我试图让LSTM模型继续上次运行中断的地方。所有代码编译正常，直到我尝试拟合网络时，它给出了一个错误：

ValueError: 检查目标时出错：期望dense_29有3个维度，但得到的数组形状为(672, 1)

我查看了多篇文章，如这篇和这篇，但我没能找出我的代码中有什么问题。

from keras import Sequentialfrom keras.preprocessing.sequence import pad_sequencesfrom sklearn.model_selection import train_test_splitfrom keras.models import Sequential,Modelfrom keras.layers import LSTM, Dense, Bidirectional, Input,Dropout,BatchNormalizationfrom keras import backend as Kfrom keras.engine.topology import Layerfrom keras import initializers, regularizers, constraintsfrom keras.callbacks import ModelCheckpointfrom keras.models import load_modelimport os.pathimport osfilepath="Train-weights.best.hdf5"act = 'relu'model = Sequential()model.add(BatchNormalization(input_shape=(10, 128)))model.add(Bidirectional(LSTM(128, dropout=0.5, activation=act, return_sequences=True)))model.add(Dense(1,activation='sigmoid'))if (os.path.exists(filepath)):   print("继续之前的训练")   model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])   with open('model_architecture.json', 'r') as f:      model = model_from_json(f.read())   model.load_weights(filepath)else:   print("第一次运行")         model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])   model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=100, batch_size=32, callbacks=callbacks_list, verbose=2)   model.save_weights(filepath)   with open('model_architecture.json', 'w') as f:       f.write(model.to_json()) checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max') callbacks_list = [checkpoint] model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=100, batch_size=32, callbacks=callbacks_list, verbose=0)

回答：

尝试使用model.summary()，你会发现网络中最后一层的输出形状（即Dense层）是(None, 10, 1)。因此，你提供给模型的标签（即y_train）也必须具有(num_samples, 10, 1)的形状。

如果输出形状(None, 10, 1)不是你想要的（例如，你希望模型的输出形状为(None, 1)），那么你需要修改模型定义。一个简单的修改方法是从LSTM层中移除return_sequences=True参数。