我在生成1D卷积时遇到了数据输入形状的问题。我查看了几篇帖子，似乎错误在于数据必须是3D的，但我的数据已经是3D的了。
# shape# x_train shape: (1228, 1452, 20)# y_train shape: (1228, 1452, 8)# x_val shape: (223, 680, 20)# x_val shape: (223, 680, 8)###n_outputs = 8n_timesteps = 1452n_features = 20model = Sequential()model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(x_train.shape[1:]))) # ie 1452, 20model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))model.add(Dropout(0.5))model.add(MaxPooling1D(pool_size=2))model.add(Flatten())model.add(Dense(100, activation='relu'))model.add(Dense(n_outputs, activation='softmax'))model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])model.fit(x_train, y_train, epochs=9, batch_size=64, shuffle=True)但我一直收到这个错误消息：
ValueError: A target array with shape (1228, 1452, 8) was passed for an output of shape (None, 8) while using as loss `categorical_crossentropy`. This loss expects targets to have the same shape as the output.从中我了解到，目标形状是3维的，而输出是2维的，因此无法计算损失，但我只需要找到一种方法来重新调整形状，使它们相等。
编辑model.summary()如下所示
_________________________________________________________________Layer (type) Output Shape Param # =================================================================conv1d (Conv1D) (None, 1450, 64) 3904 _________________________________________________________________conv1d_1 (Conv1D) (None, 1448, 64) 12352 _________________________________________________________________dropout (Dropout) (None, 1448, 64) 0 _________________________________________________________________max_pooling1d (MaxPooling1D) (None, 724, 64) 0 _________________________________________________________________flatten (Flatten) (None, 46336) 0 _________________________________________________________________dense (Dense) (None, 100) 4633700 _________________________________________________________________dense_1 (Dense) (None, 8) 808 =================================================================Total params: 4,650,764Traceback (most recent call last):Trainable params: 4,650,764Non-trainable params: 0回答：
在我的案例中，问题在于目标向量是3D的，而输出向量是2D的，因此存在明显的不匹配。要解决这个问题，可以将y_train的形状更改为(batch, 8)，或者使用return_sequences=True从前一层LSTM层返回相同形状的数据。
