类型错误：传递给“ConcatV2”操作的“值”的列表中的张量具有不全部匹配的类型 [bool, float32]

Question

我正在尝试使用我在此链接上找到的 LSTM 重现用于实体识别的笔记本：https : //medium.com/@rohit.sharma_7010/a-complete-tutorial-for-named-entity-recognition-and-extraction -in-natural-language-processing-71322b6fb090

当我尝试训练模型时，我收到一个我无法理解的错误（我对 tensorflow 很陌生）。特别是有错误的代码部分是这样的：

from keras.models import Model, Input
from keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Bidirectional
from keras_contrib.layers import CRF

# Model definition
input = Input(shape=(MAX_LEN,))
model = Embedding(input_dim=n_words+2, output_dim=EMBEDDING, # n_words + 2 (PAD & UNK)
                  input_length=MAX_LEN, mask_zero=True)(input)  # default: 20-dim embedding
model = Bidirectional(LSTM(units=50, return_sequences=True,
                           recurrent_dropout=0.1))(model)  # variational biLSTM
model = TimeDistributed(Dense(50, activation="relu"))(model)  # a dense layer as suggested by neuralNer
crf = CRF(n_tags+1)  # CRF layer, n_tags+1(PAD)
print(model)
out = crf(model)  # output

model = Model(input, out)
model.compile(optimizer="rmsprop", loss=crf.loss_function, metrics=[crf.accuracy])

model.summary()

错误就行了

out = crf(model)

我得到的错误是这样的：

TypeError: Tensors in list passed to 'values' of 'ConcatV2' Op have types [bool, float32] that don't all match.

有人可以给我一个解释吗？

Answer 1

我今天也遇到了这个问题。对我有用的是mask_zero=True从嵌入层中删除。不幸的是，我不知道为什么这会有所帮助。