting-started/functional-api-guide/
# Conv1D and MaxPooling1D will learn features from words order
x = tf.keras.layers.Conv1D(8,4)(sequence_feature_layer_outputs)
x = tf.keras.layers.MaxPooling1D(2)(x)
# Add consequences layers. See https://keras.io/getting-started/functional-api-guide/
x = tf.keras.layers.Dense(256, activation='relu')(x)
x = tf.keras.layers.Dropout(0.2)(x)
# This example supposes binary classification, as labels are 0 or 1
x = tf.keras.layers.Dense(1, activation='sigmoid')(x)
model = tf.keras.models.Model(inputs=[v for v in sequence_feature_layer_inputs.values()],
outputs=x)
model.summary()
# This example supposes binary classification, as labels are 0 or 1
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy']
#run_eagerly=True
)
# Note that fit() method looking up features in train_ds and valdation_ds by name in
# tf.keras.Input(shape=(max_length,), name='text'
# This model of cause will learn nothing because of fake data.
num_epochs = 5
history = model.fit(train_ds,
validation_data=val_ds,
epochs=num_epochs,
verbose=1
)
请在我的github上找到完整的木星笔记本，例如：
feature_column.embedding_column()中的参数维数正是模型学习期间将在其中显示标记的空间的维度。有关详细说明，请参见https://www.tensorflow.org/beta/tutorials/text/word_embeddings上的教程
还请注意，使用feature_column.embedding_column()可以替代tf.keras.layers.Embedding()。如您所见feature_column从预处理管道进行编码，但是您还是应该手动对句子进行拆分，填充和翻译。