其实这个
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，因为它影响从主题名称的解释。
此外，您对文本摘要的意思并不完全清楚。您要将处理过的文本传递到什么类型的模型\层？
顺便说一句，这很重要，因为tf.keras.layers.DenseFeatures 和tf.keras.experimental.SequenceFeatures 被支持用于不同的网络架构和方法。
正如SequenceFeatures layer 的文档所说，SequenceFeatures 层的输出应该被输入序列网络，例如 RNN。
而 DenseFeatures 会产生一个密集张量作为输出，因此适用于其他类型的网络。
当您在代码 sn-p 中执行标记化时，您将在模型中使用 嵌入。
那么你有两个选择：
- 将学习到的嵌入向前传递到密集层。这意味着您不会分析词序。
- 将学习到的嵌入传递到 Convolution、Reccurent、AveragePooling、LSTM 层等也使用词序进行学习
第一个选项需要使用：
-
tf.keras.layers.DenseFeatures 与
-
tf.feature_column.categorical_column_*()之一
- 和
tf.feature_column.embedding_column()
第二个选项需要使用：
-
tf.keras.experimental.SequenceFeatures 与
-
tf.feature_column.sequence_categorical_column_*()之一
- 和
tf.feature_column.embedding_column()
以下是示例。
两个选项的预处理和训练部分是相同的：
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))
这里我们使用第一个选项，当我们不使用词序学习时
# 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.
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
)
当我们关注单词顺序并学习我们的模型时，第二个选项。
# 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.
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 上找到包含此示例的完整 jupiter notebooks：
feature_column.embedding_column() 中的参数维度正是模型学习期间将呈现令牌的空间维度。详细解释见https://www.tensorflow.org/beta/tutorials/text/word_embeddings教程
另请注意，使用feature_column.embedding_column() 是tf.keras.layers.Embedding() 的替代方法。如您所见，feature_column 从预处理管道中进行编码步骤，但您仍应手动进行句子的拆分、填充和转换。