ValueError: Input 0 is incompatible with layer repeat_vector_58: expected ndim=2, found ndim=3

Question

我正在尝试构建入侵检测 LSTM 和自动编码器。但是我无法理解为什么 repeat_vector_58 需要 ndim=3。我无法弄清楚这一点。下面是我的代码：

x_train.shape: (8000, 1, 82)

x_test.shape: (2000, 1, 82)

x_train = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
x_test = np.reshape(testT, (testT.shape[0], 1, testT.shape[1]))

start = time.time()
model = Sequential()
model.add(LSTM(128, activation='relu',recurrent_dropout=0.5,return_sequences=True,input_dim=82))
model.add(RepeatVector(82))
model.add(Dropout(0.3))
model.add(LSTM(64, activation='relu',recurrent_dropout=0.5,return_sequences=False))
model.add(Dropout(0.3))
model.add(TimeDistributed(Dense(1,activation='softmax')))

ValueError: Input 0 is incompatible with layer repeat_vector_58: expected ndim=2, found ndim=3

Answer 1

LSTM 层需要 3 维输入，因为它是循环层。预期的输入是 (batch_size, timesteps, input_dim)。规范 input_dim=82 需要 2 维输入，但预期输入为 3 维。
因此，您的错误的解决方案是，将 input_dim=82 更改为 input_shape=(82,1).

model = Sequential()
model.add(LSTM(128,activation='relu',recurrent_dropout=0.5,return_sequences=True,input_shape=(82,1)))

ValueError: Input 0 is incompatible with layer repeat_vector_58: expected ndim=2, found ndim=3

ValueError: Input 0 is incompatible with layer repeat_vector_58: expected ndim=2, found ndim=3

python

autoencoder

deep-learning

lstm

keras