ValueError：logits 和标签必须具有相同的形状 ((None, 10) vs (None, 12))

Question

我正在关注POS Tagger for African Language 的教程，该教程使用基于 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))

似乎与最终密集层（12）中的神经元有关，它不对应 10 个类，但不知道如何修复它。

标题

Answer 1

将nb_labels 中的值更改为 10 并将激活设置为“softmax”。 Sigmoid 适用于二元情况。