def kerasModel(inp_shape, activation, n): lstm_input = keras.layers.Input(shape=inp_shape, name='lstm_input') x = keras.layers.LSTM(50, name='lstm_0')(lstm_input) x = keras.layers.Dropout(0.2, name='lstm_dropout_0')(x) x = keras.layers.Dense(64, name='dense_0')(x) x = keras.layers.Activation('sigmoid', name='sigmoid_0')(x) x = keras.layers.Dense(n, name='dense_1')(x) output = keras.layers.Activation(activation, name='linear_output')(x) model = keras.Model(inputs=lstm_input, outputs=output) adam = keras.optimizers.Adam(lr=0.0005) model.compile(optimizer=adam, loss='mse') return modelmodelGeneral = kerasModel((4, 1), 'linear', 1)modelGeneral.fit(np.reshape(X_aux['X_i'], (1, 4, 1)), np.reshape(X_aux['X_i1'], (1, 4, 1)), verbose=False)返回以下错误：
>>> modelGeneral.fit(np.reshape(X_aux['X_i'], (1, 4, 1)), np.reshape(X_aux['X_i1'], (1, 1, 4)), verbose=False)ValueError: Error when checking target: expected linear_output to have 2 dimensions, but got array with shape (1, 1, 4)>>> modelGeneral.summary()_________________________________________________________________Layer (type) Output Shape Param # =================================================================lstm_input (InputLayer) (None, 4, 1) 0 _________________________________________________________________lstm_0 (LSTM) (None, 50) 10400 _________________________________________________________________lstm_dropout_0 (Dropout) (None, 50) 0 _________________________________________________________________dense_0 (Dense) (None, 64) 3264 _________________________________________________________________sigmoid_0 (Activation) (None, 64) 0 _________________________________________________________________dense_1 (Dense) (None, 1) 65 _________________________________________________________________linear_output (Activation) (None, 1) 0 =================================================================Total params: 13,729Trainable params: 13,729Non-trainable params: 0_________________________________________________________________我在linear_output之前尝试重塑数据，但返回了另一个错误：
>>> x = keras.layers.Reshape(inp_shape)(x)ValueError: total size of new array must be unchanged我认为问题可能出在np.reshape(X_aux['X_i1'], (1, 1, 4))在Y->fit()中，但说实话我已经迷失了方向，所以我非常需要一些帮助！！
np.reshape(X_aux['X_i1'], (1, 1, 4))的一个示例：
array([[[ 1.5357086 , 3.84368446, 3.84368446, 232. ]]])
回答：
LSTM层应返回序列：
x = keras.layers.LSTM(50, return_sequences=True, name='lstm_0')(lstm_input)