data_filtered = row_sum.reshape((1000, 1)) ValueError: cannot reshape array of size 16 into shape (1000,1)

这个错误意味着你的 `row_sum` 数组大小为 16，而你试图将其重塑为大小为 (1000, 1) 的数组。这是不可能的，因为这意味着你需要填充 1000*1 - 16 = 984 个额外元素。你需要检查一下代码，找出为什么 `row_sum` 的大小不是你预期的大小。可能是你的代码有 bug，或者你的数据并不是你所期望的。你可以使用 `print` 语句或调试器来查看 `row_sum` 数组的大小和内容，以帮助你找出问题所在。

相关问题

def create_LSTM_model(): # instantiate the model model = Sequential() model.add(Input(shape=(X_train.shape[1], X_train.shape[2]))) model.add(Reshape((X_train.shape[1], 1, X_train.shape[2], 1))) # cnn1d Layers model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', padding='same', return_sequences=True)) model.add(Flatten()) model.add(Dropout(0.5)) model.add(RepeatVector(1)) # 添加lstm层 model.add(LSTM(64, activation = 'relu', return_sequences=True)) model.add(Dropout(0.5)) #添加注意力层 model.add(LSTM(64, activation = 'relu', return_sequences=False)) # 添加dropout model.add(Dropout(0.5)) model.add(Dense(128)) # 输出层 model.add(Dense(1, name='Output')) # 编译模型 model.compile(optimizer='adam', loss='mse', metrics=['mae']) return model # lstm network model = create_LSTM_model() # summary print(model.summary())修改该代码，解决ValueError: in user code: File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1284, in train_function * return step_function(self, iterator) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1268, in step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1249, in run_step ** outputs = model.train_step(data) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1050, in train_step y_pred = self(x, training=True) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\layers\reshaping\reshape.py", line 118, in _fix_unknown_dimension raise ValueError(msg) ValueError: Exception encountered when calling layer 'reshape_51' (type Reshape). total size of new array must be unchanged, input_shape = [10, 1, 1, 5], output_shape = [10, 1, 1, 1] Call arguments received by layer 'reshape_51' (type Reshape): • inputs=tf.Tensor(shape=(None, 10, 1, 1, 5), dtype=float32)问题

这个问题出现在Reshape层中，原因是输出形状 [10, 1, 1, 1] 的 Reshape 层与输入形状 [10, 1, 1, 5] 不兼容。解决方法是将 Reshape 层的输出形状改为 [10, 5]，这样就可以保证总大小不变，代码如下：

def create_LSTM_model():
    # instantiate the model
    model = Sequential()
    model.add(Input(shape=(X_train.shape[1], X_train.shape[2])))
    model.add(Reshape((X_train.shape[1]*X_train.shape[2],)))
    
    # 添加lstm层
    model.add(LSTM(64, activation = 'relu', return_sequences=True))
    model.add(Dropout(0.5))
    #添加注意力层
    model.add(LSTM(64, activation = 'relu', return_sequences=False))
    # 添加dropout
    model.add(Dropout(0.5))
    model.add(Dense(128))
    # 输出层
    model.add(Dense(1, name='Output'))
    
    # 编译模型
    model.compile(optimizer='adam', loss='mse', metrics=['mae'])
    return model
    
# lstm network
model = create_LSTM_model()

# summary
print(model.summary())

这样输出形状为 [10, 1, 1, 5] 的输入就可以被正确地处理了。

def create_LSTM_model(X_train,n_steps,n_length, n_features): # instantiate the model model = Sequential() model.add(Input(shape=(X_train.shape[1], X_train.shape[2]))) X_train = X_train.reshape((X_train.shape[0], n_steps, 1, n_length, n_features)) model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', input_shape=(n_steps, 1, n_length, n_features))) model.add(Flatten()) # cnn1d Layers # 添加lstm层 model.add(LSTM(64, activation = 'relu', return_sequences=True)) model.add(Dropout(0.5)) #添加注意力层 model.add(LSTM(64, activation = 'relu', return_sequences=False)) # 添加dropout model.add(Dropout(0.5)) model.add(Dense(128)) # 输出层 model.add(Dense(1, name='Output')) # 编译模型 model.compile(optimizer='adam', loss='mse', metrics=['mae']) return model # lstm network model = create_LSTM_model(X_train,n_steps,n_length, n_features) # summary print(model.summary())修改该代码，解决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)错误

根据错误提示，发现在添加 ConvLSTM2D 层时出现了错误，原因是输入的形状不正确。具体来说，ConvLSTM2D 层要求输入的形状为 (batch_size, time_steps, channels, rows, cols)，而输入 X_train 经过 reshape 后的形状为 (batch_size, time_steps, rows, cols, channels)。

解决方法是在 ConvLSTM2D 层之前，添加一层 Reshape 层，将 X_train 的形状调整为 (batch_size, time_steps, 1, rows, cols, channels)。具体修改代码如下：

def create_LSTM_model(X_train,n_steps,n_length, n_features):
    # instantiate the model
    model = Sequential()
    model.add(Input(shape=(X_train.shape[1], X_train.shape[2])))
    model.add(Reshape((n_steps, 1, n_length, n_features)))
    model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', input_shape=(n_steps, 1, n_length, n_features)))
    model.add(Flatten())
    # cnn1d Layers
    # 添加lstm层
    model.add(LSTM(64, activation='relu', return_sequences=True))
    model.add(Dropout(0.5))
    #添加注意力层
    model.add(LSTM(64, activation='relu', return_sequences=False))
    # 添加dropout
    model.add(Dropout(0.5))
    model.add(Dense(128))
    # 输出层
    model.add(Dense(1, name='Output'))
    # 编译模型
    model.compile(optimizer='adam', loss='mse', metrics=['mae'])
    return model

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

# summary
print(model.summary())