我正在学习preprocessing layers的官方Tensorflow教程，我不知道为什么在分类编码后会得到这些额外的列。
以下是一个精简的最小可重复示例（包括数据）：

import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
import pathlib

dataset_url = 'http://storage.googleapis.com/download.tensorflow.org/data/petfinder-mini.zip'
csv_file = 'datasets/petfinder-mini/petfinder-mini.csv'
tf.keras.utils.get_file('petfinder_mini.zip', dataset_url, extract=True, cache_dir='.')
df = pd.read_csv(csv_file)

# In the original dataset "4" indicates the pet was not adopted.
df['target'] = np.where(df['AdoptionSpeed']==4, 0, 1)
# Drop un-used columns.
df = df.drop(columns=['AdoptionSpeed', 'Description'])

# A utility method to create a tf.data dataset from a Pandas Dataframe
def df_to_dataset(dataframe, shuffle=True, batch_size=32):
  dataframe = dataframe.copy()
  labels = dataframe.pop('target')
  ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
  if shuffle:
    ds = ds.shuffle(buffer_size=len(dataframe))
  ds = ds.batch(batch_size)
  ds = ds.prefetch(batch_size)
  return ds

batch_size = 5
ds = df_to_dataset(df, batch_size=batch_size)
[(train_features, label_batch)] = ds.take(1)

def get_category_encoding_layer(name, dataset, dtype, max_tokens=None):
  # Create a StringLookup layer which will turn strings into integer indices
  if dtype == 'string':
    index = preprocessing.StringLookup(max_tokens=max_tokens)
  else:
    index = preprocessing.IntegerLookup(max_values=max_tokens)

  # Prepare a Dataset that only yields our feature
  feature_ds = dataset.map(lambda x, y: x[name])

  # Learn the set of possible values and assign them a fixed integer index.
  index.adapt(feature_ds)

  # Create a Discretization for our integer indices.
  encoder = preprocessing.CategoryEncoding(max_tokens=index.vocab_size())
  #encoder = preprocessing.CategoryEncoding(max_tokens=2)

  # Prepare a Dataset that only yields our feature.
  feature_ds = feature_ds.map(index)

  # Learn the space of possible indices.
  encoder.adapt(feature_ds)

  # Apply one-hot encoding to our indices. The lambda function captures the
  # layer so we can use them, or include them in the functional model later.
  return lambda feature: encoder(index(feature))

所以，跑完之后

type_col = train_features['Type']
layer = get_category_encoding_layer('Type', ds, 'string')
layer(type_col)

我得到一个结果：

<tf.Tensor: shape=(5, 4), dtype=float32, numpy=
array([[0., 0., 1., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.],
       [0., 0., 0., 1.],
       [0., 0., 0., 1.]], dtype=float32)>

类似于教程中所示的。
请注意，这是一个二元分类问题（猫/狗）：

np.unique(type_col)
# array([b'Cat', b'Dog'], dtype=object)

那么，在上面的结果中显示的分类编码之后的2个额外列的逻辑是什么？它们代表什么，为什么是2（而不是1、3或更多）？
（我非常清楚，如果我想要一个简单的one-hot编码，我可以简单地使用to_categorical()，但这不是这里的问题）