Python UserWarning:`Sequential.model`已弃用

日志：

from keras.models import Sequential
from keras.layers import Dense, Activation, Embedding, Flatten, LSTM, Dropout, Conv1D, SpatialDropout1D
from keras.optimizers import Adam

model = Sequential()
model.add(Embedding(max_fatures, embed_dim,input_length = x.shape[1]))
model.add(SpatialDropout1D(0.5))
model.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = ['acc'])
model.summary()

model.model.save('my_model.h5')
with open('tokenizer.pickle', 'wb') as handle:
    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)

C:\Users\AppData\Local\Continuum\anaconda3\lib\site packages\keras\engine\sequential.py:110：

UserWarning：

Sequential.model

已弃用

Sequential

是

Model

的一个子类，您只需使用

Sequential

实例即可 直接的。警告。警告（“

Sequential.model

已弃用。”

在jupyter笔记本电脑中执行模型时，它将以

.ipynb

文件格式工作，但在VS代码中以

.py

文件格式执行时停止工作

代码：

from keras.models import Sequential
from keras.layers import Dense, Activation, Embedding, Flatten, LSTM, Dropout, Conv1D, SpatialDropout1D
from keras.optimizers import Adam

model = Sequential()
model.add(Embedding(max_fatures, embed_dim,input_length = x.shape[1]))
model.add(SpatialDropout1D(0.5))
model.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = ['acc'])
model.summary()

model.model.save('my_model.h5')
with open('tokenizer.pickle', 'wb') as handle:
    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)

保存模型：

from keras.models import Sequential
from keras.layers import Dense, Activation, Embedding, Flatten, LSTM, Dropout, Conv1D, SpatialDropout1D
from keras.optimizers import Adam

model = Sequential()
model.add(Embedding(max_fatures, embed_dim,input_length = x.shape[1]))
model.add(SpatialDropout1D(0.5))
model.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = ['acc'])
model.summary()

model.model.save('my_model.h5')
with open('tokenizer.pickle', 'wb') as handle:
    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)

---

UserWarning提到，您必须使用

#保存模型
model.save（'my_model.h5'））

---

要保存顺序模型而不是

model.model.save（'my_model.h5'）

。由于

Sequential（）.model.save（）

已被弃用。

尝试过同样的操作，文件正在执行，但在目录中看不到保存的“my_model.h5”文件。请在调用

model.fit（）后尝试保存模型

方法。也可以使用

tensorflow.keras

而不是

keras

。不需要model.model，只需执行model.save即可