保存并继续培训LSTM网络

Question

我试图让LSTM模型在上次运行结束后继续运行。所有编译都很好，直到我尝试适应网络。然后它给出一个错误：

ValueError：检查目标时出错：期望dense_29具有3个维度，但得到形状为(672，1(的数组

我检查了各种文章，比如this和this，但我没有发现我的代码中有什么问题。

from keras import Sequential
from keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
from keras.models import Sequential,Model
from keras.layers import LSTM, Dense, Bidirectional, Input,Dropout,BatchNormalization
from keras import backend as K
from keras.engine.topology import Layer
from keras import initializers, regularizers, constraints
from keras.callbacks import ModelCheckpoint
from keras.models import load_model
import os.path
import os
filepath="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("extending training of previous run")
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("First run")      
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)

Answer 1

尝试model.summary()，您会看到网络中最后一层(即密集层(的输出形状是(None, 10, 1)。因此，您提供给模型的标签(即y_train(也必须具有(num_samples, 10, 1)的形状。

如果输出形状(None, 10, 1)不是您想要的(例如，您想要(None, 1)作为模型的输出形状(，则需要修改模型定义。实现这一点的一个简单修改是从LSTM层中删除return_sequences=True参数。