平整层的影响及其正确使用

我正在做简单的 1D 卷积模型,其构建如下

model1= Sequential()
model1.add(Conv1D(60,32, strides=1, activation='relu',padding='causal',input_shape=(64,1)))
model1.add(Conv1D(80,10, strides=1, activation='relu',padding='causal'))
model1.add(Conv1D(100,5, strides=1, activation='relu',padding='causal'))
model1.add(MaxPooling1D(2))
model1.add(Dense(300,activation='relu'))
model1.add(Flatten())
model1.add(Dense(1,activation='relu'))
print(model1.summary())

其模型总结如下

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv1d_1 (Conv1D)            (None, 64, 60)            1980      
_________________________________________________________________
conv1d_2 (Conv1D)            (None, 64, 80)            48080     
_________________________________________________________________
conv1d_3 (Conv1D)            (None, 64, 100)           40100     
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 32, 100)           0         
_________________________________________________________________
dense_1 (Dense)              (None, 32, 300)           30300     
_________________________________________________________________
flatten_1 (Flatten)          (None, 9600)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 9601      
=================================================================
Total params: 130,061
Trainable params: 130,061
Non-trainable params: 0
_________________________________________________________________

如果我在第一个密集层之前移动扁平层,如下所示,我得到以下模型架构。这一模型的模型参数数量似乎比前一个大得多。为什么扁平层的放置有如此大的影响?放置展平层的正确方法是什么。

model1= Sequential()
model1.add(Conv1D(60,32, strides=1, activation='relu',padding='causal',input_shape=(64,1)))
model1.add(Conv1D(80,10, strides=1, activation='relu',padding='causal'))
model1.add(Conv1D(100,5, strides=1, activation='relu',padding='causal'))
model1.add(MaxPooling1D(2))
model1.add(Flatten())
model1.add(Dense(300,activation='relu'))
model1.add(Dense(1,activation='relu'))