Tensorflow 如何在lstm输出的每个时间步应用平均池？

我试图在lstm输出的每个时间步应用平均池，请查找我的架构，如下所示

X_input = tf.keras.layers.Input(shape=(64,35))
X= tf.keras.layers.LSTM(512,activation="tanh",return_sequences=True,kernel_initializer=tf.keras.initializers.he_uniform(seed=45),kernel_regularizer=tf.keras.regularizers.l2(0.1))(X_input)
X= tf.keras.layers.LSTM(256,activation="tanh",return_sequences=True,kernel_initializer=tf.keras.initializers.he_uniform(seed=45),kernel_regularizer=tf.keras.regularizers.l2(0.1))(X)
X = tf.keras.layers.GlobalAvgPool1D()(X)
X = tf.keras.layers.Dense(128,activation="relu",kernel_initializer=tf.keras.initializers.he_uniform(seed=45),kernel_regularizer=tf.keras.regularizers.l2(0.1))(X)
X = tf.keras.layers.Dense(64,activation="relu",kernel_initializer=tf.keras.initializers.he_uniform(seed=45),kernel_regularizer=tf.keras.regularizers.l2(0.1))(X)
X = tf.keras.layers.Dense(32,activation="relu",kernel_initializer=tf.keras.initializers.he_uniform(seed=45),kernel_regularizer=tf.keras.regularizers.l2(0.1))(X)
# X = tf.keras.layers.Dense(16,activation="relu",kernel_initializer=tf.keras.initializers.he_uniform(seed=45),kernel_regularizer=tf.keras.regularizers.l2(0.1))(X)
output_layer = tf.keras.layers.Dense(10,activation='softmax', kernel_initializer=tf.keras.initializers.he_uniform(seed=45))(X)
model2 = tf.keras.Model(inputs = X_input,outputs = output_layer)

我想在每个时间步上取平均值，而不是在每个单元上

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例如，现在我正在获取形状（无，256），但我想从全局平均池层获取形状（无，64），我需要做什么。

我不确定这是最有效的方法，但您可以尝试以下方法：

X = tf.keras.layers.Reshape(target_shape=(64,256,1))(X)
X = tf.keras.layers.TimeDistributed(tf.keras.layers.GlobalAveragePooling1D())(X)
X = tf.keras.layers.Reshape(target_shape=(64,))(X)

而不是：

X = tf.keras.layers.GlobalAvgPool1D()(X)

总结如下：

Model: "functional_13"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_14 (InputLayer)        [(None, 64, 35)]          0         
_________________________________________________________________
lstm_26 (LSTM)               (None, 64, 512)           1122304   
_________________________________________________________________
lstm_27 (LSTM)               (None, 64, 256)           787456    
_________________________________________________________________
reshape_2 (Reshape)          (None, 64, 256, 1)        0         
_________________________________________________________________
time_distributed_8 (TimeDist (None, 64, 1)             0         
_________________________________________________________________
reshape_3 (Reshape)          (None, 64)                0         
_________________________________________________________________
dense_61 (Dense)             (None, 128)               8320      
_________________________________________________________________
dense_62 (Dense)             (None, 64)                8256      
_________________________________________________________________
dense_63 (Dense)             (None, 32)                2080      
_________________________________________________________________
dense_64 (Dense)             (None, 10)                330       
=================================================================
Total params: 1,928,746
Trainable params: 1,928,746
Non-trainable params: 0

X=tf.reduce_mean（X，axis=-1），而不是tf.keras.layers.GlobalAvgPool1D（）（X）