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我一直在使用 Elmo 在 IMDB 数据集上嵌入 250 个句子的矩阵。应用 Elmo 后，我得到了一个维度数组（250、1024）。生成 Elmo 矩阵的代码如下：

def getElmo(elmo,x):
    embed=elmo(x,signature="default",as_dict=True)["elmo"]
    with tf.compat.v1.Session() as ses:           
        ses.run(tf.compat.v1.global_variables_initializer())
        ses.run(tf.compat.v1.tables_initializer())
        return ses.run(tf.reduce_mean(embed,1))

listTrain=[X_train[i:i+10] for i in range(0,X_train.shape[0],10)]
listTrainT=[X_trainT[i:i+10] for i in range(0,X_trainT.shape[0],10)]
elmoTrain=[getElmo(elmo,i)for i in listTrain]

我将矩阵保存到一个泡菜文件中，当我打开它时，它具有以下数据和尺寸：

[[ 0.01701645 -0.08691402  0.01426436 ...  0.01268931  0.09302917
   0.00627212]
 [-0.01594155 -0.11892342  0.03353928 ...  0.00920583  0.07130951
  -0.00491649]
 [ 0.02554347 -0.04609346  0.03476872 ... -0.019207    0.13730985
   0.01841145]
 ...
 [ 0.23489136 -0.24797124  0.03176903 ... -0.16736303  0.46603483
   0.07271077]
 [-0.03589417 -0.10203484 -0.07184037 ... -0.05782426  0.21744442
   0.0481869 ]
 [ 0.06803051 -0.11667343  0.00658324 ... -0.07491366  0.12236159
   0.00994192]]

(250, 1024)

我用了250个句子。我的 LSTM 模型如下：

model=Sequential()
maxlen=1024           
#vocab size equals 250
embedding_layer=Embedding(vocab_size,1024,weights=[embedM],input_length=maxlen,trainable=False)  model.add(embedding_layer)
model.add(LSTM(128))
model.add(Dense(1,activation="sigmoid"))
model.compile(optimizer="adam",loss="binary_crossentropy",metrics=["acc"])
print(model.summary())

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_32 (Embedding)     (None, 1024, 1024)        256000    
_________________________________________________________________
lstm_17 (LSTM)               (None, 128)               590336    
_________________________________________________________________
dense_17 (Dense)             (None, 1)                 129       
=================================================================
Total params: 846,465
Trainable params: 590,465
Non-trainable params: 256,000
_________________________________________________________________

但我得到的错误是：

InvalidArgumentError: indices[117,181] = -1 is not in [0, 250)
     [[{{node embedding_32/embedding_lookup}}]]

为什么我会收到此错误？

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