无效参数：indices[124,0] = 2629 不在 [0, 64) 中（多输入模型）

Question

我正在关注Deep Learning with Python 第 7.1.2 节多输入模型。在代码清单 7.1 中，我遇到以下错误：

InvalidArgumentError: 2 root error(s) found.
  (0) Invalid argument:  indices[124,0] = 2629 is not in [0, 64)
     [[node functional_11/embedding_8/embedding_lookup (defined at E:/Studies/PythonCode_DLBook/Codes/Chap7_Code2.py:30) ]]
     [[functional_11/embedding_9/embedding_lookup/_16]]
  (1) Invalid argument:  indices[124,0] = 2629 is not in [0, 64)
     [[node functional_11/embedding_8/embedding_lookup (defined at E:/Studies/PythonCode_DLBook/Codes/Chap7_Code2.py:30) ]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_29208]

Errors may have originated from an input operation.
Input Source operations connected to node functional_11/embedding_8/embedding_lookup:
 functional_11/embedding_8/embedding_lookup/26947 (defined at C:\Users\abdul\anaconda3\envs\PIAIC\lib\contextlib.py:113)

Input Source operations connected to node functional_11/embedding_8/embedding_lookup:
 functional_11/embedding_8/embedding_lookup/26947 (defined at C:\Users\abdul\anaconda3\envs\PIAIC\lib\contextlib.py:113)

Function call stack:
train_function -> train_function

使用的代码是：

from tensorflow.keras.models import Model
from tensorflow.keras import layers
from tensorflow.keras import Input
import numpy as np

text_vocabulary_size = 10000
question_vocabulary_size = 10000
answer_vocabulary_size = 500

text_input = Input(shape=(100,), 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=(100,),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'])

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)

我确实意识到 Embedded_text 层出现错误，因为它的输入与传入的数据形状不匹配。

但是我不知道如何解决这个问题，事实上我目前不知道如何设置/检查不同层之间的输入数据形状和数据形状。 因此，如果有人展示如何在设计模型时检查图层形状以及如何解决此类问题，那将非常有帮助。

Answer 1

对于 TF，一种常用的方法是使用model.summary() 来检查网络每一层的输出形状。运行代码返回

Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================
text (InputLayer)               [(None, 100)]        0
__________________________________________________________________________________________________
question (InputLayer)           [(None, 100)]        0
__________________________________________________________________________________________________
embedding (Embedding)           (None, 100, 10000)   1000000     text[0][0]
__________________________________________________________________________________________________
embedding_1 (Embedding)         (None, 100, 10000)   1000000     question[0][0]
__________________________________________________________________________________________________
lstm (LSTM)                     (None, 100)          4040400     embedding[0][0]
__________________________________________________________________________________________________
lstm_1 (LSTM)                   (None, 100)          4040400     embedding_1[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate)       (None, 200)          0           lstm[0][0]
                                                                 lstm_1[0][0]
__________________________________________________________________________________________________
dense (Dense)                   (None, 500)          100500      concatenate[0][0]
==================================================================================================
Total params: 10,181,300
Trainable params: 10,181,300
Non-trainable params: 0

所以这将是故障排除的第一步，如果您想查看每一层的预期输入形状，model.get_config() 是一种方法。我会向您推荐问题here。

此外，我建议您阅读layers.LSTM 和layers.Embedding 的文档，以牢牢掌握您传入的参数和您正在创建的层。希望这有助于故障排除过程:)