model = tf.keras.Sequential() model.add(tf.keras.layers.LSTM(80, activation='relu', return_sequences=True, input_shape=(10, 14))) model.add(tf.keras.layers.Dropout(0.2)) model.add(tf.keras.layers.LSTM(80, activation='relu')) model.add(tf.keras.layers.Dense(80)) model.add(tf.keras.layers.Dense(28)) model.compile(metrics=['accuracy'], loss='mean_squared_error', optimizer='adam') model.summary()

该模型是一个序列模型，使用了两个LSTM层。第一个LSTM层有80个单元，激活函数为ReLU，返回序列。输入形状为(10, 14)的张量。第二个LSTM层也有80个单元，激活函数为ReLU。之后添加了一个有80个单元的全连接层和一个有28个单元的输出层。模型使用均方误差作为损失函数，优化器为Adam，并计算准确率作为额外的指标。

以下是模型的摘要信息：

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm (LSTM)                  (None, 10, 80)            34400     
_________________________________________________________________
dropout (Dropout)            (None, 10, 80)            0         
_________________________________________________________________
lstm_1 (LSTM)                (None, 80)                51520     
_________________________________________________________________
dense (Dense)                (None, 80)                6480      
_________________________________________________________________
dense_1 (Dense)              (None, 28)                2268      
=================================================================
Total params: 94,668
Trainable params: 94,668
Non-trainable params: 0
_________________________________________________________________