张量流中的不同配置

如何解决张量流中的不同配置

嗨，我有以下LSTM模型

model = Sequential()
model.add(LSTM(64,return_sequences=True,recurrent_regularizer=l2(0.0015),input_shape=(timesteps,input_dim)))
model.add(Dropout(0.5))
model.add(LSTM(64,input_dim)))

model.add(Dense(64,activation='relu'))
model.add(Dense(64,activation='relu'))

model.add(Dense(n_classes,activation='softmax'))
model.summary()

model.compile(optimizer=Adam(learning_rate = 0.0025),loss = 'sparse_categorical_crossentropy',metrics = ['accuracy'])

model.fit(X_train,y_train,batch_size=32,epochs=100)

现在我看到了另一种配置

model = tf.keras.models.Sequential([
tf.keras.layers.LSTM(64,input_shape= 
(timesteps,input_dim))
#tf.keras.layers.Dropout(0.5)
tf.keras.layers.LSTM(64,input_dim))

tf.keras.layers.Dense(64,activation='relu'),tf.keras.layers.Dense(64,tf.keras.layers.Dense(n_classes,activation='softmax)
])

model.summary()

model.compile(optimizer=Adam(learning_rate = 0.0025),epochs=1)

model = Sequential()和model = tf.keras.models.Sequential有什么区别？

TensorFlow的版本是否不同？我也知道我的lstm模型是有状态还是无状态

对于第一个配置，我也导入了以下库，但没有看到消息

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential,load_model
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers  import SimpleRNN
from tensorflow.keras.layers import LSTM
from tensorflow.keras.regularizers import l2
from tensorflow.keras.optimizers import Adam