ValueError Traceback (most recent call last) <ipython-input-54-536a68c200e5> in <module> 52 return model 53 # lstm network ---> 54 model = create_LSTM_model(X_train,n_steps,n_length, n_features) 55 # summary 56 print(model.summary()) <ipython-input-54-536a68c200e5> in create_LSTM_model(X_train, n_steps, n_length, n_features) 22 X_train = X_train.reshape((X_train.shape[0], n_steps, 1, n_length, n_features)) 23 ---> 24 model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', 25 input_shape=(n_steps, 1, n_length, n_features))) 26 model.add(Flatten()) ~\anaconda3\lib\site-packages\tensorflow\python\trackable\base.py in _method_wrapper(self, *args, **kwargs) 203 self._self_setattr_tracking = False # pylint: disable=protected-access 204 try: --> 205 result = method(self, *args, **kwargs) 206 finally: 207 self._self_setattr_tracking = previous_value # pylint: disable=protected-access ~\anaconda3\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs) 68 # To get the full stack trace, call: 69 # `tf.debugging.disable_traceback_filtering()` ---> 70 raise e.with_traceback(filtered_tb) from None 71 finally: 72 del filtered_tb ~\anaconda3\lib\site-packages\keras\engine\input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name) 233 ndim = shape.rank 234 if ndim != spec.ndim: --> 235 raise ValueError( 236 f'Input {input_index} of layer "{layer_name}" ' 237 "is incompatible with the layer: " ValueError: Input 0 of layer "conv_lstm2d_12" is incompatible with the layer: expected ndim=5, found ndim=3. Full shape received: (None, 10, 5)解决该错误

这个错误提示中指出，输入到 "conv_lstm2d_12" 层的张量的维度不兼容。这个层期望的输入张量维度是 5，而实际输入张量的维度是 3。具体来说，期望的维度是 (batch_size, timesteps, channels, rows, cols)，而实际的维度是 (batch_size, timesteps, features)。

这个问题的原因可能是在创建模型时，没有正确地指定输入张量的形状。你可以尝试将输入张量 X_train 从 shape=(samples, timesteps, features) 转换为 shape=(samples, timesteps, 1, n_length, n_features)，再将其作为模型的输入。代码如下：

def create_LSTM_model(X_train, n_steps, n_length, n_features):
    model = Sequential()
    model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu',
                     input_shape=(n_steps, 1, n_length, n_features)))
    model.add(Flatten())
    model.add(Dense(1))
    model.compile(optimizer='adam', loss='mse')
    # reshape input to be 5D [samples, timesteps, channels, rows, cols]
    X_train = X_train.reshape((X_train.shape[0], n_steps, 1, n_length, n_features))
    return model

# lstm network
model = create_LSTM_model(X_train,n_steps,n_length, n_features)
# summary
print(model.summary())

注意，这里的 1 是指输入张量的 channels 维度。如果你的输入张量本来就有 channels 维度，那么这里的 1 可以改为对应的值。