我在修改一个旧代码，通过向模型中添加注意力层。但我无法弄清楚如何以正确的输入大小堆叠这些层。
实际输入数据将是 (200,189,1)。
//我正在尝试类似这样的代码
def mocap_model(optimizer='SGD'): model = Sequential() model.add(Conv2D(32, 3, strides=(2, 2), padding ='same', input_shape=(200, 189, 1))) model.add(Dropout(0.2)) model.add(Activation('relu')) model.add(Conv2D(64, 3, strides=(2, 2), padding ='same')) model.add(Dropout(0.2)) model.add(Activation('relu')) model.add(Conv2D(64, 3, strides=(2, 2), padding ='same')) model.add(Dropout(0.2)) model.add(Activation('relu')) model.add(Conv2D(128, 3, strides=(2, 2), padding ='same')) model.add(Dropout(0.2)) model.add(Flatten()) return modelcnn = mocap_model() main_input = Input(shape=(200, 189, 1)) rnn = Sequential()rnn = LSTM(256, return_sequences=True, input_shape=(200,189)) model = TimeDistributed(cnn)(main_input) model = rnn(model) att_in=LSTM(256,return_sequences=True,dropout=0.3,recurrent_dropout=0.2)(model)att_out=attention()(att_in)output3=Dense(256,activation='relu',trainable=True)(att_out)output4=Dense(4,activation='softmax',trainable=True)(output3)model=Model(main_input,output4) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])model.summary()但我得到了这个错误：
----> 8 model = TimeDistributed(cnn)(main_input)ValueError: 层 sequential_40 的输入 0 与该层不兼容: 期望的最小维度数为4，但发现的维度数为3。接收到的完整形状: (None, 189, 1)
回答：
输入形状问题。 tf.keras.layers.TimeDistributed 期望输入的批次大小。期望的输入: 形状为 (batch, time, ...) 的输入张量。在 main_input 中添加批次大小
main_input = Input(shape=(10, 200, 189, 1))工作示例代码
import tensorflow as tfcnn = tf.keras.Sequential()cnn.add(tf.keras.layers.Conv2D(64, 1, 1, input_shape=(200, 189, 1)))cnn.add(tf.keras.layers.Flatten())cnn.output_shapemain_input = tf.keras.Input(shape=(10, 200, 189, 1))outputs = tf.keras.layers.TimeDistributed(cnn)(main_input)outputs.shape输出
TensorShape([None, 10, 2419200])