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python - 使用 TensorFlow 的多类文本分类。输出问题

问题描述

我正在检查来自面向DataScience多类问题的 BBC 新闻代码，有 5 个标签和 6 个预测输出，我尝试给它提供娱乐新闻

txt = ["last star wars .....  the film is much more dark  more emotional. it s much more of a tragedy."]
seq = tokenizer.texts_to_sequences(txt)
padded = pad_sequences(seq, maxlen=max_length)
pred = model.predict(padded)
labels = ['sport', 'business', 'politics', 'tech', 'entertainment']
print(pred, labels[np.argmax(pred)])

它返回

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-20-05bd09f68629> in <module>
      4 pred = model.predict(padded)
      5 labels = ['sport', 'business', 'politics', 'tech', 'entertainment']
----> 6 print(pred, labels[np.argmax(pred)])

IndexError: list index out of range

我只是在娱乐标签和输出之后添加了testlabel！

   [[7.9564927e-07 7.9830606e-06 8.0213253e-04 3.8535093e-04 7.4164674e-02
  9.2463899e-01]] test label

模型在这里

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_dim,
                             input_length=max_length),
    # specify the number of convolutions that you want to learn, their size, and their activation function.
    # words will be grouped into the size of the filter in this case 5
    tf.keras.layers.Conv1D(128, 5, activation='relu'),
    tf.keras.layers.GlobalAveragePooling1D(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(6, activation='softmax')
])
model.summary()

如果我将最后一个稠密更改为 5，它仍然会在训练中出现以下错误。

InvalidArgumentError:  Received a label value of 5 which is outside the valid range of [0, 5).  Label values: 3 2 1 5 4 4 2 5 5 3 1 1 3 1 4 1 5 3 2 5 3 3 4 3 5 2 3 2 4 5 3 5
     [[node loss_1/dense_3_loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at <ipython-input-25-5196760ed1a6>:2) ]] [Op:__inference_keras_scratch_graph_6926]

Function call stack:
keras_scratch_graph

有人可以向我解释一下吗？这是为什么？，以及如何获得与标签数量相同的输出？

标签： pythontensorflowkerastext-classificationtf.keras

解决方案

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