我对机器学习和Keras非常新手，卡在了输入数据的步骤。我的数据看起来像这样：

[[[0.01363717 0.        ]  [0.01577874 0.        ]  [0.01463021 0.        ]] [[0.01577874 0.        ]  [0.01463021 0.        ]  [0.01006721 0.        ]] [[0.01463021 0.        ]  [0.01006721 0.        ]  [0.00762504 0.        ]]...]

数据的形状是：(1607, 3, 2)。如何将：

 [[0.01363717 0.        ]  [0.01577874 0.        ]  [0.01463021 0.        ]]

作为输入传递给一个包含512个CuDNNLSTM单元的层？

这是我的整个网络：

def create_model():    model = Sequential()    model.add(CuDNNLSTM(512, input_shape=(3,2), return_sequences=True, name='inputlstm1'))    model.add(Dropout(0.2))    model.add(CuDNNLSTM(512, return_sequences=True,name='lstm2'))    model.add(Dropout(0.2))    model.add(CuDNNLSTM(512, return_sequences=True,name='lstm3'))    model.add(Dropout(0.2))    model.add(Dense(32, activation='relu', name='dense1'))    model.add(Dropout(0.2))    model.add(Dense(1, activation='softmax', name='denseoutput2'))    # Compile model    model.compile(        loss='mse',        optimizer='adam',        metrics=['accuracy'],    )    return model 

以及它的拟合过程：

model=create_model()history=model.fit(xtrain, ytrain,batch_size=1, epochs=5, validation_data=(xtest, ytest), verbose=1)

回答：

构建Keras层时需要指定传入数组的形状，这里要训练的数组形状为(3,2)，共有1607个样本，

input_shape = (3,2)X = LSTM(124, activation = 'sigmoid', name='layer1', dropout = 0.4) (temp)

如果你想使用堆叠LSTM，可以使用以下方法：

input_shape = (3,2)    X = LSTM(124, activation = 'sigmoid', name='layer1', dropout = 0.4,return_sequences=True) (temp)    X = LSTM(64, activation = 'sigmoid', name='layer2', dropout = 0.4) (X)

编辑

def create_model():    model = keras.models.Sequential()    model.add(keras.layers.CuDNNLSTM(512, input_shape=(3,2), return_sequences=True, name='inputlstm1'))    model.add(keras.layers.Dropout(0.2))    model.add(keras.layers.CuDNNLSTM(512, return_sequences=True,name='lstm2'))    model.add(keras.layers.Dropout(0.2))    # 堆叠LSTM的最后一层不需要返回输入序列    model.add(keras.layers.CuDNNLSTM(512,name='lstm3'))    model.add(keras.layers.Dropout(0.2))    model.add(keras.layers.Dense(32, activation='relu', name='dense1'))    model.add(keras.layers.Dropout(0.2))    model.add(keras.layers.Dense(1, activation='softmax', name='denseoutput2'))    # Compile model    model.compile(        loss='mse',        optimizer='adam',        metrics=['accuracy'],    )    return model