我正在遵循非洲语言POS Tagger的教程，它使用基于LSTM的分类器。运行代码时：
import pandas as pd
import numpy as np
from keras.preprocessing import sequence
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense, LSTM, SimpleRNN, Flatten
from keras.layers.embeddings import Embedding
from tensorflow.keras.callbacks import EarlyStopping
#from keras.callbacks import EarlyStopping
# load English-Yemba dictionary as CSV file
df = pd.read_csv('dict_en_yb.csv')
# display few words pairs
df.sample(frac=.1).head(15)
nb_labels = len(df.word_type.unique())
nb_words = df.shape[0]
# Create letter to token dictionary
chars = sorted(list(set(' '.join(df.yb))))
letter2idx = dict((c, i+1) for i, c in enumerate(chars))
# Create token to letter dictionary
idx2letter = dict((i, c) for c, i in letter2idx.items())
vocabulary_size = len(letter2idx)+1
print("Vocabulary size: ", vocabulary_size)
X = []
Y = []
max_len = 0
for i, r in df.iterrows():
word_vector = [letter2idx[c] for c in set(r.yb)]
label = r.word_type
if len(word_vector)>max_len:
max_len = len(word_vector)
X_max = word_vector
Y_max = label
X.append(word_vector)
Y.append(label)
X = sequence.pad_sequences(X, maxlen=max_len)
Y = to_categorical(Y)
X.shape, Y.shape
X_train, X_test, Y_train, Y_test = train_test_split(X, Y,
test_size=.2,
shuffle=True, random_state=999)
print("Training with {} words, validation with {} words".format(X_train.shape[0],
X_test.shape[0]))
# Building a 1-layer LSTM with 100 cells with embeddings
embedding_dim = 8
model_lstm = Sequential()
model_lstm.add(Embedding(vocabulary_size, embedding_dim, input_length=max_len))
model_lstm.add(LSTM(100))
model_lstm.add(Dense(nb_labels, activation='sigmoid'))
model_lstm.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model_lstm.summary()
# Training the network
early_stopping = EarlyStopping(monitor='val_acc',
min_delta=0,patience=5,verbose=1, mode='auto')
history_lstm = model_lstm.fit(X_train, Y_train, epochs=50, batch_size=32,
validation_data=(X_test, Y_test),
callbacks=[early_stopping])
scores_lstm = model_lstm.evaluate(X_test, Y_test, verbose=0)
我收到错误：
File "testing.py", line 64, in <module> callbacks=[early_stopping])
...
ValueError: logits and labels must have the same shape ((None, 10) vs (None, 12))
这似乎与最终密集层(12(中的神经元有关，它们不对应于10类，但不知道如何修复它。
标题
将nb_labels中的值更改为 10，并将激活设置为"softmax"。Sigmoid 适用于二进制情况。