当我尝试训练网络时（在python 3.7中）出现此错误，我从python深度学习书（chollet的书第261页，第7章）中获得了此练习。我试图在text_vocabulary_size上加+1，但它不起作用U.U，这是我的代码和错误
代码：
from keras.models import Model
from keras import layers
from keras import Input
text_vocabulary_size = 10000
question_vocabulary_size = 10000
answer_vocabulary_size = 500
text_input = Input(shape=(None,), dtype='int32', name='text')
embedded_text = layers.Embedding(64, text_vocabulary_size)(text_input)
encoded_text = layers.LSTM(32)(embedded_text)
question_input = Input(shape=(None,), dtype='int32', name='question')
embedded_question = layers.Embedding(32, question_vocabulary_size)(question_input)
encoded_question = layers.LSTM(16)(embedded_question)
concatenated = layers.concatenate([encoded_text, encoded_question], axis=-1)
answer = layers.Dense(answer_vocabulary_size, activation='softmax')(concatenated)
model = Model([text_input, question_input], answer)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['acc'])
import numpy as np
num_samples = 1000
max_length = 100
text = np.random.randint(1, text_vocabulary_size, size=(num_samples, max_length))
question = np.random.randint(1, question_vocabulary_size, size=(num_samples, max_length))
answers = np.random.randint(0, 1, size=(num_samples, answer_vocabulary_size))
model.fit([text, question], answers, epochs=10, batch_size=128)
model.fit({'text': text, 'question': question}, answers, epochs=10, batch_size=128)
错误：
InvalidArgumentError: indices[124,0] = 190 is not in [0, 64)
[[{{node embedding_33/embedding_lookup}}]]
这是网络的摘要，希望对您有所帮助
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
text (InputLayer) (None, None) 0
__________________________________________________________________________________________________
question (InputLayer) (None, None) 0
__________________________________________________________________________________________________
embedding_33 (Embedding) (None, None, 10000) 640000 text[0][0]
__________________________________________________________________________________________________
embedding_34 (Embedding) (None, None, 10000) 320000 question[0][0]
__________________________________________________________________________________________________
lstm_33 (LSTM) (None, 32) 1284224 embedding_33[0][0]
__________________________________________________________________________________________________
lstm_34 (LSTM) (None, 16) 641088 embedding_34[0][0]
__________________________________________________________________________________________________
concatenate_17 (Concatenate) (None, 48) 0 lstm_33[0][0]
lstm_34[0][0]
__________________________________________________________________________________________________
dense_17 (Dense) (None, 500) 24500 concatenate_17[0][0]
==================================================================================================
Total params: 2,909,812
Trainable params: 2,909,812
Non-trainable params: 0
和追溯
InvalidArgumentError Traceback (most recent call last)
<ipython-input-52-556d838d7025> in <module>
6 question = np.random.randint(1, question_vocabulary_size, size=(num_samples, max_length))
7 answers = np.random.randint(0, 1, size=(num_samples, answer_vocabulary_size))
----> 8 model.fit([text, question], answers, epochs=10, batch_size=128)
9 model.fit({'text': text, 'question': question}, answers, epochs=10, batch_size=128)
~\Anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1037 initial_epoch=initial_epoch,
1038 steps_per_epoch=steps_per_epoch,
-> 1039 validation_steps=validation_steps)
1040
1041 def evaluate(self, x=None, y=None,
~\Anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training_arrays.py in fit_loop(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
197 ins_batch[i] = ins_batch[i].toarray()
198
--> 199 outs = f(ins_batch)
200 outs = to_list(outs)
201 for l, o in zip(out_labels, outs):
~\Anaconda3\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py in __call__(self, inputs)
2713 return self._legacy_call(inputs)
2714
-> 2715 return self._call(inputs)
2716 else:
2717 if py_any(is_tensor(x) for x in inputs):
~\Anaconda3\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py in _call(self, inputs)
2673 fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata)
2674 else:
-> 2675 fetched = self._callable_fn(*array_vals)
2676 return fetched[:len(self.outputs)]
2677
~\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in __call__(self, *args, **kwargs)
1437 ret = tf_session.TF_SessionRunCallable(
1438 self._session._session, self._handle, args, status,
-> 1439 run_metadata_ptr)
1440 if run_metadata:
1441 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
~\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
526 None, None,
527 compat.as_text(c_api.TF_Message(self.status.status)),
--> 528 c_api.TF_GetCode(self.status.status))
529 # Delete the underlying status object from memory otherwise it stays alive
530 # as there is a reference to status from this from the traceback due to
InvalidArgumentError: indices[124,0] = 190 is not in [0, 64)
[[{{node embedding_33/embedding_lookup}}]]
先谢谢大家
答案 0 :(得分：2)
在您的两个嵌入中：
embedded_text = layers.Embedding(64, text_vocabulary_size)(text_input)
embedded_question = layers.Embedding(32, question_vocabulary_size)(question_input)
我认为您将参数以相反的顺序放置，第一个参数是词汇量，第二个参数是输出嵌入量，所以应该是：
embedded_text = layers.Embedding(text_vocabulary_size, 64)(text_input)
embedded_question = layers.Embedding(question_vocabulary_size, 32)(question_input)
您随时可以在keras documentation中进行检查。