Python ValueError:登录和标签必须具有相同的形状（（无，10）与（无，12））

我将遵循的教程是，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)

我得到一个错误：

 File "testing.py", line 64, in <module> callbacks=[early_stopping])
...
ValueError: logits and labels must have the same shape ((None, 10) vs (None, 12))

回调中的第64行文件“testing.py=[early\u stopping]） ... ValueError:登录和标签必须具有相同的形状（（无，10）与（无，12）） 这似乎与最后一个致密层（12）中的神经元有关，这些神经元不对应于10类，但不知道如何修复

---

标题将

nb_标签中的值更改为10，并将激活设置为“softmax”。Sigmoid用于二进制情况。
nb_标签的值为10，max_len的值为15（调试时）。这不是我的代码，我正试图复制所提供教程中的代码，因为我是深度学习的初学者。我告诉您的更改是否有助于您运行代码？然后将y_测试更改为每个长度为10，长度必须匹配。y_train和y_测试中的元素长度为12，但不知道如何使它们具有长度10，由于它是自动创建的：X_train，X_test，Y_train，Y_test=train_test_split（X，Y，test_size=.2，shuffle=True，random_state=999）数据不能更改，但可以更改代码。将nb_标签更改为12。